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I am working on some macroeconomic forecasting on a 15-year data sample. I don't know which multivariate time series technique would be appropriate for the analysis since one variable is stationary at the second difference. Could someone please suggest which method would be better for my analysis, along with the supporting material?
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I recommend you NOT to convert quarterly to monthly data. You will have more observations but the increase of precision will be illusory. You say to have an I(2) variable but you don't say anything about the other variables. For example, if all of them are I(0), I don't think that cointegration will help. You should say more about your problem if you want appropriate suggestions.
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Economics has treated econometrics as a universal solvent, a technique that can be applied to any economic question, which is sufficient and, therefore, makes other applied techniques redundant.
Peter Swann in his book indicates the place of econometrics and argues against this notion and even takes this as a severe error. He advises fellow economists that they learn to respect and assimilate what he calls vernacular knowledge of the economy. His top message to economists is what the great French composer, Paul Dukas, advised his pupil: “Listen to the birds, they are great masters.” If any fellow economist asks: “don’t most economists do this already?” Then the answer by Swann is clear: “… some economists do use vernacular knowledge some of the time to underpin what they do … incidentally to make a piece of high technique more approachable … outside this limited context, economists do not tend to take the vernacular seriously."
Any argument for or against it?
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Dear Simon, you confirm my contribution. Statstistics was applied by many sciences long before the term econometrics was invented. Like mathematics (one could, of course, say that statistics is part of mathematics), it is universal in the sense, that it can be applied for rahter different scientific studies. Nevertheless, it can only be applied, if there is a theory of the respective science.
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Hi all, I'm doing fyp with the title of the determinant of healthcare expenditure in 2011-2020. Here are my variables: government financing, gdp, total population.
first model is: healthcare expendituret=B0+B1gov financingt + B2gdpt + B3populationt + et
second causal relationship model is healthcare expenditure per capitat= B0 + B1gdp per capitat +et
It is possible to use unit root test then ADRL for the first model and what test can use for the second model?
Thank you in advance for those reply me :)
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You should read the Global Burden of Disease articles on health care financing. Their appendices fully disclose their variables (& they've spent a lot of time & effort dettermining the best ones) and they provide their (sophisticated) statistical code. Lead author on most of that work is Joseph Dieleman or something like Global Burden of Disease Health Financing Collaborator Network. A full biblio of this work is at https://www.healthdata.org/about/joseph-dieleman if you click on the publications tab. The pubs are all open-access & most are on ResearchGate. :-)
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In the context of ADAS features, what kind of econometric modeling would be the best to represent it? Here it is to be noted that ADAS parameters will be such that will be recognized the integrity and performance of the system. For example, in-vehicle cooling, seat and glass settings, etc.
Thank you, in advance.
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It is not clear what you want to study. There is no econometric model to help you, if you do not know what you want to do.
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In the file that I attached below there is a line upon the theta(1) coefficient and another one exactly below C(9). In addition, what is this number below C(9)? There is no description
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I asked that question to the person who code that package, and he said C(9) coefficient does not have any meaning here, just ignore. It comes up because the package is written for the old version of Eviews and has not been updated that is why.
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Good day scholars!
Suppose I have five logged time series variables with mixed order of integration such as two I(1)s, two I(2)s and one I(0). Apart from simultaneous equation system, which other econometrics models can be used to model these variables?
Thanks for being there always
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I would ask what economic model are you trying to estimate. before you use any econometrics you should have an outline model. Are you satisfied that the different orders of integration are consistent with your theory?
You say that you have two I(1)s, two I(2)s, and one I(0). There is a basic principle that your equations must be balanced. If your dependent variable is I(0) than the explanatory variables or combinations of explanatory variables must also be I(0). The first difference of your I(1) variables is I(0) are possible explanatory variables. It is possible that your I(1) variables are cointegrated and the cointegrated combination of these two variables may also be an explanatory variable. It is possible that there is a combination of your I(2) variables which is I(1) and this I(1) variable is cointegrated with one of your I(1) variables. giving you a possible explanatory I(0) variable. You may even be able to integrate your I(0) variable to make it I(1). These are possible candidates and there are many more.
Econometrics does not tell you which of these is appropriate. Most of this work and the interpretation of your results is decided by your prior economic analysis.
Even in a simultaneous equation system, you must ensure that your equations are balanced. Apart from simultaneous equation systems, you have VAR, SVAR, VECM, and even single equation systems. Again the economic methodology arises from your economics.
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The models that are used to check the robustness of the main econometric model may not always provide 100% parallel outcomes. Does it mean that there are flaws in the main estimation outcomes?
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I think the similarity of your robustness check to the main method depends on the properties of both estimators. For instance, using augmented ardl as a robust test for bootstrap Ardl could yield similar result. Same might go for LSDV and GMM because they are both dynamic model method and can also correct for endogeneity problem. So the similarity depends on the similarity of the estimators. The coefficient doesn't really matter though but the similarity of the signs of the coefficient is very important..
Regards
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Dear research community, 
I am currently working with Hofstede's dimensions, however, I do not exactly use his questionnaire. In order to calculate my index in accordance to his process, I am looking for the meaning of the constants in front of the mean scores. 
For example: PDI = 35(m07 – m02) + 25(m20 – m23) ... What do 35 and 25 mean? How could I calculate them with regard to my research? 
Thank you very much for your help!
Best wishes, 
Katharina Franke
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Given that the structural break points play a crucial role in time-series analysis, many past studies have ignored them. There are studies that have identified break points but have not incorporated them with the main econometric model. Given that the incorporation of break dummies can significantly influence the final model outcomes, what is the rationality of finding the breaks and not incorporating them to the main model?
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BREAKS IN TIME SERIES is a means for detection whether throughout the series there was a significant shock that would throw the long-term equilibrium out of its path. If the effect of shock is temporary and the series return to it's means, we say that the effect of the shock has been integrated into the series. In this case we still deal with the same regine.
REGIME CHANGE occurs when the break is significant and the new means does not go back to it's long-term equilibrium. This regime change tells us that the previous model no longer could explain the series subsequent to the shock. A new model should be proposed or the previous model should be revised to accommodate the effect of shock.
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I collected 109 responses for 60 indicators to measure the status of urban sustainability as a pilot study. So far I know, I cannot run EFA as 1 indicator required at least 5 responses, but I do not know whether I can run PCA with limited responses? Would you please suggest to me the applicability of PCA or any other possible analysis?
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I would recommend you read about the difference between EFA and PCA first. Whether or not you should run an EFA has nothing to do with the number of response options on the indicators, five or otherwise. In general, EFA is preferable to PCA as it is considered to be the 'real' factor analysis. The are many threads on RG on this issue.
Best
Marcel
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I'm working on Chinese OFDI in Africa and I really want to analyze my hypothesis via an Econometric Model based on the Micro level index. So if you already have a room or article link please let me know to learn how is possible.
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YES OF COURSE WE CAN THERE IS A FIELD CALLED MICRO-ECONOMETRIC
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hi economist researchers, pls help the what type of econometric model is used in order to compare the income of improved crop variety adopter and non adopters?
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in this case, you must use a dummy variable that is 1 and 0. Those are adopter is treated as 1 and non-adopter treated as Zero. Than Run multiple linear regression model
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What is the most acceptable method to measure the impact of regulation/policy so far?
I only know the Difference-in-Difference (DID), Propensity Score Matching (PSM), Two-Step System GMM (for dynamic) are common methods. Expecting your opinion for 20 years long panel for firm-level data.
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recent development
(1) Wooldridge two-way Mundlak regression and fixed effects and dif-in-dif
(2) synthetic control
(3) Cerulli, G. 2015. Econometric Evaluation of Socio-Economic Programs: Theory and Applications.
(4) Pesaran (2015) Time Series and Panel Data Econometrics
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Dear everyone,
I am in great distress and desperately need your advice. I have the cumulated (disaggregated) data of a survey of an industry (total export, total labour costs etc.) of 380 firms. The original paper is using a Two-stage least square (TSLS) model in oder to analyze several industries with one Independent variable having a relationship with the dependent variable, which was the limitation not to use an OLS method, according to the author. However, i want to conduct a single industry analysis and exclude the variable with the relationship, BUT instead analyze the model over 3 years. What is the best econometric model to use? Can is use an OLS regression over period of 3 years? if yes, what tests are applicable then?
Thank you so much for your help, you are helping me out so much !!!!!!!
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Dear, conducting any standard model depends on an important factor, namely the number of observations included in the model, for example, if the observations are small, the Phelps-Peron test can be conducted to test the stability, and if the observations are large, the whooping-full test can be conducted, and in light of the stability results, we can determine the model that can be conducted Julius Hogan
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Hello everyone,
i would like to analyze the effect of innovation in 1 industry over a time period of 10 years. the dependent variable is export and the Independent variables are R&D and Labour costs.
What is the best model to use? i am planning to do a Log-linear model.
Thank you very much for your greatly needed help!
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Before deciding on the econometric model, you should go through the stationarity test (ADF test). If the data are stationary, OLS Regression with a log-linear model would be fine. But, if not, you may go for VAR or ARDL. You should also check the robustness of the model by going through residual tests such as Autocorrelation LM Test.
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Dear colleagues,
I am planning to investigate the panel data set containing three countries and 10 variables. The time frame is a bit short that concerns me (between 2011-2020 for each country). What should be the sample size in this case? Can I apply fixed effects, random effects, or pooled OLS?
Thank you for your responses beforehand.
Best
Ibrahim
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It seems a very small sample to apply microeconometric techniques. Having 27 seven observations and 10 covariates, at most, you will have 27 - 1 - 10 = 16 degrees of freedom. This is pretty low. If I had to decide to pursue a project based on that, I would try to avoid it.
It is really closer to multiple times series than panel data. Have a look at this link:
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I have panel data covering 12 countries of the MENA region for the period 1996-2019. The dependent variable is tourism receipts and the independent variables are the World Bank governance indexes ( political stability, government effectiveness, control of corruption, and voice and accountability), I also have UNESCO sites - dummy- and a few economic indexes such as GDP and GDP Per Capita. What are the models I should implement? fixed or random? and what are step-by-step processes? how should I start?
Below I tried to do OLS pooling, but not sure if this is correct or not.
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Dear Rand,
I could not open your data set, because I have no unpacking program. But that is not so important, as I have looked on some of the data in the World Bank Statistics.
My first proposal is to choose a higher dimension for the tourism revenues (e.g. billions of units - $?). This would lead to a better readability of the results.
For the first step of the specification of the model/equation to estimate, you need no data. Simply ask yourself and others, how the political stability and other political circumstances in a country would influence the decision to go there as a tourist and how much to spend.
Then I would look on the time series (from which one can get insights on changes over time) for some of the countries to find patterns between the exogenous variables and the dependent variable. The main problems may be: 1. The political variables might have a rather limited influence. 2. They may show too little variances. 3. A change of e.g. political stability will not lead to an instantaneous change of tourism. Improvement will likely have its positive influence only after some years, whereas a change to instability will have a shorter-run effect (asymmetric behaviour). 4. You will likely find high correlations between the political variables, which means that you should omit some of them. 5. The indices are far from an ideal cardinal scale, it is even not clear, whether a higher value really measures an improvement.
If you (hopefully) find-out some relations, you can try simple OLS estimations with one of the political variables lagged for several years and you may try to do that for transformed variables (percentage change of revenues with absolute differences of the indices).
The comparison between countries could, in principle, give you an impression of the political influence on (the level of) tourism, but I fear that other factors (landscape, seaside, history, culture) are by far dominating. If your aim is only to exercise econometric methods, you can try an estimate for the whole data set. For this purpose, I recommend, to calculate indices instead of the absolute revenue values (to neutralize the “non-political” differences between countries. But you should take into account that you have more explaining variables than you used in your first approach – namely all these political indicators with different lags. I think, that for such an approach you lose too many degrees of freedom and, therefore, you would not get reasonable results.
If I had this difficult (not to say: impossible) task, I would never do a pooled estimation, but I would try to find good time series estimates for all 10 countries, and then compare the estimated coefficients to find-out similarities.
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I have tried to estimate Bayesian spatial econometric models using R. However, I felt like the functions and packages are limited in R. Has anyone used R for Bayesian spatial econometric? Would other software do better job in estimating it?
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R is one of the best tools when it comes to spatial modeling in General, and as I have worked with R multiple times you just need to find the right cran file that serves well your manner, Also check GIS software in literatures, many studies used it for Bayesian spatial modeling. Good luck
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I am working on a project regarding the impact of ICT investment on economic growth of the MENA region from 2007 to 2017. we will use econometric model and we wil set the ICT development index as a proxy to measure the ICT. I found data about ranks of IDI in 2007,2008,2010,2011,2012,2013,2015,2016,2017, but I can't find in 2009 and 2014, so can someone help me where can I find these two years?
thanks in advance.
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How to make ICT index, I mean what weights to assign internet, mobile and fixed phone line?
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Dear Colleagues,
I ran an Error Correction Model, obtaining the results depicted below. The model comes from the literature, where Dutch disease effects were tested in the case of Russia. My dependent variable was the real effective exchange rate, while oil prices (OIL_Prices), terms of trade (TOT), public deficit (GOV), industrial productivity (PR) were independent variables. My main concern is that only the Error Correction Term, the dummy variable, and the intercept are statistically significant. Moreover, residuals are not normally distributed, while also the residuals are heteroscedasdic. There is no serial correlation issue according to the LM test. How can I improve my findings? Thank you beforehand.
Best
Ibrahim
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I notice the following about your specification. (1) Your inclusion of a constant (and its subsequent significance) means you allow for (and find) a trend in the real exchange rate independent of any trends in the other variables. Is that economically reasonable? (2) I assume the CRISIS variable is a zero-one dummy for time periods with a "crisis" of some sort. Apparently it is not in the cointegration vector. Why not? If it were, then I'd expect to find CRISIS differences in the error correction equation. Instead, you have it in levels. Thus you specify that a temporary crisis has a permanent effect of the level of the real exchange rate independent of the other variables. Is that what you intend? (3) You do not include the lagged difference of the real exchange rate in the error correction equation. Why not? Normally it would be there.
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I have non-stationary time-series data for variables such as Energy Consumption, Trade, Oil Prices, etc and I want to study the impact of these variables on the growth in electricity generation from renewable sources (I have taken the natural logarithms for all the variables).
I performed a linear regression which gave me spurious results (r-squared >0.9)
After testing these time series for unit roots using Augmented Dickey- Fuller test all of them were found to be non-stationary and hence the spurious regression. However their first differences for some of them, and second differences for the others, were found to be stationary.
Now when I test the new linear regressions with the proper order of integration for each variables (in order to have a stationary model) the statistical results are not good (high p-value for some variables and low r-squared (0.25))
My question is how should I proceed now? Should i change my variables?
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Please note that transforming variable(s) does NOT make the series stationary, but rather makes the distribution(s) symmetrical. Application of logarithmic transformation needs to be exercised with extreme caution regarding properties of the series, underlying theory and the implied logical/correct interpretation of the relationships between the dep variable and associated selected regressors.
Reverting to your question, the proposed solution would be to use the Autoregressive Distributed Lag (ARDL) model approach, which is suitable for datasets containing a mixture of variables with different orders of integration. Kindly read the manuscripts attached for your info.
All the best!
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Does anyone have the codes (written on RATS/MATLAB/any other platform) for Rolling Hinich Bicorrelation test and Rolling Hurst Exponent test? Would greatly appreciate if you could share them.
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Hi! I have a model for a panel data and my teacher told me to do an estimation of the model with different coefficients for one of the explicative variables. She gave me an example:
lpop @expand(@crossid)    linv(-1) lvab lcost (for different coefficients for intercept)
or
lnpop c  lninv   @expand(@crossid)*lnvab  lncost (for different coefficients for this variable).
Can someone explain me how to do that? I tried but it didn't work..
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It is shown as lnpop, so i think you need to transform the variables into log. Simply follow this steps if it is the case,
quick<generate series<lnpop= log(pop). and log the rest of the variables in the same way.
after that put lnpop in the estimate equation.
Best wishes for you.
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Dear colleagues,
I applied the Granger Causality test in my paper and the reviewer wrote me the following: the statistical analysis was a bit short – usually the Granger-causality is followed by some vector autoregressive modeling...
What can I respond in this case?
P.S. I had a small sample size and serious data limitation.
Best
Ibrahim
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Ibrahim Niftiyev , probably the reviewer wants to see not only whether a variable affects or not the other (i.e. the results of the Granger causality tests), but also to which extent (the magnitude and temporality of the dynamic relationship, something you can obtain from the IRFs of a VAR model). If you want to apply a VAR but you have a small sample size/data limitations, you want to consider a Bayesian VAR. Bayesian VARs are very popular and Bayesian methods are valid in small samples.
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In my work, I use the multivariate GARH model (DCC-GARCH). I am testing the existence of autocorrelation in the variance model. Ljung-Box tests (Q) for standardized residuals and square standardized residuals give different results.
Should I choose the Ljung-Box or Ljung-Box square test?
N=1500
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The Ljung-Box test is aimed at testing the independance of errors using residuals of an ARMA model estimated on the same data. But it makes use of autocorrelations so it is not powerful when the errors are uncorrelated but not independent. When applied to squared residuals, it can reveal ARCH and GARCH effects. Note that the errors of a ARCH-GARCH model are uncorrelated but not independent. Have a look at the excellent book by Francq and Zakoian entitled "GARCH Models: Structure, Statistical Inference and Financial Applications" published by Wiley in 2010.
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Hello everyone. I am using the VECM model and I want to use variance decomposition, but as you know variance decomposition is very sensitive to the ordering of the variable. I read in some papers that it will be better to use generalized variance decomposition because it is invariant to the ordering of the variables. I am using Stata, R or Eviews and the problem is how to perform Generalised VD and please if anyone knows help me
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I am running an ARDL model on eviews and I need to know the following if anyone could help!
1. Is the optimal number of lags for annual data (30 observations) 1 or 2 OR should VAR be applied to know the optimal number of lags?
2. When we apply the VAR, the maximum number of lags applicable was 5, beyond 5 we got singular matrix error, but the problem is as we increase the number of lags, the optimal number of lags increase (when we choose 2 lags, we got 2 as the optimal, when we choose 5 lags, we got 5 as the optimal) so what should be done?
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  1. My first comment is that all cointegrating studies must be based on the economic theory (and common sense) of the system that you are examining. Your theory should suggest which variables are stationary, which are non-stationary, and which are cointegrated. Your ARDL, VECM, etc, analyses are then tests of the fit of the data to your theories. It is not appropriate to use these methodologies to search for theories that fit the data. Such results will give spurious results. Now suppose that you have outlined your theory in advance of touching your computer keyboard to do your econometrics.
  2. You have only 30 annual observations. This is on the small size for an elaborate analysis such as this. It appears that you have one dependent variable and possibly 3 explanatory variables. If you have 5 lags you are estimating about 25 coefficients which is not feasible with 30 observations.
  3. If you wish to use the ARDL methodology you must be satisfied that (1) there is only one cointegrating relationship and (2) that the explanatory variables are (weakly) exogenous. Otherwise, a VECM approach is required and you may also not have enough data for a VECM analysis.
  4. Is it possible that you would use a simpler approach? Could you use graphs or a simpler approach to illustrate your economic theory? These are questions that you alone can answer. Advanced econometrics is not a cure for inadequate data and a deficit of economic theory.
  5. If this is an academic project, consult your supervisor. While what I have set out above is correct, it may not be what your supervisor expects at this stage in your studies.
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It is obvious that economic publications are becoming more and more quantitative that they seem detached from reality and lost economic sense. The advanced econometric models we use are sometimes difficult for other economists to comprehend. Is it not time to prioritize qualitative researches so we have a larger audience and our publications become more relevant?
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Depends on the research question and the target audience. While policy and social related issues are better handle using qualitative methodology it loses the generality and reliability of the findings to extend to other situations and context.
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Hi!
I would like to ask about the possibility of improving the MAPE values for the VAR-model. When the lag intervals change, the MAPE values improve. However, the question arises as to the rationale for such an approach to changing the lag intervals. Earlier in the analysis of the VAR-model, I explained the feasibility of the lag intervals following the test results of Lag Length Criteria and Lag Exclusion Tests. And now I'm not sure if from a scientific point of view I can explain the change in the lag intervals as a result of improving the MAPE values. Tell me, please, maybe someone knows the rationale for such an approach?
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Generally speaking, MAPE may not be the best choice. Please let me recommend this guide to measuring forecasting accuracy:
Btw, if you have time series with non-negative actuals, you can try data transformations to improve forecasts. If you apply data transformations, I would recommend MAE or the AvgRelMAE.
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Hello. I am working on a classroom study on the effect of environmental factors on German energy demand. I did not find an econometric model or related decomposition model. Can anyone help?
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I have panel data for bond and I am trying to estimate the following model:
depvar x1 x2 x3 x4, fe
Where x1 and x2 are firm specific variables while x3 and x4 are time-variant variables. So in the t observation x3 and x4 are the same for all the unit observed.
Differnet software give me the estimation but I'm not sure to make the right process.
It is correcto to use Fixed Eff. or it is better to switch to RE?
Moreover, I have the same problem in a similar situation where depvar is binomial? Can I use a logistic panel regregression?
Thanks for your help and your time.
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If you don't sample out of the population, FE is always the right choice since it will control for the firm-specific effects. RE is likely to suffer from omitted variable bias if you don't have a well-specified model.
Using a combination of firm-specific and time-variant global variables is also not a problem.
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Dear Colleagues,
I paid attention to that, when I estimate an equation by Least Squares in Eviews, under the options tab we have a tick mark for degrees of freedom (d.f.) Adjustment. What is the importance and its role? Because, when I estimate an equation without d.f. Adjustment, I get two statistically significant relationship coefficients out of five explanatory variables; however, when I estimate with d.f. Adjustment, I do not get any significant results.
Thank you beforehand.
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Are you attempting prediction or are you trying to do some form of “causal” relationship. If you are estimating a ”causal“ model you conclusions are conditional on the model estimated. Strictly speaking, it would be better to use the adjusted degrees of freedom - particularly with your small sample. In this case, a non-significant coefficient does not necessarily imply that the coefficient is truly zero. It is more likely that your sample is too small t establish a significant result. Its p-value must not be very far from your significance level. If the estimate is of the sign and magnitude expected by theory I would accept the value and report the p-value. Esimating 5 coefficients is a big demand from a sample of 23 obs.
If you are simply doing prediction or forecasting and are not attributing explanatory power to your coefficients you might be better with a simpler model which might have better predictive ability.
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Dear Colleagues,
If I have 10 variables in my dataset (time series) out of which 9 is explanatory and 1 dependent, and if I clarify that all the variables are non-stationary, should I take the first difference of the dependent variable as well?
Best
Ibrahim
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Econometric models estimated with non-stationary data are profoundly invalid and misleading (Greene, 2002). An example of a simple scenario: - in a regression with one regressor, there are three variables that could be stationary or non-stationary; namely the dependent variable (Y), the regressor (X), and the disturbance term (u). A suitable econometric treatment of such a model depends critically on the pattern of stationarity and non-stationarity of these three variables (incl. the dependant variable). Since quite often variables can be non-stationary at I(0), it is important to understand the forces behind such non-stationarity, which largely include structural breaks, deterministic trends, and stochastic trends. Differencing (including the explained variable as in your case) is a common appropriate in nonstationary models, and this is often correct (Granger & Newbold, 1974; Green, 2002; and Stock, James & Watson, 2011).
Granger, C. W. J., and Paul Newbold. 1974. Spurious Regressions in Econometrics. Journal of Econometrics, 2(2):111-120.
Greene, W. 2002. Time series Models. (pp. 608-662) In Econometric Analysis, 5th edition. Prentice Hall, Upper Saddle Rive, NJ.
Stock, James H., and Mark Watson. 2011. Introduction to Econometrics. 3rd ed. Boston: Pearson Education/Addison Wesley.
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I am working on the green solow model as part of my university studies. I am trying to create an econometric model close to the one of Brook and Taylor. However, I am desperately looking -without success- for a database that lists the abatement costs for reducing polluting emissions by country. Do you know a database that you can recommend to me?
Thank you for your time and I wish you a pleasant day / evening.
God bless you,
Cedric
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Thanks Prof. John for your info!
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Dear colleagues,
I am applying the PCA to political and institutional variables to create an index and use it in the regression analysis as a dependent variable. However, the variables which will form the main components contain different measurements. For example, while control of corruption ranges between -2.5 (weaker) and 2.5 (stronger), freedom of press ranges between 0-100 and if the value is higher, it shows fewer degrees of freedom of the press. So, I am in a loss to understand if this difference creates any hardship to PCA to produce a valid index. In other words, is it a problem for PCA if one variable implies higher success as the values of it get higher, while the other variable shows higher success as the values of it get lower? What should I do in this case? Thank you beforehand.
Best
Ibrahim
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Check if the bivariate scatterplots are linear or at least monotonically increasing or decreasing.
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How to assess the socioeconomic impact of the dam with econometric models and if you want to compare the impact on the livelihood of the same community before and after the dam construction which method is easy and applicable.
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The tool I normally use is public meetings with focus group discussions as a priority.
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I need an expertise consultation for best Econometrics model in order to assess technical efficiency of commercial banks on resource mobilization in Ethiopia.
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Dear sir,
I guess an intrinsic inclusive approach, founded/guarded through regulations would provide a sound participatory model that would help elevate socioeconomic balance. This should be considered a quite good approach for developing and underdeveloped countries.
If you are intended to go for some related publication, I would love to be a part of it.
My best regards,
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Dear respectful community researchers,
I am interested to find out the impact of Institution, Geography, and Trade on Economic Development of a single country.
I do know that for several countries' analysis, researchers mostly used the Hausmann and Taylor (1981) model to find the impact. Unfortunately, I have no idea if any model exists to be suitable to execute on a single country.
I am very much looking forward to hearing from you.
Thank you so much indeed
Best Regards,
Abdul Rahim
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To devleop these type of models on a single country, you have to start your study by a strong motivation to convince the reader why you do such a work on a single country. This has its limitations.
normally, we these type of questions Gravity models to be applied and Dynamic Panel data models for robustness.
As you are facing a single country, consult time series tools: Cointegration, ARDL, NARDL or VECM
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I am writing my dissertation about the effects of economic reforms on economic growth in the case of Egypt in 2016 and I need suggestions for what econometric models are usually used in testing such effects using indices like GDP, inflation and so on.
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It is not clear, whether your aim is to explain an extraordinarily high or low growth in 2016 (for that you will not need econometrics) or general effects on real and nominal GDP growth for a longer time period. Without an economic theory and a carefully derived model, econometrics will not help. To apply statistics, the main trouble will be to define what measures are reform and to transform these measures (which need not be directly measurable, e.g. agreements or treaties) into numbers. Another problem is that one should not mix up the effect on GDP and GDP growth. A (reform) measure would hopefully lead to a higher GDP level, but hardly to a higher permanent growth. It may take several years until the higher GDP is reached, then you have, of course, a higher growth during the adjustment, but not a higher growth afterwards (The percentage growth could even be lower because of the higher base). The timing of the GDP-effects is likely rather different for different measures.
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If we have to check the co movements/spill over effects between certain variable of Pakistan and 5 other countries, Is it possible by using a single GARCH model. If yes then WHAT will be the VARIANCE EQUATION?
OR       WE HAVE TO study EACH country one by one against  PAKISTAN AND KEEPING ALL OTHER countries IN VARIANCE EQUATION
Assume
basic model: A= Con + B+C+D+E+error. (refer to figure for detailed elaboration)
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Farshad Fathian Can you check, please. This might be the same as we discuss
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please give me some recommended econometric models for studying adoption of agricultural technologies
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What are the best econometric models for adoption study?
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I am interested in multi-hurdle econometric model to analyze multiple hurdle data for my study. Currently, I look for model specification materials and commands used for the analysis purpose in STATA. I really acknowledge and appreciate your help...!
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  • I want to estimate the stochastic frontier model to find the technical efficiency and technological gap of agricultural in each geographical region.
  • However, the outcome variable (agricultural income) is not a continuous variable but my outcomes variable is ordered categorical variable.
Example:
y = 1 ; if agricultural income is less than or equal 100$
y = 2 ; if agricultural income is more than 100$ but not greater than 200$
y = 3; if agricultural income is more than 200$
  • How can I estimate Stochastic Frontier Models for Discrete Output Variables with STATA or R? What command I could use? or Is there another approach to deal with this type of outcome variables?
The explanation of the model is in page 290: https://link.springer.com/chapter/10.1007/978-3-030-23727-1_9
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I think you may need to program it by introducing a known link function on your frontier model. If you are using a parametric stochastic frontier model (SF), say, y=x'b+v-u, where v is error term and u is inefficiency random variable, you may need to specify a link function F such that y=F(x'b)+v-u, so that it can satisfy the support of y. Not sure if available packages are there in R. It would be much simplified if you can observe the agriculture income directly.
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Trying to see how increasing minimum wage affects unemployment rates at the county level. Will look at state with federal min wage vs state with higher min wage. What variables need to be controlled for? Is there a better model/regression/method?
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In terms of control variables, you have to look at " asymmetric information theory", and for DID, it is a good method but you check the robustness with PSM-DID, Two-Step System GMM, even DDD.
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Hello,
I'm doing a research about the US-China Trade war impact on FDI right now but I'm not sure what model should I choose because the informations only appear in a short range(Between 2018-2019). My advisor recommend to use multiple regression with a Trade war as a dummy or use a VAR model. Do you have any recommend on what model should I use? Or if you have a recommend study/research about anylising shock effect on FDI then it would really helps me a lot.
Thank you
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A VAR model will be a good alternative. The shocks to the model is well captured in VAR modelling and to see at what stage the model settle down after a shock. It is not for forecasting but to trace the impact of the shock using Impulse-response function and variance decomposition.
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I would like to review some papers that uses econometric model to estimate Total Factor Productivity (TFP) of any country. I found papers using Solow Residual technique only. So, would you please suggest me some paper using econometric models to estimate TFP.
Thank you!
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Thank you!
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Dear All,
I would like to perform event study analysis through website: https://www.eventstudytools.com/.
Unfortunately they ask for uploading data in a format i dont understand , dont know how to put data in this form, and i dont find a user manual or email to communicate with them.
Can anyone kindly advise how to use this service and explain it in a plain easy way?
Thanks in advance.
Ahmed Samy
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Dear All,
I'm conducting an event study for a sample of 25 firms that each gone through certain yearly event (inclusion in an index).
(The 25 firms (events) are collected from last 5 years.)
I'm using daily price abnormal returns (AR), and consolidated horizontally the daily returns for the 25 firms to get daily "Average abnormal Returns" (AAR).
Estimation Window (before the event)= 119 days
Event Window = 30 days
1- I tested the significance of daily AAR through a t-test and corresponding P-value, How can i calculate the statistical power for those daily P-values?
(significance level used=.0.05, 2 tailed)
2- I calculated "Commutative Average Abnormal Returns" (CAAR) for some period in the event window, performed a significance test for it by t-test and corresponding P-value, how can i calculate the statistical power of this CAAR significance test?
(significance level used=.0.05, 2 tailed)
Thank you for your help and guidance.
Ahmed Samy
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I am working on the economic history of Switzerland and I would like to know which determinants foster industrialization during the 19th century.
I am working with time-series. My dependent variable is the Gross added value of Swiss industries and I have 5 explanatory variables (like education, railway, tariffs etc.). The times period studied runs from 1890 to 1913.
I first used a VAR model but reviewers are not so convinced... They prefer panel data (but I don't have !) or they think that VAR is unusual...
So, do you have any idea about the macroeconometric model I should use to deal with my research question ?
Thanks a lot !!
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Hi Charles, I visited Switzerland twice for 1-month and 6-month internships. Nice country. Yeah, Switzerland's industrialization should be a nice research topic, but I wonder about your period of study. Do previous studies consider 1890-1913 as a period of insustrialization in Switzerland? Do you have quarterly or monthly data for running the VAR model? For how many Swiss industries do you have the gross added value? Of course (since Einstein's job in Switzerland), do you have the time series of Swiss patents for that period? By industry or broken by type of patents? Considering the empirical section of your study, (if you only have annual data) I would strongly recomend to add the 40 or 50 years after the period of study because a plain VAR model is not good for small samples of data (a Vector Error Correction -VEC- model should work better for such data, instead). But there is another reason explaining the cold reaction of the reviewers: a VAR model is not quite useful for discovering the industrialization determinants since it is an "atheoretical" statistical model: you may need a "Structural VAR" (aka SVAR model) or better a Structural VEC (SVEC) model. Then you will be able (abstracting from the data frequency I already mentioned) to propose a theoretical structure (meaning, useful research estimated parameters) to discuss following your hypotheses. Those hypotheses should come from the previous section, the core section, which is the "theoretical" section. Such a section should consider at least the hypothesis you can select from reading many studies about economic history of Switzerland BUT also review some growth models, like the classic ones usually reviewed in macroeconomics' textbooks (Solow model, etc.) and other more recent ones like Romer's model (which consider the idea of Research & Development (please remember my idea about patents). For discovering those models, Romer's book for graduate students is the best. Finally, allow me a personal comment: I do like reading about economic history because it is a great companion for macroeconomic economists. It is such a down-to-Earth topic !
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Hello researchers and experts,
I am trying to run a Spatial Auto-regressive Model (SAR) and Spatial Durbin Model (SDM). But when I am trying to see impacts, that is direct, indirect, and total effect, R is giving an error by stating that : "Error in intImpacts(rho = rho, beta = beta, P = P, n = n, mu = mu, Sigma = Sigma, : length(listw$neighbours) == n is not TRUE".
Here is my codes:
SMod = readOGR(dsn = "C:/Users/FRJM/Desktop/Data/Spatial 3079", layer = "Sp3079")
Coord <- coordinates(SMod)
SpD100 <- dnearneigh(Coord, d1 = 0, d2 = 150, longlat = TRUE)
SpD100listW <- nb2listw(SpD100, style="W", zero.policy=TRUE)
Equ1 <- Volatility~A+B+C+D+E
reg.SAR1=lagsarlm(Equ1, data=SMod, SpD100listW, tol.solve=1.0e-20, zero.policy=TRUE)
impacts(reg.SAR1, listw=SpD100listW)
If I am writing code : summary(reg.SAR1), I am getting summary result. So there is no error on running : lagsarlm(Equ1, data=SMod, SpD100listW, tol.solve=1.0e-20, zero.policy=TRUE).
Only problem is running the "impacts" command.
Thanks in advance for your answer.
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Try to get a complete case analysis data (removing the missing values in all models) and then obtain the listw matrix. Then build a model and estimate the marginal impacts. That should work for you.
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Hello, I am dealing with a econometric model that uses system GMM. Please answer the following questions: 1.In order to get better result, what tests should I accomplish before running the final regression? I mean do I need to test autocorrelation, heteroscedasticity etc. for diffferent series of the panel data ? 2.What is the appropriate test to determine the endogeneous,exogeneous and predetermined variable in the panel data? How these issues can be resolved?
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Post estimation diagnostic tests are performed to provide proof for validity of estimates. The following diagnostic tests are necessary when GMM technique is applied: 1) first order/ second order serial correlation tests. 2) Sargan test/ Hansen test for over-identification restrictions. 3) Wald chi square /F ratio test for join significance for the model. You have to check the instruments to ensure they are less than the groups employed in the analysis.
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ex. there are two latent variables namely psychological and motivational factors of entrepreneurs. what could be possible explanation of this cov.
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If it seems to refer that they are almost equal constructions, which should first consider evaluating the dimensionality of each variable.
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Hello fellow researchers,
I am currently in the process of writing my master thesis and my topic of research is homicide rates in the developing world. My research objective is to find a relationship between capital punishment and homicide rates if there exists any.
The literature on similar topics are available on the internet, however I am not very good when it comes to statistics and econometric and would like some assistance in creating my econometric model for my research. I have gathered all the necessary data required for my dependent and independent variables.
Basically, I have a panel dataset of 24 countries from 2001 to 2012 and it is strongly balanced (no gaps or missing values in any variable). The only problem I am stuck at is how can I make my econometric model so I can actually use the data for testing and regression analysis.
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I have seen that some researchers just compare the difference in R2 in two models: one in which the variables of interest are included and one in which they are excluded. However, in my case, I have that this difference is small (0.05). Is there any method by which I can be sure (or at least have some support for the argument that) this change is not just due to luck or noise?
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Partial F-test will be useful hear. After the 1st variable is in, you add other variables ONE at a time. after 2nd bariable is added you have your y-variable as function of 2 variables giving model with 2 d.f. & certain Sum of Squares (SS). from 2-variable SS subtract 1-variable SS. that change in SS will have 1d.f. cost. So extra variable SS divided by '1' is the "Change in regression mean squares (regMS). Further, divide 2-variable residual SS (RSS) by 2-variable residual d.f to get Residual mean Squares (resMS). Now divide Change in regMS by resMS to get partial F-value & look up Tables for probability of partial F-value. If significant keep the 2nd variable in & do the same for any further independent variable you may want to add to your model. Adjusted Rsq is = 100*[1 - {(regSS/regDF)/(totalSS/totalDF)}]
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To illustrate my point I present you an hypothetical case with the following equation:
wage=C+0.5education+0.3rural area (
Where the variable "education" measures the number of years of education a person has and rural area is a dummy variable that takes the value of 1 if the person lives in the rural area and 0 if she lives in the urban area.
In this situation (and assuming no other relevant factors affecting wage), my questions are:
1) Is the 0.5 coefficient of education reflecting the difference between (1) the mean of the marginal return of an extra year of education on the wage of an urban worker and (2) the mean of the marginal return of an extra year of education of an rural worker?
a) If my reasoning is wrong, what would be the intuition of the mechanism of "holding constant"?
2) Mathematically, how is that just adding the rural variable works on "holding constant" the effect of living in a rural area on the relationship between education and wage?
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To assume that other variable do not change in order to allow for an evaluation of partial variation in a dependent variable due to variation in the only independent while other variables do not change
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Dear All,
Doing Financial Event studies using excel is just horrible process for the arrangement and chopping of huge data and complicated manual calculations..etc
Please advise what software are out there that can do Financial Event Studies in a more neat and time efficient way?
Thanks
Ahmed Samy
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Stata, Eviews and R can do time series measurement well. You need to know how to implement event study using these software from the literature review of other similar studies.
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I have used high frequency financial time series data for the study of futures market volatility and liquidity. wanted to ask that what are the major limitations of econometric models like GARCH and TARCH and how could they impact our results?
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Volatility by their very nature is unpredictable. Perhaps a more manageable task is scenario building rather forecasting the path of the volatility.
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Dear All,
I’m conducting and event study for inclusion of companies in a certain index.
The event is the “inclusion event” for companies in this index for last 5 years.
For the events, we have yearly Announcement date (AD) for inclusions, and also effective Change Dates (CD) for the inclusion in the index.
Within same year, I have aligned all companies together on (AD) as day 0, and since they are companies from same year, CD will also align for all of them.
The problem comes when I try to aggregate companies from different years together, although I aligned them all to have same AD, but CD is different from one year to another so CD don’t align for companies from different years.
How can I overcome this misalignment of CD from different years , so that I’m able to aggregate all the companies together?
Many Thanks.
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Dear Prof. Raymond,
My aim is to see what happens to returns of stocks when they join a certain stock exchange index, do they generate abnormal returns?
I’m trying to study that for the last 3 years.
So the event is “joining the index” which happens with 2 dates (1) announcement date (AD), on which stock exchange announces the news of those stocks will join the index, and (2) change date (CD), which is the date for really including those stocks in the index, this CD is is decided and announced by the stock exchange during the AD.
I have attached a similar work for your kind reference.
Thanks for your reply.
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I am running a regression in Stata.
As the dependent variable, I have the market share of smartphones (quarterly) for Apple and Samsung, and independent variables are Functional improvements and Design innovation (scored also quarterly).
My supervisor suggested that I have time fixed in order to account for the Christmas boost and I do not really understand how to do it.
And the second question is, am I capturing the interaction effect correctly?
So far I did...
xtset idcompany qdate
reg marketshare design function
and for interaction effect I did
gen designfunction=design*function
reg marketshare design function designfunction
and I got really good P values and R^2, but my coefficient for design*function is ( -.09) I am very curious how should I interpret it.
Does this all make sense? I am really new to Stata. I would really appreciate any help.
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Hello Alexa Drk. Generally speaking, Statalist (https://www.statalist.org/forums/forum/general-stata-discussion/general) is a better place to post questions about how to do X using Stata.
Second, I would think that if you are using -xtset-, you would want to use -xtreg- rather than -regress-.
Third, if you use the # or ## operators to include interactions (as in the file Aymen Ammari linked to), you'll be able to use -margins- and -marginsplot- to explore the nature of the interactions. See section 11.4.3 here:
HTH.
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Dears,
I'm conducting an event study for the effect of news announcement at certain date on stock return.
Using the market model to estimate the expected stock return in the "estimation window" , we need to regress stock returns ( stock under study) with returns from market portfolio index.
1- How can we decide upon choosing this market portfolio index for regression ?
Is it just the main index of the market?
Sector index from which the stock under study belong?..etc ?
2- Is it necessary that stock under study be among the constituents of this market index?
Appricite to justify your kind answers with research citations if possible
Many thanks
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You can consider using the country's stock market index as a proxy for market portfolio.
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I am doing some research on the growth of joblessness in the manufacturing sector of Pakistan. In the literature I have studied, the seemingly unrelated regression technique (SUR) was used instead of the ordinary least square (OLS) method. Can anyone help me to identify the reasons why OLS is not used here? Or under what conditions SUR technique should be used instead of OLS?
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Thanks to all of you for providing clear concept about SUR model.
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.
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There is no single, universal, multi-faceted model built of many indicators selected from fundamental and technical analysis, including market, economic and financial analyzes, indicator analyzes, due diligence and others that could be used to analyze the situation, economic situation, volatility, etc. for every capital market, including the currency market, stock market, market of raw materials and other production factors, and for every situation, i.e. bear market, bull market, balance, strong changes in trends, different levels of investment risk, various phases in the economic cycle of the economy, and irrespective of the national specifics of the operation of financial markets. Specific analytical models should be built for a specific economic situation, for a specific market. Then it is possible to achieve a high level of decision-making accuracy on the basis of forecasts formulated from this type of multi-faceted, complex indicator models for analyzing the current and prospective situation on a specific stock exchange market or other specific capital market operating in a specific macroeconomic environment, in a specific financial system, in a specific financial system country.
Greetings.
Dariusz Prokopowicz
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I'm working with life satisfaction as my dependent variable and some other independent variables that measure purchasing power (consumption, income and specific expenditures). To take into account the diminishig marginal returns of this last variables (following the literature) I transformed them in terms of their natural logarithm. However, now I want to compare the size of the coefficients of specific expenditures with the ones of consumption and income. Specifically, I would like some procedure which allows me to interpret the result like this: 1 unit of resources directed to a type of expenditure (say culture) is more/less effective to improve life satisfaction in comparison with the effect that this same unit would have under the category of income. If I just do this with withouth the natural logarithm (that is, expressed in dolars) the coefficients change in counterintuitive ways, so I would prefer to avoid this.
I was thinking about using beta coefficients, but I don't know if it makes sense to standarize an already logarithmic coefficient.
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Am not sure Santiago I follow what you said. Elasticity can be used and Beta weights can be used. If I understand you? I will interpret elasticity as I % increase in the RHS variable changes the regressand by the estimated sign and coefficient of the RHS eg LnY= 2 -0.5Ln(X) -- Here a percent increase in X decreases y by .5 on the avg
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I am completing a dissertation project for my undergraduate economics degree and am in need of any assistance that can be offered in guiding me towards an appropriate econometrics model to test the effect of trade policy on GDP in the following east African countries: Kenya, Tanzania, Uganda, Burundi and Rwanda. As such I was thinking that using a VAR model for each country repeatedly for 10 years would be a possible method to tease out the effect of trade policy.If possible could I model my work based on the following paper https://www.researchgate.net/deref/http%3A%2F%2Fwww.cluteinstitute.com%2Fojs%2Findex.php%2FJBER%2Farticle%2Fview%2F2801%2F2849
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I agree with Matteo Mazzarano . Ten years is not enough, You could use moor than two or three independent variables , you need moor than 25 years as a ( time series data) for every country.
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In a regression with a database with N=1200, I have an independent dummy variable that measures if the surveyed is unemployed or employed. The variable has the following characteristics:
Unemployment = 0 - Frecuency: 1196
Unemployment = 1 - Frecuency : 4
The regression gives me a significant coefficient, but, also, very counter intuitive (especifically, thay Life Satisfaction has a possitve association with unemployment). I think, however, that it's wrong to obtain a valid conclusion from just 4 cases in Unemployment=1. I also have other dummy variables where the situation is even less clear. For example:
Dummy = 0 - Frecuency: 1170
Dummy = 1 - Frecuency: 30
Or even more:
Categorical option A = 0 - Frecuency: 1150
Categorical option B = 1 - Frecuency: 30
Categorical option C = 2 - Frecuency: 12
Cateogorical optio D = 3 - Frecuency: 8
Can I obtain valid conlcusions from this? And, in more general terms, is there a minimun number of observations needed per category of response in each independent variable so the conslusions that arise from it are pertinent/correct? If that's the case, how can I calculate this number?
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I sm sgree with Paul
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In order to analyze if there is a mediation effect using Baron & Kenny's steps, is it necessary to include the control variables of my model, or is it enough to do the analysis just with the independent variable, the mediator variable and the dependent variable of my interest?
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"I don't have the theory to include more control variables that may me important for this model." -- so this is a statement. You know the field and the arguments why or why not a variable might have to be considered. You state that, at the best of your knowledge. So you can defend it and clearly point the advantages and limitations of your model. If reviewers (and, later, readers) have different ideas, they are invited to discuss it.
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I have a dummy varible as the possible mediatior of a relationship in my model. By reading the Baron and Kenny's (1986) steps, I see that, in the second one you have to test the relationship between the indepentend variable and the mediator, using the last one as a dependent variable. However, normally you won't use an OLS when you have a dummy as a dependent variable. Should I use a Probit in this case?
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Now I understand. Please look at the definition of dummy variable. What you mean seems to me to be a categorical variable. BTW Actually I am not. I just answer these questions because I enjoy the enlightened repartee. If you want to know my background you are welcome to click on my name where a number of my papers, affiliations, my degrees, etc are included. May the force be with you. D. B.
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I faced some difficulty in the analysis part of my research paper. I am in confusion whether to use excel analysis or Econometric model for data analysis. Which one is the best? expecting feedback from researchers.
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Where is your supervisor? Do you have statistical expertise available to you locally? Talking someone through conducting an analysis is like talking someone through landing an aircraft. You need to know a lot about the situation they are in, and the first thing you always say is "find someone who knows how to fly a plane!"
Excel's reputation for statistical accuracy is very poor. You need real statistical software. I am recommending jamovi (https://www.jamovi.org) to everyone as a starter package.
But liaise locally first.
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In my investigation about the determinants of subjective well being (life satisfaction) I have some variables that measure the access to food and also other variables that measure the affections (if, in the last week, the interviewed felt sad/happy, for example). These variables don't show high levels of simple Pearson correlations nor high levels of VIF. In experimenting with different models (including and excluding some variables), I see that access to food has a positive and significant coefficient, except in the ones that the affective varaibles are included. Can I make the case that this is due to the fact that affective variables are mediating the effect of access to food to life satisfaction? I also tried a with an interaction between access to food and affective variables but they are not significant.
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Thank you Paul!
And yes, David. What can you say about the question if you re-read it changing the word variable with "coefficient of the variable"?
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Reading Wooldridge's book on introductory econometrics I observe that the F test allows us to see if, in a group, at least one of the coefficients is statistically significant. However, in my model I have that, individually, one of the variables of the group I want to test is already statistically significant (measured by the t-test). So, if that is the case I expect that, no matter with which variable I test for, if I include the one that is already individually significant, the F test will also be significant. Is there any useful usage I can make with the F test in this case?
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Hello Santiago and Calleagues,
A t -test for one variable is identical to an F-test, in a simple regression model (with one explanatory variable); as a matter of fact in this case the square of the t-statistic value is exactly equal to the F statistic value (t^2 =F).
Once you include additional independent variables the F-test is the one that you rely to report the results. If you have significant F- test then you report the regression results and you look which explanatory variables are significant using the t-test. However if the F-test is insignificant you stop there and you do not report the regression.You need to find another model that at the minimum passes the F-test.This is a specification issue.
Kind regards,
George K Zestos
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I have a well endowed database with almost 29 0000 observations and I want to make an analysis with more than 50 variables. What are the problems that can arise from this situation? Can the model be overfitted? If it is possible, why?
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Yes. Your model can be overfitted. You can think of overfitting in several ways, but let us take two different avenues. First, number of relevant variables. Imagine that the truly correct model has only 30 of the total of 50 variables that you happen to have. Whatever method you use to identify the correct variables in your model can lead you to "false discoveries". This is closely related to the type I error in statistical inference. You can be very strict with the type I error, but this will never be zero. So, you are admitting the possibility of false discoveries. These false discoveries have more chances to occur, the more variables and transformations of variables you try over the same sample. You mention that you have 50 variables...but what about using the squares of these variables, or logs of them or product of pairwise combinations of them. The more combinations you try over the same sample, the more likely is that you end up with false disvoveries: variables that are not in the model...but your statistical method is unable to detect that they are not....
Second, imagine that the true share of variance that can be explained in your model is 50%. So, this is the best R2 you can get if you could identify the correct model. Now, because you are trying to find the correct variables using the same sample over and over again, you end up with a sample R2 of 75%. You again have an overfitting issue induced by the data mining process.
Large N helps but a lot of the overfitting problem relies on the repeated use of the same sample to find a correct model.
This is a key topic....hope it helps!
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I have reported life satisfaction as my dependent variable and many independent variables of different kinds. Of them, one is the area in which the individual lives (urban/rural) and other is the access to public provided water service. When the area variable is included in the model, the second variable is non significant. However, when it is excluded, the public service gains enough significance for a 95% level of confidence. The two variables are moderately and negatively correlated (r= -0.45).
What possible explanations do you see for this phenomenom?
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Mr. Valdivieso. I think the Multicollinearity is almost definitely the problem. Try the simple VIF test. Then try to change the way you measure the variables Area & Access. Good luck.
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Suppose, No one researcher does so with the help of econometrics model as before. As a beginner can i solve all these analysis with the help of econometrics model irrespective of considering the other factors what i don't know?
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Why not? The most of the prior studies used econometrics model.
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I have read that it's relatively unproblematic to include dummy variables (for structural breaks for instance) in ARDL cointegration. How is this done? I have included them simply as independent variables (Chow and Bai-Perron tests suppport the break dates). Is that ok? I use Stata, with the Stata module "ARDL".
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Yes, you can but only as an exogenous variable (just like you treat seasonal dummies).
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I'm investigating the determinants of subjective well being in my country and I have a well endowed database in which I found a lot of environmental, psicosocial