Applied Econometrics - Science topic

It is generally considered essential, though not strictly mandatory. That is, the thories provide frameworks for understanding complex social phenomena, guiding research questions, and interpreting findings. They also help researchers make sense of data and offer explanations for observed behaviors and trends.

(i) Total digital payments made by the firm

(ii) Number of financial transactions

This information can be found in the financial statement of banks

Bloomberg terminal

The HDI values for the years 2008 and 2009 were calculated using the new methodology introduced in 2010.

Check these.

Sustainable climate risk management at the firm level is a complex and evolving field. It typically involves identifying, assessing, and mitigating climate-related risks while also seeking opportunities for sustainability and resilience. There's no one-size-fits-all model, as each firm's specific circumstances, industries, and locations may require different approaches. However, you can consider the following general framework as a starting point:

Climate Risk Assessment: Begin by conducting a thorough assessment of climate-related risks. This includes physical risks (e.g., extreme weather events, sea-level rise, and resource scarcity) and transition risks (e.g., policy changes, market shifts, and technological advancements).

Collect Climate Data: Gather data related to climate trends, both historical and future projections, that are relevant to your business and industry. Consider using climate models and scenario analysis.

Financial Impact Analysis: Assess how these climate risks might impact your firm's financial performance, assets, and operations.

Develop various climate change scenarios to understand how different climate outcomes could affect your business. This is critical for risk management and resilience planning.

Physical Risk Mitigation: Implement strategies to protect your physical assets from climate-related damages, such as investing in resilient infrastructure or relocating vulnerable assets.

Transition Risk Mitigation: Develop strategies to adapt to policy changes and market shifts. This may include diversifying products and services, transitioning to low-carbon technologies, and staying informed about relevant policy developments.

Consider integrating sustainability measures to reduce your firm's carbon footprint and environmental impact. This can include energy efficiency initiatives, renewable energy adoption, waste reduction, and supply chain sustainability.

Develop resilience plans that ensure business continuity in the face of climate-related disruptions.

Consider voluntary reporting through initiatives like the Task Force on Climate-Related Financial Disclosures (TCFD) to communicate climate risks and strategies to stakeholders.

Engage with industry peers, government entities, and non-governmental organizations to stay informed about the latest climate developments and best practices.

Be prepared to adapt your strategies as climate risks evolve and new information becomes available. Continual learning and flexibility are key.

Regularly monitor and assess the effectiveness of your climate risk management and sustainability measures. Transparency in reporting can help build trust among stakeholders.

Ensure compliance with relevant climate-related regulations and standards. Stay informed about evolving regulatory requirements.

It's important to tailor your firm's climate risk management model to your specific industry, location, and risk profile. Consider seeking advice from climate risk experts, and collaborate with stakeholders to develop a comprehensive and sustainable approach to climate risk management.

Here is a link

In panel data, it is possible to fit fixed effect models separately for each dummy variable, but this approach is not recommended because it can lead to biased estimates and inefficient inference.

Fixed effect models in panel data are typically used to control for unobserved heterogeneity that is time-invariant. This is accomplished by including a set of dummy variables for each individual in the data. The inclusion of these individual-specific dummy variables absorbs the time-invariant heterogeneity, allowing for unbiased estimation of the remaining time-varying effects.

Fitting fixed effect models separately for each dummy variable would essentially be the same as estimating a separate model for each individual. This approach would fail to account for the time-invariant unobserved heterogeneity that is common to all individuals. As a result, the estimates would be biased and inefficient.

Therefore, it is recommended to include all the dummy variables in the model together as a set of fixed effects. This allows for a more efficient estimation and unbiased inference of the time-varying effects.

@Randimal Herath

It depends on how the shock influenced the treated and control unit.

If you can argue that the shock influenced all units in the same way, then the inclusion of two way fixed effects (particularly time fixed effects in this case) can help you to rule out the effects of such 'common' shock.

However, if there are reasons to think that the structural shock differently affected treated and control units, then this could result in potential biases in the estimation and identification of the average treatment effect.

There is a novel literature dealing with the correct identification of such effects. I would suggest you reading the following paper:

"VISUALIZATION, IDENTIFICATION, AND ESTIMATION IN THE LINEAR PANEL EVENT-STUDY DESIGN" from Simon Freyaldenhoven, Christian Hansen, Jorge Pérez Pérez and Jesse M. Shapiro. You can find the paper here: http://www.nber.org/papers/w29170

I particularly suggest you to read section 3.1. of the paper as long as accompanying the paper there is the Stata command "xtevent" which is particularly useful to deal with such econometric analyses.

Answer from ChatGPT

It is possible to build a GMM with the number of observations (n) being lower than the number of time periods (t). However, in general it is recommended to have a larger number of observations than the number of parameters being estimated. This is known as the "curse of dimensionality" and can lead to overfitting and poor model performance. Additionally, a GMM requires estimation of the mean, covariance, and mixing proportion for each component, which may be difficult to do with a small number of observations. Therefore, it is generally recommended to have a sufficient amount of data when building a GMM model.

Look at a scatterplots which may help but it seems that you don't have much of a relationship here. Best wishes David Booth

The difference between R squared in the ARDL model and R squared in the bounds test is that R squared in the ARDL model is about the rubostness of the overall model result and not about the robustness of the bounds test result only. This means R squared in the ARDL model will tell you how much the variability in Y or Ys (your dependent variable/variables) is/are explained by the ARDL model given Xs variables (your independent variables). On the other hand, R squared in the bounds test is specifically about the bounds test result within the ARDL model and not about the robustness of the overall model result. You need an ARDL model first to have a bounds test but not a bounds test to have the ARDL model. I hope it helps you.

thanks to both of you!

I have often been approached with questions such as this. I would have known the person and his background in economics, statistics/econometrics, linear algebra, and mathematics. I would also have known or asked why he wanted to proceed in this way and what he hoped to achieve. The lists below are long but I would hope that you find something there that is useful.

I know of three modern introductions to econometrics that are accompanied by R companions.

The first three books cover an undergraduate introduction to econometrics. I found item 5 very useful but you probably need to have a good knowledge of econometric theory to take full advantage of it.

There are also various graduate textbooks on time series analysis, panel data analysis, and various other aspects of econometrics.

Econometrics is often taught as a series of recipes. e.g. estimate the following expression using OLS, without an explanation of the importance of the underlying economic theory. Causality is often inferred from tests of significance without an understanding of how the statistical tests depend on the underlying economic theory. The ideas of tests of hypotheses and p-values are often not understood. The books below are attempts to explain what can be achieved by statistical/econometric analysis and how to avoid false conclusions.

The coefficient of ECM is also consistent with the rule of thumb which suggests that, the coefficient of error correction term (ECT) should be negative, less than one in its absolute value and significant.

I recommend that you use the free statistical program JAMOVI which allows the inclusion of multiple moderators and mediators.

I will answer this question by suggesting that you consider your basic model.

1. You have a strange mixture of nominal, real variables and growth rates.

2. Interest rates are the equivalent of log differences

3 If the growth rate of M4 is non stationary this implies that the log of M 4 is I(3). I find this hard to believe.

4. Are there seasonal effects in your data?

5. I would be very doubtful if ARDL is valid in your case.

6. If I understand correctly you intend to identify your system by using a particular ordering of your variables. It will be difficult to justify any particular order

7.I would be concerned that you might have trouble accounting for bubbles in house prices and subsequent falls. The 20 years that you are using was a very difficult time for house prices and they were out of equilibrium for a lot of this period. Your project is very ambitious.

8. I would recommend that you talk to your supervisor and agree an analysis that might be more feasible..

Mr a. D.

The ECT(-1)os always the lagged value of your dependent variable.

Regards

bellow this link

https://stats.oarc.ucla.edu/sas/dae/multinomiallogistic-regression/

Technological progress is measured by the rate at which these inputs improve, and the direction of technological progress is represented by the relative pace of these improvements.

Technological access is measured by the adequate skills and effectiveness.

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. :-)

Either something went wrong or performed the same test. Did you use the same version of the software as the original study?

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.

If I have understood the question well, you have a system of regression equations, not a single one; unless you replace the alpha coefficients in the main equation which yields a new one in terms of new exogenous variables (Z'n x yn, etc.). Then alpha coeffs will disappear and the single regression can be solved for the other coeffs, i.e. betas and gammas. Alphas can be calculated easily.

Hi there, I am using Eventus software and I am wondering how the software computes the Market index in order to calculate abnormal returns?

Often, the log-normal distribution is a sensible assumption for percentages that are not bounded at 100%. Are there any arguments against this in your case?

It makes a huge difference for interactions if you analyze the data on the original scale or on the log scale. If is is sensible to assume relative effects (a given impulse will change the response depending on the "base value" of the response, so absolute changes will differ when the base values differ), interactions seen on the original scale are highly misleading in terms of the functional interpretation (i.e. when you aim to understand the interplay of the factors).

I thought PMG estimator (a form of ARDL ) is more approprite

*Panel ARDL

*PMG

xtpmg d.output d.fcapinf d.bankcr d.pfolio d.equity, lr(l.output fcapinf bankcr pfolio equity) ec(ECT) replace pmg

xtpmg d.output d.fcapinf d.bankcr d.pfolio d.equity, lr(l.output fcapinf bankcr pfolio equity) ec(ECT) replace full pmg

*MG

xtpmg d.output d.fcapinf d.bankcr d.pfolio d.equity, lr(l.output fcapinf bankcr pfolio equity) ec(ECT) replace mg

xtpmg d.output d.fcapinf d.bankcr d.pfolio d.equity, lr(l.output fcapinf bankcr pfolio equity) ec(ECT) replace full mg

*Hausman Test to Know Which to Choose (Significant prob. will mean choosing MG and insignificant prob. will mean choosing PMG)

hausman mg pmg, sigmamore

Kindly visit..

http://www.laits.utexas.edu/orkelm/kelmpub/VSM2013_Manual.pdf

I agree with researcher Abdulrahman M. Ahmed . There is not necessarily a correlation between national energy consumption and national GDP. Steven Setiawan, you should try to have the information on energy consumption by province.

You can yes. You can also use Eviews or Stata softwares

Depending on you objective statement, if your objective is to compare variables that influence a particular problem, you can use the t- test to compare and them give justification.

Dear All,

I have worked on this and written codes below for estimating marginal effect after MVPROBIT. I hope this will be useful. *Example data use http://www.stata-press.com/data/r7/school.dta, clear *Step 1: Running the MV probit model mvprobit (private = years logptax loginc) (vote = years logptax loginc)

*Step 2: Post estimation command to estimate Predictions from multivariate probit models estimated by SML mvppred pred_xb, xb *Step 3" Generate coeefiencts for each binay category gen Coef_years_private = -.0089447 gen Coef_logptax_private =-.1018381 gen Coef_loginc_private =.3787381 gen Coef_years_vote = -.0160871 gen Coef_logptax_vote =-1.260877 gen Coef_loginc_vote =.9744685 *Step 4: Calculating Marginal effect using (Coefficients and linear Predictions) gen ME_years_private =normalden(pred_xb1)*Coef_years_private gen ME_logpatx_private =normalden(pred_xb1)*Coef_logptax_private gen ME_loginc_private =normalden(pred_xb1)*Coef_loginc_private gen ME_years_vote =normalden(pred_xb1)*Coef_years_vote gen ME_logpatx_vote =normalden(pred_xb1)*Coef_logptax_vote gen ME_loginc_vote =normalden(pred_xb1)*Coef_loginc_vote *Step 5 After estimating the ME for each observation we can get the mean using the summarize or mean command.

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

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.

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:

Just perform a regression of the variable on time or a simple correlation.

Neverthless, usually, what we do is to carry out a test for mean differences at two different points of time.

Byrne, Joseph P., and Roger Perman. Unit roots and structural breaks: a survey of the literature. University of Glasgow, Department of Economics, 2006.

In stata software there is xtbunitroot command for break point unitroot test.

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.

Sigmasq is sigma square of the distribution of residuals which is considered as a proxy of the variance of the distribution of the dependent variable. Indeed this distribution is necessary to maximum liklihood method

Sigmasq is estimated in the second step after estimating the parameter related to estimators

Otherwise, SE is the standard error of regression which is the average of differencesome between actual values and fitted values of the dependent variable

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.

You may find this attached paper helpful.

Best wishes, David Booth

Can you use daily input data to forecast daily output data then cumulate to monthly? You will be smoothing output forecasts, but observed output is already smoothed.. Because output data is only available monthly, daily variation in output is not observable.

As pointed out by the answers given above Granger-causality represents a particular type of causality related to precedence. Granger-causality testing can also be used as part of a strategy to determine whether variables are weakly, strongly and/or super exogenous - see the reference below:

Engle, R.F., Hendry, D.F. and Richard, J.F. (1983) Exogeneity. Econometrica, 51, 277-304. http://dx.doi.org/10.2307/1911990

If you have more than two variables you should consider multivariate Granger-causality style testing using a VAR. You should also consider whether variables are stationary or nonstationary. If variables are stationary you can apply Granger-causality testing in a stationary VAR. If the variables are nonstationary and not cointegrated you can difference the variables and apply Granger-causality testing in a stationary VAR of the differenced variables . If the variables are nonstationary and are cointegrated you can apply Granger-causality testing in a VECM (that has short-run and long-run components) that assumes all the data are stationary by either cointegration or differencing transformations. If there is no cointegration the error-correction term can be excluded and testing is conducted by a Wald test in a stationary VAR. Alternatively, you can use the Toda and Yamamoto (1995) surplus lag Granger-causality test that uses nonstationary data. See Dave Giles blog for a discussion of this - the link is given below.

This method means you do not need to consider the notions of long-run and short-run Granger-causality. This is useful if you simply wish to know whether Y Granger-causes X for example. In a VECM long-run Granger-causality is tested using the t-ratio on the error-correction term in each equation. Short-run Granger-causality uses Wald tests to set the coefficients on all lagged differences of on variable to zero in an equation.

Since your objective is not to test for causal link between the variables, I think you can perform the ARDL without the causality test. The Granger causality does not necessarily address the cause-and-effect relationship between the variables however, it helps to determine if one variable can predict or forecast another variable.

See these links:

This estimator allows you for controlling for heteroskedasticity and serial correlation of the form AR, not every type of serial correlation. Clustered standard errors are robust to heroskecasticity and any form of serial correlation over time.

When we are interpreting the estimated model, then we extract all information, based on the conventional level of significance. The most important issue in your research is that how your analysis is related to a real-world problem, that is economic justification behind the significant and non-significant results. Similarly, I suggest you estimate multiple different econometric models and then compare these models based on economic theory, statistical analysis. This exercise will ultimately help you to select a final and more efficient model.

Dear Mr. Niftiyev, your dependent variable varies betweem -2,5 and +2,5. Hence, it is better to employ a Tobit, Probit or Logit approaches if possible. The choice between these three approaches depends mostly on the distribution patterns of the variables.

If the time series is short, you can can be converted the data into quarterly data, it will give better results

Wilhelm Lengler I found this discussion in statalist that might help to solve this question

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.

Good question

I agree with anton

مهتم

First generation panel data analysis often assume cross sectional independence, i.e the shock to a variable in a country will not have any effect on the other countries variables. However, as a result of globalization and other related cross nation interlinks, it is apparent that a problem in country A can affect country B. Most of the conventional panel analysis like fixed effect, Random effect, Panel ols, among others fall into this category. In order to correct the bias in the estimate of 1st generational panel analysis, the 2nd generational panel analysis was invented. This methods appropriately incorporate the cross sectional dependence in the modeling. This includes methods like ccemg, cs-ardl, cup-FM and so on..

Hello. I hope you are doing well. It seems to me that in this case you can use NARDL midel to check the Asymmetric Impact of price volatility besides the ECM. moreover, to check the causal direction you can apply Toda Yamamato test.

Good luck

Does anyone know if you can run the MG estimator in Eviews? (Hausman test rejects null hypothesis therefore PMG is not efficient, and i need to use the MG estimator.)

I should know best what you are estimating, but, in principle, age is one of the few exogenous variables. The endogeneity/exogeneity of one variable is a thing can be mainly assessed retorically. At best, and I supposed is what you are doing, you can compare IV with OLS through a Hausman test (some people call that endogeneity test) but it is subject to the assumpation that the IV is valid.

Being a relationship not estimating before I would suggest perform the analysis without IVs. Then, if possible, at most, try to find an IV showing similar results to defend your analysis with a robustness check.

Best,

José-Ignacio

You can use auto.arima function from 'forecast' package for R.

Alternatively, if you have many observations, you can try out-of-sample comparison of alternative models with different values of d.

To compare alternative models, you can use the instructions described here:

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)}]

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

Not so much difference between the two at the level of estimation. At the level of testing however, the augmented ARDL takes the testing further. For ARDL, the dependent variable is required to be I(1). The augmented ARDL overcome this by a number of tests to sort out things properly in order to avoid the degenerate cases.

Now Eviews does not directly provide the testing procedure for augmented ARDL. However, there's an addin called NARDL on the Eviews page written by yours sincerely. After estimating your model just the way you would normally do, then use the Make Testable Form to generate an OLS version of the results. All the test you need to carry out can then be done directly.

Note that the new set of critical values has been suggested. You need to use those critical values and