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# Panel Data - Science topic

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Questions related to Panel Data

Hello Colleagues

Trust you are all fine,

Please I have been struggling for days on estimating some spatial diagnostic test on my model. The stata command I am using is spatdiag command.

I am working on a panel data with 759 observations. However, I am using a 33x33 matrix. Each time I run the spatdiag command after the regression model, I get this error "Matrix W is 33x33, regression has been carried out on 759 obs. To run -spatdiag- weights matrix dimension must equal N. of obs"

This problem is in similitude with this https://www.statalist.org/forums/forum/general-stata-discussion/general/1531048-how-to-run-lm-tes-to-check-of-spatial-auto-correlation-in-panel-data, but no response.

I would be glad if I can get a comment on how to go about. I look forward to hearing from you

What are the pre-estimation tests or cautions for Dynamic Panel Data Analysis? For example, in Pooled OLS we run the Unit root Test, Cointegration Test, VIF test etc.

I have panel data for 30 years and fr 50 countries. Now I want to take 5 years avaergaes of data but there are missing values. e.g for some range in some panels, there are only 3 values avaialble. Now should I divide it with 5 (total years in this range) or 3 (number of years for which data is available)?

I am writing a PhD thesis and extracted data from 11 years annual financial statements of the selected sample. Please, need your guide for Whether I can use PLS-SEM to analyse the data and if NOT what will be the best regression tool to analyse such data? Thank you.

Hi, can anyone share best proxies to measure the firm-level Fintech activities?

What is the data source?

Greetings to everyone

I would appreciate it if you could help me

**According to the articles and econometrics book of the Panel; Including Professor Gujarati's book and other books, can I conclude that in estimating the panel data model for my own research; If the number of research stages (186 countries) is more than the number of years of research (24 years), is there no need to perform stationary test?**

**I would be grateful if you could introduce me a phrase to prove that there is no need for stationary test in such cases, referring to the phrases, page, text, and name of the book (article).**

Hi everyone,

I would highly appreciate if you could practically instruct me how to do unit root test within the panel data of cross-sectional dependence and structural breaks, using STATA or RStudio.

Thank you so much!

Long story short:

I use a long unbalanced panel data set.

All tests indicate that 'fixed effects' is more appropriate than 'random effects' or 'pooled OLS'.

No serial correlation.

BUT, heteroskedasticity is present, even with robust White standard errors.

Can someone suggest a way to either 'remove' or just 'deal' with heteroskedasticity in panel data model?

I am running a panel data and the Hausman test shows p-value = 0.0113, which means that FE model is better. What test is required to choose between the Fixed effect model and Pooled OLS? Pls kindly explain clearly the test and the steps or Stata command? Thank you very much.

**In my data set, T is greater than N, so I chose quantile regression for my data set. So is it appropriate for that?**

Dear all, I have a user written estimation code based on the paper dynamic panel data estimations with fixed effect by Galvao(2009). I have seen many papers using this method and pvalue, standard errors and t statistic were reported, however, the code only shows the beta coefficient. So I'm guessing these papers used bootstrap standard error. I have been trying to get these but I haven't gotten it yet. I'll be glad if anyone who has used this method could provide the complete code or help with mine

My thesis includes variables of inflation rate (monthly), interest rate (weekly), exchange rate (daily), and crude oil production (monthly), as well as imports and exports (annually). The period is from 2018 to 2024. I will use many pre-tests such as unit root tests, cointegration tests, etc. Before starting, I am worried about the missing data. My missing data is in the exchange rate and interest rate. I need guidance. Since I want to use panel data, is interpolation useful for time series data? Will it provide accurate results? Please help and guide me. Thank you.

Hi guys,

in the context of my master thesis i analyze the statistical relationship between income and subjective well-being (Panel: SOEP, n: 300.000 observations over 10 years).

After creating a model that is in harmony with the existing literature i conducted a fixed-effects "within" Regression (with robust standard error) that includes all relevant control variables.

I got a highly significant (0.01 level)

**regression coefficient of 0.1**for my income variable.Despite that i received a

**R squared value of 0.06 and a negative adj. R. of -0.19**.I do not really know how to interpret the negative R squared. Does it mean my model doesnt fit and has no explanatory power?

I was expecting a small R squared duo to the many factors influencing subjective well-being, but not a negative one?

Anyone got an advice how to interpret this result? Can i still draw conclusions regarding the statistically significant coefficient and a causal link between income and SWB?

Im thankful for any advice!

hello researcher greetings

Actually I want to run panel data model in stata, my panel data consist monthly time variable with 6 cross-sectional observation. When I am putting my data on stata the time variable is coming to be string. When i generating monthly time variable, the time variable get extended to many time period ahead. Can any one help me to solve such problem.

Suppose, a variable like FDI has no data over a period of time in a country. At that time, how can we run our model on panel data?

added an answer

22 seconds ago

Hi my peers,

I have study with panel data from 2009 to 2018. My study is causal relationship . I need to highlight the temporal effect on the regression results... How can I do that?

I tried two methods but each produced different result to some extent.

The first was through creating year dummies through command.... tab year, gen (yr_) ,this produce ten dummy years.

The second method was through i.year which produced only 9 years, leaving the beginning year i.e 2009. However, the regression result to great extent unchanged but significant years become less in i.year inclusion.

So, please

I dont know what correctly to follow of these or if there is another way that show the temporal effects on the study findings.

Thank you in advance for your help

I have performed panel data regression analysis and selected the FEM model, and also used GLS to address autocorrelation and heteroscedasticity. However, my R-Square value is so high that it almost reaches 1, is that true? because my lecturer doubts this.

I have panel data with 3 variables, monthly data from 2015 to 2024, and 190 countries. I want to analyze the data and see the monthly trends in all 3 variables. Please suggest how I can graphically represent my panel data. and which statistical technique I can use to analyze the data.

i want to know; that how to proceed with the mentioned data set to capture the long run and short run association in panel data, thank you

Should the unit root test on panel data T=5 year N=38 still be carried out?

I am trying to calculate an ECM with panel data and thereby I have the following problem. I run the ecm command from the ecm package an error occurs saying “non-numeric matrix extent “. I found out that the reason is the creation of the panel data. I tried two different approaches to fix the problem.

At first I created the panel dataset with the pdata.frame command from the plm package.

p.dfA <- pdata.frame(data, index = c("id", "t"))

where “index” indicates the individual and time indexes for the panel dataset. The command converts the id and t variables into factor variables which later leads to the error in the ecm command.

Secondly, I created the panel dataset with the panel_data command from the panelr package.

p.dfB <- panel_data(data, id = "id", wave = "t")

where “id” is the name of the column (unquoted) that identifies participants/entities. A new column will be created called id, overwriting any column that already has that name. “wave” is the name of the column (unquoted) that identifies waves or periods. A new column will be created called wave, overwriting any column that already has that name.

This panel_data command also converts the id variable into a factor variable. So, the same error occurs in the ecm command.

If I transfer the factor variables back into numeric variables, I lose the panel structure of the dataset.

Could someone please explain to me how to run an ECM with panel data in R?

Thank you very much in advance!

*Sample R Script attached*

I am a doctorate degree student, working on mt thesis.

Panel Error Correction Model (PECM) not the model Panel Vector Error Correction Model

I want to understand why most analyses conducted in Africa use GMM instead of other panel data estimation techniques.

If the number of observations is small, does it affect the results and reliability

Hi,

In the panel data model, where I'm researching the effects of demographic indicators associated with the aging of the population on the Poverty Risk Rate indicator. In this model, I found a negative regression coefficient for the regressor Proportion of seniors (it is a statistically significant effect), which means that with a higher proportion of seniors, the rate of poverty risk should decrease and vice versa. Please, how could I explain this in my thesis? The model is also tested for heteroskedasticity, autocorrelation, and multicollinearity, and all come out well.

Thank you!

Could you please clarify how to add robust (white) errors to the function -xthenreg in Stata14? I am running Threshold GMM Estimator (

*Seo*and Shin (2016)) for my panel (N=60, T=28). Commands such as -vce or -robust, that are applicable for other types of threshold regressions or usual GMM, do not work here. Thank ypu for your help!#panel

Hello

I have two questions:

1. Can I use more than one method for filling the missing values? Like moving averages, and imputing mean where moving averages didn't work?

2. Are the methods for treating missing data in panel data same like any other? Can I use 2 period moving averages if when I plot it on excel '2 period moving averages' is the best and closest representation of my available datapoints?

Thanks.

I am working on a panel data study. All the variables are stationary at the first difference level. My data shows cointegration among panels. So how should I proceed further with the estimation. I am using stata 17. On running the xtcointreg command, it shows it is not recognized, while I have already installed the xtcointreg package.

I am working in panel data. Could anyone help me the stata command for Durbin-Hausman cointegration test, proposed by Westerlund (2008)? Because I got a mixed order of integration.

I have a dataset with T=11(2010-2020) and N=63(provinces). I want to estimate the causality relationship in short run and long run between 2 variables. Which model can be suitable?

I am having trouble differentiating between a random effects model and a linear mixed effects model. I am currently using this model https://bashtage.github.io/linearmodels/panel/panel/linearmodels.panel.model.RandomEffects.html#

for my research. Can somebody tell me if this is a random effects model or a linear mixed effects model and what are the differences between the two models?

I need Non-Linear Causality test or model to use in panel data, in R, Stata, Python..

I have independent variables on company level (panel data) while dependent variable is timeseries. what type of regression will be conducted?

I want know one can use autodistributed lag model to estimate panel

In the attached image, D1 dummies are for interaction term of public debt for each state, and D2 is a single dummy for structural break of Fiscal Responsibility Legislation implementation year which is different in each state. Is this specifcation of model is correct in a panel data fixed effect model?

Also, how we can understand we have to for GMM model for our data? why do we go for different GMM models when there are random and fixed effect methods that can be decided by the Hausman test and which almost cover individual and time-specific effects? Also, to get entities' specific slope, we could use a random coefficient model or hierarchical regression model, right?

Thank you

panel data series are stationary of mixed order , I(0) and I(1) and dependent variable is stationary at level. is PMG and MG estimation methods would be suitable or not? Can these methods work ....if dependent variable is stationry? suggest if any other method of estimation will works better.

Hi,

I am preparing a thesis on whether there is a relationship between the development of financial institutions and economic growth. I'm using panel data, and I'm a little confused about fixed ,random effects model and VAR model, what's the difference between them ?

Please is ethical to collect data using international currency like the US Dollar and generalised for countries with different welfare levels, Balance of Payment and other macro variables that can influence the exchange of local currencies and the US Dollar. What is the alternative to this method?

I'm currently working with a panel data set that has 6 cross-sections and 19 time periods. In the process, I've encountered issues related to endogeneity, as well as challenges with both stationary and non-stationary data. To address these, I decided to run a panel ARDL, but unfortunately, I faced a near-singular matrix issue. This led me to remove a few variables that I initially wanted to test.

I'm reaching out to seek your advice. Are there alternative methods or approaches I could consider for a data set of this nature? Your insights would be highly appreciated.

Hi everyone, i have a question regarding my dataset and would be grateful for any help:

I have a dataset i downloaded online that gives me information about variables like Pool size, Dealvolume etc. Every observation is a different tranche that is part of a deal (a deal may contain multiple tranches). The Tranches are included at the time the deal is launched. The dataset contains every Tranches from the last 10 years. From my understanding panel data is data collected at multiple times. The problem of understanding i have is that every observation (tranche) is just collected once, at launched data. Although the dataset contains observations over the last 10 years, so i collect data over time (-> panel data) but not for the same entities, because a tranche is part of a deal that is observed when the deal launches. So looking at tranches in year 1 and year 4 they're not the same tranches, as they are part of a deal.

I hope i managed to explain what i mean. I have problems to distinguish if it's a panel data or a pooled cross sectional data or maybe something else?

Help is really much appreciated. Kind regards.

Hello Everyone...

I want to write my thesis for this purpose. Anyone guide me? What are the steps of the panel ardel test in eviews (a-z)? What tests do we do for panel data?

Hello, I am trying to analyze factors that influence the adoption of technology, and while doing that, I am facing issues with rbiprobit estimation. I have seven years (2015-2021) balanced panel data that contains 2835 data. The dependent variable y1 (Adopt2cat), the endogenous variable "BothTechKnowledge," and the instrumental variable "SKinfoAdoptNew" takes value 0 and 1. Although the regression works, I am unsure how to include panel effects in the model.
I am using follwing codes:
rbiprobit Adopt2cat ACode EduC FarmExp HHCat LaborHH LandSizeDec LandTypeC landownership SoilWaterRetain SoilFertility CreditAvail OffFarmCode BothTechAware IrriMachineOwn, endog(BothTechKnowledge = ACode EduC FarmExp HHCat LaborHH LandSizeDec LandTypeC landownership SoilWaterRetain SoilFertility CreditAvail OffFarmCode BothTechAware IrriMachineOwn SKinfoAdoptNew)
rbiprobit tmeffects, tmeff(ate)
rbiprobit margdec, dydx(*) effect(total) predict(p11)
If we do not add time variables (year dummy), can we say we have obtained pooled panel estimation? I kindly request you to please guide me through both panel and pool panel estimation procedures. I have attached the Data file for your kind consideration.
Thank you very much in advance.
Kind regards
Faruque

I am currently conducting research using a panel data - fixed effect regression model with approximately 15,000 data observations before data cleansing. However, for one of the variables (an independent variable with continuous data type), there are quite a few data points with a value of 0 (around 6,000 observations). These zero values are not missing values.

Can I still proceed with the analysis using panel data - fixed effect?

Are there any specific steps I should take to address this issue?

Thanks.

Note: I would greatly appreciate it if there is any reference literature discussing this issue

I am doing my graduate thesis for my master degree. In my research, I tried to use staggered difference-in-differences model to estimate the treatment effect. My explained variable is the number of patents. Without adding time-fixed effect, the symbol and significance of core explanatory variable fits my hypothesis and symbol of other explanatory variables also fit my hypothesis. But when the time-fixed effect are added, the coefficient of core explanatory variable become insignificant(significant at 10% significance but insignificant at 5% significance).

I have searched a lot of literature. I guess the reason is that the number of patents is a strong time-varying variable(In China, the number of patent is growing rapidly these years). When the time-fixed effect is added into the model, the value of coefficient decreased sharply and that cause the result because the standard error nearly doesn't change.

I have find some solutions. Firstly, I can use poisson regression or negative binomial regression because number of patent can be described as count variable. But can the panel data poisson regression be used in staggered difference-in-difference model? Secondly, I can use the number of patents directly instead of using the logarithm. But is there any literature support this way? Thirdly, maybe I can use single way fixed effect?

Which way is better? I am waiting for reply, thanks.

How can i use data from large-scale manufacturing firms to calculate

**how much of the quasi-rents in the firm are distributed to labor and how much to capital**? The firm panel data does not include human capital information but has wage and other financial information.I would like to construct principal components from 30+ financial ratios for a predictive model. I would then use logistic regression and support vector machines for the predictions, conditioned on the principal components. I have panel data with T << N. For PCA, the data should be iid. I am concerned that the time series are not independent. I have only 10 years of data, which precludes most time series statistical testing. I have seen several peer-reviewed papers constructing principal components using panel data, but the potential problems with using panel data for PCA are not discussed in the papers. The papers all seem to use the standard PCA approach one would normally use with a cross-section but with panel data. I have researched several means of doing a PCA with time series and a couple that use panel data as one of several examples of a general (usually very complicated) PCA procedure, but nothing seems to "fit the bill" for my standard panel dataset. I would greatly appreciate some direction as to where I might go to look for an adequate procedure or suggestions for a procedure that could possibly work. I am an applied econometrician, and it would be difficult for me to translate a complex procedure into code. So, ideally, I would like to find a procedure for which there is existing code (I use SAS and R). Thanks in advance for any insights provided,

. xtabond2 IndicedeTheil PIBparhabitantUSconstants InflationdéflateurduPIBa ITC Créditintérieurfourniausecte, gmm( IndicedeThei

> l, lag(2 4)collapse) iv( PIBparhabitantUSconstants InflationdéflateurduPIBa ITC Créditintérieurfourniausecte) twostep small n

> odiffsargan

Favoring space over speed. To switch, type or click on mata: mata set matafavor speed, perm.

Dynamic panel-data estimation, two-step system GMM

------------------------------------------------------------------------------

Group variable: codepays Number of obs = 171

Time variable : Année Number of groups = 10

Number of instruments = 9 Obs per group: min = 12

F(4, 9) = 559.85 avg = 17.10

Prob > F = 0.000 max = 20

----------------------------------------------------------------------------------------------

IndicedeTheil | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-----------------------------+----------------------------------------------------------------

PIBparhabitantUSconstants | .0000781 .0000141 5.53 0.000 .0000461 .00011

InflationdéflateurduPIBa | -.0137815 .0021388 -6.44 0.000 -.0186197 -.0089432

ITC | .0045328 .0029732 1.52 0.162 -.002193 .0112587

Créditintérieurfourniausecte | -.0241084 .0106227 -2.27 0.049 -.0481386 -.0000781

_cons | 3.522798 .8558666 4.12 0.003 1.586694 5.458903

----------------------------------------------------------------------------------------------

Warning: Uncorrected two-step standard errors are unreliable.

Instruments for first differences equation

Standard

D.(PIBparhabitantUSconstants InflationdéflateurduPIBa ITC

Créditintérieurfourniausecte)

GMM-type (missing=0, separate instruments for each period unless collapsed)

L(2/4).IndicedeTheil collapsed

Instruments for levels equation

Standard

PIBparhabitantUSconstants InflationdéflateurduPIBa ITC

Créditintérieurfourniausecte

_cons

GMM-type (missing=0, separate instruments for each period unless collapsed)

DL.IndicedeTheil collapsed

------------------------------------------------------------------------------

Arellano-Bond test for AR(1) in first differences: z = -0.35 Pr > z = 0.724

Arellano-Bond test for AR(2) in first differences: z = -0.68 Pr > z = 0.498

------------------------------------------------------------------------------

Sargan test of overid. restrictions: chi2(4) = 141.46 Prob > chi2 = 0.000

(Not robust, but not weakened by many instruments.)

Hansen test of overid. restrictions: chi2(4) = 5.04 Prob > chi2 = 0.283

(Robust, but weakened by many instruments.)

I am conducting a study with three energy sectors over 15 years of timeline. I intend to perform a panel data regressions.

I wanted to confirm if there is any rule which says that I cannot take more than three regressors or interdependent variables in the panel model equation because my cross-section observation number is only three (three energy sectors).

**I am running panel data analysis. How can I run**

**auto-correlation test of Wooldridge for panel data in Stata?**

My dependent variable consists of cross-sectional data with 8 observations, while the independent variables consist of time series data with 45 observations.

Hi. I have a panel data (daily) for 4 years and i want to split it into train and test sets. Many paper said: 80:20, some 70:30. Is there a specific criteria ? any study ? thank you

Hello, I am using panel data with N= 37 (countries) and T= 31 (1990 to 2020). After reading about literature of dynamic panel methods, I found that when T is small and N is large, it's better to use System GMM. And with large T and small N it's better to use PMG-ARDL
My questions are how small should be T and N to choose between the two methods ? And in my case (N= 37 ; T= 31) which method is more suitable, GMM or PMG ?
NB : Using the PMG, I found more significant results than GMM, in short run.
Thank you

Recently I collected a global dataset with 700 observations but strongly unbalanced. And I need to figure out the nonlinear relationship between variables. I tried panel threshold and quantile regression but it all requires balanced data, and I don't think simply two-way FE is enough to explain the nexus. Could anyone give me some advice on analyzing unbalanced datasets?

Hello, community! Thank you for clicking on my question. I am working on a paper about social disorganization (structural factors) and domestic violence in the US from a county-level perspective. The topic is not new but the unique point is that the DV data (crisis calls, shelter seekers, ...) used in the research was collected by myself from annual reports of DV agencies. However, after looking into the literature, I am growing more and more concerned about my way of investigating this topic in the following aspects:

Firstly, the dataset turns out to be rather small and low-quality. I only got no more than 1000 samples during 2016-2021 after I manually went through more than 2000 DV organizations' websites, filtered those with annual report data, and extract data. Meanwhile, my panel data is not balanced as some DV agencies published annual reports from 2016 to 2021 while some reveal non or little. Can anyone tell me if it is still OK?

Secondly, the county level seems too large for social disorganization theory. As each agency's services' geographical scope is not certain, I think a county-level view is appropriate and the county-level census data is available. However, when I looked more into the social disorganization theory, it is more related to the community/neighborhood context. I am no longer sure if my research is valid.

Thirdly, the capacity of my dataset to reflect the real DV level. Although the police station's DV data can neither reflect the real DV level, I found that the DV data from DV agencies is highly related to its funding. So I divided the DV data by funding. Not sure if it can be better.

Fourthly, the variable standing for social ties hasn't been found. There should be an intermediary factor, which is social ties, between socioeconomic factors and DV occurrence. However, based on my data-collecting method, I couldn't find one variable standing for social ties.

I would like to know how do you think about it! Before wrapping up my question, I would like to talk about my background. I worked for an Asian women's aid agency during my graduate studies, where I grew familiar with the services provided by this kind of agency. This is the reason why I was reminded of the DV data directed from the DV agencies. So my research actually starts from the availability of data (actually I collected it for months) instead of from a certain question. I would also appreciate it a lot if you find any other value of this dataset collected from the DV agencies.

Dear all

I have a set of balance panel data, i:6, t: 21 which is it overall 126 observation. I decided that 1 dependent variable (y) and 6 independents variables (x1,x2......).

First: I do unit root test it shows:

y I(I)

x1 I(0)

x2 I(I)

x3 I(I)

x4 I(0)

X5 I(I)

x6 I(0)

If I would like to run panel data regression (Pooled, Fixed Effect and Random Effect), is that the correct form for inputting the model in Views:

d(y) c x1 d(x2) d(x3) x4 d(x5) x6

or

Shall I sort all variables in the same difference level, adding "d" to all ?

please correct if I am wrong, these are the steps I would like to conduct the statical part of a panel data:

1. Test Unit Root

2. Panel Regression?

3. ARDL

My dependent variable consists of cross-sectional data with 8 observations, while the independent variables consist of time series data with 45 observations.

I have a panel data set and one variable called pain level (which has 4 categories: no, slight, moderate and severe). If I directly write the code of "tab painlevel", Stata will tell me the N (overall obs.) in these 4 categroies. However, if I want to know n (group obs.) in each pain level, what code should I choose?

Thanks!

Is there any ideal size of observation?

Hi, I am using a set of panel data for my research paper and i was wondering if random effects model goes further into the generalized method of moments or if they are two separate analyses.

Thank you in advance!

For example some of the variables in the model are stationary at level and others are not. how do we deal with that.

In carrying out panel data regression analysis, it is required that Hausman Specification Test be carried out to choose from Fixed Effect or Random Effect estimation approaches. Another theory holds that Breusch-Pagan Lagrange multiplier (LM) test for panel data is also required to choose between Random Effect estimation and Pooled Effect Estimation.

Which of the preliminary tests should come first? Are these tests the final determinants of which estimation approach to deploy?

I am examining trade differential and CO2 emissions in SSA. I have T=42years and N=37countries. The model is suffering from cross sectional dependence and heteroskedasticity, which may render the fixed effect estimator biased and inconsistent. Which method is suitable? Thanks advance for your humble suggestions.

Please can someone recommend the most appropriate SEM software for handling Panel data and at the same time moderation and mediation situation with the antecedent relevant diagnostic test and/or robustness check required for time series data. User friendliness for a beginner can equally be taken into consideration.

Can I use SEM when I have data for only one country (so it is time series data with more than one variable), as it is not panel data now? If I can use it, do I need to take care of something special that is not in the panel data?

Dear all,

I have panel data with time invariant variable.

look at statistic for firststage the R Square Partial appear too low. There are any Threshold for Partial R Square...

Someone can help:

First-stage regression summary statistics

--------------------------------------------------------------------------

| Adjusted Partial

Variable | R-sq. R-sq. R-sq. F(2,746) Prob > F

-------------+------------------------------------------------------------

TURN_1 | 0.1417 0.1233 0.0713 28.6487 0.0000

--------------------------------------------------------------------------

Shea's partial R-squared

--------------------------------------------------

| Shea's Shea's

Variable | partial R-sq. adj. partial R-sq.

-------------+------------------------------------

TURN_1 | 0.0713 0.0527

--------------------------------------------------

Minimum eigenvalue statistic = 28.6487

Critical Values # of endogenous regressors: 1

H0: Instruments are weak # of excluded instruments: 2

---------------------------------------------------------------------

| 5% 10% 20% 30%

2SLS relative bias | (not available)

-----------------------------------+---------------------------------

| 10% 15% 20% 25%

2SLS size of nominal 5% Wald test | 19.93 11.59 8.75 7.25

LIML size of nominal 5% Wald test | 8.68 5.33 4.42 3.92

---------------------------------------------------------------------

estat overid

Tests of overidentifying restrictions:

Sargan (score) chi2(1) = 1.08549 (p = 0.2975)

Basmann chi2(1) = 1.06282 (p = 0.3026)

Best regards

I am working on my thesis about the Environmental Kuznets Curve for Latin America and Southeast Asia, with a pannel data with fixed and random effects. I am using logarithmics and robust standard errors. I would to assess the difference in the effects between the two regions.

I obtain the results attached in the pictures. I do not understand why the cubic terms lose all their significance when adding the Latin America regional dummy and interacting it with the GDPpc variables. In the first regression (the one without regional variables), the cubic term makes all the variables significant, and in the second regression is the opposite. How does this make sense?

Depedendent variable: ln of CO2 per capita

Thanks in advance.

I am estimating female labour participation rates using panel data for 7 countries. I have data period from 1991 to 2021. I have reviewed the literature, and it suggests using GMM only when you have larger Ns and small Ts. Can you please help in this regard that which advanced or dynamic panel model be used?

Since OLS and Fixed effect estimation varies, for a fixed effect panel data model estimated using a fixed effects (within) regression what assumptions, for example no heteroskedasticity, linearity, do I need to test for, before I can run the regression.

I'm using the and xtreg,fe and xtscc,fe commands on stata.

I am working with a panel data set of purchases over week.

Therefore, a poission distrubition is suitable.

When there is overdispersion, a negative binomal regression is adviced.

I am combing these distrubition with a random intecept model with multiple predictors (glmer). When i test for overdispersion, there is no need to use negative binomal

However, when i look at the assumptions of poission it assumes indepence of datapoints, which is not the case in panel data.

Is this assumption also implemented within negative binomnal? Or can I just use poission and ignore the indepence assumption as i have panel data?

I would greatly appreciate it if you could recommend measurement methods for assessing two-way causality in panel data. If there are any corresponding software packages or specific steps for conducting such analyses, it would be highly appreciated.

I am trying to prepare a panel dataset based on 4 different rounds of the NSSO enterprise survey datasets. I have appended the 4 different datasets having exactly the same variables in the same order using STATA. Since the data provided by the NSSO is based on sample survey wherein the same sample may not have been surveyed the next year and the sample size also varies from one year to the other. Therefore I want to make a repeated cross-section data using the NIC codes given against each sample enterprise given for each of the 4 years. Now I generated a new variable in the appended dataset called 'nic' which is intended to act as the panel variable using the following command:

egen nic=group(NIC)

When I apply the following command to set the data as panel using the panel variable 'nic', I get an error message:

xtset nic Year, yearly

The error message says: "repeated time values within panel

r(451)".

Kindly help.

The population is 233 firms, I use purposive sampling to get the sample size of 100 firms. Data is collected for 3 years so there are 300 observations in total. Is it enough to have significant result?

Autocorrelation, Hetroscedastcity and the normality of residuals are being considered as the important diagnostics to examine the credibility of model. If Autocorrelation is examining by DW so up to which level the DW will be sufficient for reliable results?

What measure will used for hetroscedasticity in panel regression?

Hi, I am currently writing my master thesis on early indicators of bank failure. For this I have calculated probability of default as my dependent variable and have around 15 different financial ratios as explanatory variables, I have also included two measures of interest sensitivity as possible independent variables.

My data consists of 125 companies over 20 years. I'm using STATA and need help with how I should format my data from excel. I'm also unsure of which kind of regression is best suited for my data. I've tried reading

**Econometric Analysis of Cross Section and Panel Data by Woolridge but I'm feeling a bit lost.**Attached is the Excel file.

Thank You for the help!