By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have been using " plm " package of R to do the analysis of panel data. One of the important test in this package for choosing between "fixed effect" or "random effect" model is called Hausman type.

A similar test is also available for the Stata. The point here is that Stata requires fixed effect to be estimated first followed by random effect. However, I didn't see any such restriction in the "plm" package. So, I was wondering whether " plm " package has the default "fixed effect" first and then "random effect" second. For your reference, I mention below the steps in Stata and R that I followed for the analysis.

As you can see, the test yields the same result no matter which of the models you feed it as the first and which as the second argument. Learn more. Hausman type test in R Ask Question. Asked 7 years, 5 months ago. Active 5 years, 7 months ago. Viewed 14k times. Metrics Metrics RoyalTS seems to have answered your question. Do you really want to use the test, though? It's not the most reliable indicator of whether to use FE or RE ref. Oct 21 '12 at Thanks for the paper.

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However, we still have robust hausman test xtoverid and Wooldridge in stata. The paper you mentioned didn't talk about these tests. I am not sure about these tests in plm package of R. Active Oldest Votes.Login or Register Log in with.

## RHAUSMAN: Stata module to perform Robust Hausman Specification Test

Forums FAQ. Search in titles only. Posts Latest Activity. Page of 1. Filtered by:. Dan Su. Difference between robust and non-robust? Hi statisticians! Can anyone explain to me when we should use the robust option when running what kind of models? In which case, the robust and nonrobust standard errors will not change much? Thanks so much! I know in SAS we have the empirical option, dose anyone know which option or package we have in R to get the robust results? Thanks a ton! Tags: None.

Clyde Schechter.

### st: RE: robust Hausman test, xtpcse

The robust variance estimator is robust to heteroscedasticity. It should be used when heteroscedasticity is, or is likely to be, present. In some commands, -xtreg, fe- and -xtpoisson, fe- come to mind, there may be others I'm not thinking of off the top of my headspecifying -vce robust - leads to the cluster robust variance estimator. This one, in addition to being robust to heteroscedasticity is also robust to correlation of errors within the specified clusters the panel variable when invoked automatically by the command itself and serial correlation.

It should be used when these are present or suspected, and when the number of clusters is large enough for it to be valid. As a practical matter, most real world panel data has these problems, and it is easier to pre-emptively deal with them by specifing -robust- than it is to try to test for their presence. So the -vce cluster robust - is generally a good idea in any panel data analysis with a sufficient number of clusters. There is no universal agreement about the minimum number of cluster needed.

I have seen rules of thumb suggesting a minimum of 10, or a minimum of 25, or of 50 in order for the cluster robust variance estimator to actually be an improvement over the ordinary variance estimator. I do not use R and cannot answer the second question.

But there are others on the Forum who use both Stata and R and might respond to that. Last edited by Clyde Schechter ; 22 Aug Comment Post Cancel. Enrique Pinzon StataCorp. Thanks, Enrique. That was very informative. I wasn't aware of that.

Thank you everyone!!! It's really helpful!!! Philip Gigliotti. I personally always use cluster robust options when analyzing panel data.

This is standard in my field econ and public policy. While I understand that it may be technically permissible in some situations to not use these commands, reviewers would always question it, and I've never seen a paper that tried to make an excuse for not using it. Previous Next.Login or Register Log in with. Forums FAQ.

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Posts Latest Activity. Page of 2. Filtered by:. Priya Sharma. Robust Hausman Test 04 Sep Should I use the command 'xtoverid' or the command 'rhausman'? Tags: hausmen testpanel datastata.

Carlo Lazzaro. Priya: welcome to this forum. If you need to run Robust Hausman Test you should consider the community-contributed command -rhausman. Comment Post Cancel. Hello Mr. Carlo, thank you for your response. I am not sure what you mean by 'community-contributed command' I have attached the command I ran, would that be correct?

Additionally, why can't I use the 'xtoverid' command? Priya: community-contributed commands are created by Statalisters -rhausman- was created by Boris Kaiser for their research purposes and are then made downloadable by other interested listers.

Put differently, they are not Stata built-in commands. That said, the results of -rhausman- confirm that -re- is the way to go with your data.

### Robust Regression | Stata Data Analysis Examples

On a second thought, you could also have used -xtoverid- although it works as a test of overidentifying restrictions, whereas -rhausman- calls -bootstrap. Last edited by Carlo Lazzaro ; 04 Sep Thank you for your response Mr. This has solved a major problem of mine. Is this a problem as my R2 is extremely low. Priya: I would run a -xttest0- to check whether there's evidence of -re.

Priya: I'm not sure I got you right. Sorry about the confusion. According to my p-value of 0.Search everywhere only in this topic.

Advanced Search. Classic List Threaded. Marco Buur. Am i right? Re: st: hausman test. The positive definite error is concerning. I would suggest using xtoverid as an alternative test. Schaffer, M. Schaffer, Mark E. RE: st: hausman test. Marco, -hausman- isn't valid with -robust- see Hayashi, "Econometric",p. Steve's advice to use -xtoverid- is the right way to go. In reply to this post by Marco Buur. Martin Weiss AW: st: hausman test.

Through the usual -ssc inst ivreg If so, you should get the latest version. What does -which ivreg2- give you? Siyam, Amani. Dear Stata-listers, We have designed a short questionnaire using Acrobat 7 and the only option we have for the respondents to email their entries is via an xml data file. I tried using xmluse but it does not seem to recognize the "doctype". Many thanks for your help in advance. Baum, M. Schaffer, and S. After that, -which- again and see whether your output matches mine Thanks it works!!!

August An: [hidden email] Betreff: Re: st: hausman test Thanks it works!!! You need to install the program. Search everywhere only in this topic Advanced Search st: hausman test. In reply to this post by Marco Buur Thanks it works!!! In reply to this post by Marco Buur You need to install the program.

Free forum by Nabble. Edit this page.To browse Academia. Skip to main content. Log In Sign Up. McRey Banderlipe II. Introduction Panel datasets usually encounters problems when a dependent variable is observed to exhibit heterogeneity beyond what can be explained by the variation in independent variables. As a consequence, failure to account for these unit effects would result in poorly fit models with the independent variables showing biased estimates.

In determining which of these two models would apply in analyzing panel datasets, the commonly used specification test Hausman, is used. This test is intended to assess how parameter estimates differ across the methods, based on the understanding of the trade-off between bias and variance in the two estimators. For one, a REM can introduce bias but reduce the variance of estimates of coefficients, while a FEM model remain unbiased but has a high degree of variance Clark and Linzer, Moreover, rejection of the null hypothesis that FEM and REM estimators do not differ substantially means that the error term under REM is probably correlated with one or more regressors Gujarati and Porter, The paper of Clark and Linzer questions which of the two unobserved effects estimation is much more appropriate and the Hausman test was identified to associate with the random effects since problems of estimation involves causation.

This paper seeks a take-off from the work of Clark and Linzer by using a more robust Hausman test proposed to show that the test statistic is closely associated with random effects. This will be verified by means of comparison between traditional and robust Hausman tests, as well as the Monte Carlo simulation of the robust version of this test.

Revisiting traditional vs. The fixed effects FE allow for correlation between the unobserved effects or heterogeneity and the explanatory variables of interest while random effects RE do not. Failure to meet the second requirement implies that the resulting test would have an asymptotic size smaller or larger than the nominal size of the test. Monte Carlo simulation and results In this paper, the data generating process involves constructing a balanced panel data with 1, simulated observations for each of the 10 hypothetical sampling units and yearly observations for 10 years, resulting to a total of 10, observations.

We employed two variables X1 and X2 for this purpose and the observations were obtained from a normal sampling distribution.

Two major tests were conducted. First, we compared the results of a simulation using the traditional Hausman test and the robust Hausman test using Stata. The commands used are shown in Appendix A. Under strict exogeneity, the GLS procedure was performed and from the results we used the p- values to test whether or not to reject the null hypothesis that random effects is better than the fixed effects in terms of accounting for the significance of the relationship between the unique errors and the regressors.

The findings Appendix B and C show that the p-values under the robust and tradition Hausman tests are 0. In the Monte Carlo analysis, we performed simulations for1, and 10, repetitions to confirm the power of the statistic to accept or reject the null hypothesis that the error correction model or REM is appropriate and that there is no need to rely on statistical inferences that are conditional to the individual error components Gujarati and Porter, Appendix D shows the generic command used.

The results in Appendix E are consistent for all the simulations, such that the p-values of 0. Conclusion Towards the end, this note concludes that the use of robust Hausman test leads to a stronger association with the use of random effects models in panel data analysis of linear regression models. While same results co-exist with the traditional test, the robustness of the standard errors increases the power of the exogenous variables to relate with the endogenous variable of interest.

While it focused on linear models, there are still questions as to how usable the robust model is when applied to a non-linear function of explanatory variables, or there exists another noise in the measurement of these variables. Consistent with Clark and Linzerthe results of the Hausman test should not be used solely for deciding whether FEM or REM estimation is suitable in an econometric model.

In closing, Gujarati and Porter advises the reader to keep in mind that while panel data sought to overcome the limitations of cross-sectional data analysis, it should not maintain itself as the ultimate cure to solve problems in econometric models.

References Clark, T. Should I use fixed or random effects? Unpublished manuscript. Atlanta, GA: Emory University.Unlike the latter, the Mundlak approach may be used when the errors are heteroskedastic or have intragroup correlation. Say I want to fit a linear panel-data model and need to decide whether to use a random-effects or fixed-effects estimator. My decision depends on how time-invariant unobservable variables are related to variables in my model.

Here are two examples that may yield different answers:. In the first case, innate ability can affect observable characteristics such as the amount of schooling someone pursues. In the second case, geographic characteristics are probably not correlated with the variables in our model. Of course, these are conjectures, and we want a test to verify if unobservables are related to the variables in our model. First, I will tell you how to compute the test; then, I will explain the theory and intuition behind it.

If you reject that the coefficients are jointly zero, the test suggests that there is correlation between the time-invariant unobservables and your regressors, namely, the fixed-effects assumptions are satisfied. If you cannot reject the null that the generated regressors are zero, there is evidence of no correlation between the time-invariant unobservable and your regressors; that is, the random effects assumptions are satisfied. Below I demonstrate the three-step procedure above using simulated data.

The data satisfy the fixed-effects assumptions and have two time-varying covariates and one time-invariant covariate. We reject the null hypothesis. This suggests that time-invariant unobservables are related to our regressors and that the fixed-effects model is appropriate. Note that I used a robust estimator of the variance-covariance matrix. I could not have done this if I had used a Hausman test.

We know how to think about this problem from our regression intuition. This is what we test. The implied model is given by. The third equality relies on the fact that the regressors and unobservables are mean independent. The test is given by.

Mundlak, Y. Econometrica Home About. Categories: Statistics Tags: fixed effecthausmanmundlakpanel datarandom effect. Programming an estimation command in Stata: Global macros versus local macros Programming an estimation command in Stata: Where to store your stuff.University of Bern. More about this item Keywords Hausman test ; cluster robust ; specification test ; fixed effects ; random effects ; instrumental variables ; Statistics Access and download statistics Corrections All material on this site has been provided by the respective publishers and authors.

You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:bocode:s See general information about how to correct material in RePEc. For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum.

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This command implements a cluster- robust version of the Hausman specification test using a bootstrap procedure. For example, this test can be used to compare random effects RE vs. Boris Kaiser, Handle: RePEc:boc:bocode:s Note: This module should be installed from within Stata by typing "ssc install rhausman".

Windows users should not attempt to download these files with a web browser. More about this item Keywords Hausman test ; cluster robust ; specification test ; fixed effects ; random effects ; instrumental variables ; Statistics Access and download statistics. Corrections All material on this site has been provided by the respective publishers and authors.

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