2005
DOI: 10.1177/1536867x0500500205
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Estimation and Testing of Fixed-effect Panel-data Systems

Abstract: This paper describes how to specify, estimate, and test multiple-equation, fixed-effect, panel-data equations in Stata. By specifying the system of equations as seemingly unrelated regressions, Stata panel-data procedures worked seamlessly for estimation and testing of individual variable coefficients, but additional routines using test were needed for testing of individual equations and differences between equations.

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Cited by 87 publications
(57 citation statements)
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“…The diagnostics tests confirm that even though we use fixed effect estimates, the problems of heteroscedasticity and first‐order autocorrelation would restrict robust estimates. Therefore, we use panel‐corrected standard error (PCSE) estimates (Blackwell ) for linear panel data models where the parameters are estimated by Prais–Winsten regression (to control autocorrelation), and disturbances are assumed to be only panel‐level heteroscedastic (robust coefficients are obtained) with no contemporaneous correlation across panels. The results of PCSE estimates with heteroscedasticity‐corrected standard errors are given in Models 4 and 8, respectively, in Table .…”
Section: Major Findingsmentioning
confidence: 99%
“…The diagnostics tests confirm that even though we use fixed effect estimates, the problems of heteroscedasticity and first‐order autocorrelation would restrict robust estimates. Therefore, we use panel‐corrected standard error (PCSE) estimates (Blackwell ) for linear panel data models where the parameters are estimated by Prais–Winsten regression (to control autocorrelation), and disturbances are assumed to be only panel‐level heteroscedastic (robust coefficients are obtained) with no contemporaneous correlation across panels. The results of PCSE estimates with heteroscedasticity‐corrected standard errors are given in Models 4 and 8, respectively, in Table .…”
Section: Major Findingsmentioning
confidence: 99%
“…Including exports as a ratio of domestic sales was the method employed to account for the cyclical nature of the cattle industry. 4 An alternative would be to employ the multiple-equation, fixed effects panel data method suggested by Blackwell (2005); however, this approach only estimates a system of linear seemingly unrelated regression and does not address simultaneity problems so this approach was abandoned. Lagrange multiplier tests for heteroskedasticity confirmed a problem for each of the equations.…”
Section: Modelmentioning
confidence: 99%
“…In all regressions, we correct for serial correlation in FPI using the Prais‐Winsten () method for panel data. The approach, which follows Bekaert et al (, footnote 13), uses a Prais‐Winsten regression to account for panel‐specific first‐order serial correlation and panel‐corrected standard errors to account for heteroskedasticity and contemporaneous residual correlation across panels (Beck and Katz (), Wooldridge (), Blackwell ()). Clustering in this fixed effects model is not the appropriate solution because the model does not have sufficient “effective” degrees of freedom (i.e., the number of clusters will be less than the number of coefficients).…”
mentioning
confidence: 99%