Sargan-Hansen statistic 16.515 Chi-sq(4) P-value = 0. Test: Ho: difference in coefficients not systematicĬhi2(4) = (b1-b2)' * ^(-1) * (b1-b2)Ĭross-section time-series model: xtreg re robust cluster(companyID) 100ī1: obtained from xtreg sqrtROA sqrtSTD sqrtLTD SIZE GROWTH, fe cluster( companyID)ī2: obtained from xtreg sqrtROA sqrtSTD sqrtLTD SIZE GROWTH, re cluster( companyID) Thus, my question is which results should i trust and go for? After implementing the rhausman and xtoverid commands, i receive contradicting results. Therefore, i have opted for xtreg, fe/re cluster() and then in order to decide between them i used rhausman and xtoverid commands. Sargan-Hansen statistic 318.394 Chi-sq(2) P-value = 0.0000Īfter running Pooled OLS model i detected autocorrelation and heteroscedasticity violations. Test of overidentifying restrictions: fixed vs random effectsĬross-section time-series model: xtreg re robust cluster(idcode) 56323316 (fraction of variance due to u_i) adjusted for 4,699 clusters in idcode)Īge |. Group variable: idcode Number of groups = 4,699 Random-effects GLS regression Number of obs = 28,101 xtreg ln_wage c.age c.tenure, re vce(cluster idcode) 2) the evidence of a group-wise effect is enough to switch from pooled OLS to -xt- suite. 59743613 (fraction of variance due to u_i)īreusch and Pagan Lagrangian multiplier test for random effects Group variable: idcode Number of groups = 4,710Ĭorr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Random-effects GLS regression Number of obs = 28,510 Breusch-Pagan test basically uses the principle of calculating half of the explained sum of squares from auxiliary regression. Although the BP test was originally developed to deal with both individual and time effects, the version of the BP test to detect only the individual effects seems to have received the most attention. I want to mention that there is no heteroscedasticity problem, only the autocorrelation is the issue. The most well-known test for detecting unobserved heterogeneity is due to Breusch and Pagan (1980, BP test hereafter). ![]() xttest0 but it returns an error message last estimates not found r(301)Ģ) Which command/ procedure would be appropriate in order to exclude Pooled OLS? The null hypothesis is rejected, meaning FE is to be chosen.ġ) Am i doing everything correctly till now?Īn now i want also to exclude Pooled OLS by running the Breusch and Pagan Lagrangian multiplier test for random effects. xtregar, re) and then the default Hausman test. You can use breuschpagan from statsmodels, which takes OLS residuals and candidates for explanatory variables for the heteroscedasticity and so it does not rely on a specific model or implementation of a model. xtregar gives reliable estimates in the presence of AR1. I am deciding among Pooled OLS, Fixed and Random Effects panel models in the presence of first-order autocorrelation ( null hypothesis of Wooldridge test for autocorrelation in panel data is not rejected).
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