What is xtgls in stata?
Description. xtgls fits panel-data linear models by using feasible generalized least squares. This command allows estimation in the presence of AR(1) autocorrelation within panels and cross-sectional correlation and heteroskedasticity across panels.
What does Xtreg mean in Stata?
-xtreg- is the basic panel estimation command in Stata, but it is very slow compared to taking out means. For example: xtset id xtreg y1 y2, fe runs about 5 seconds per million observations whereas the undocumented command.
What does Xtreg Fe do in Stata?
Stata’s xtreg random effects model is just a matrix weighted average of the fixed-effects (within) and the between-effects. In our example, because the within- and between-effects are orthogonal, thus the re produces the same results as the individual fe and be.
What is fgls in stata?
Feasible Generalized Least Squares (FGLS) and Instrumental Variable approach – Statalist.
What is FGLS regression?
Feasible generalized least squares (FGLS) estimates the coefficients of a multiple linear regression model and their covariance matrix in the presence of nonspherical innovations with an unknown covariance matrix.
What are Driscoll Kraay standard errors?
Driscoll-Kraay standard errors are robust to very general forms of cross-sectional (“spatial”) and temporal dependence when the time dimension becomes large. This nonparametric technique of estimating standard errors does not place any restrictions on the limiting behavior of the number of panels.
How do you read the Hausman test?
Test Results
Interpreting the result from a Hausman test is fairly straightforward: if the p-value is small (less than 0.05), reject the null hypothesis. The problem comes with the fact that many versions of the test — with different hypothesis and possible conclusions — exist.
What is two way fixed effects?
The two-way linear fixed effects regression ( 2FE ) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time.
How do you choose between fixed and random-effects?
The most important practical difference between the two is this: Random effects are estimated with partial pooling, while fixed effects are not. Partial pooling means that, if you have few data points in a group, the group’s effect estimate will be based partially on the more abundant data from other groups.
What is FGLS estimator?
What is the difference between OLS and GLS?
The real difference between OLS and GLS is the assumptions made about the error term of the model. In OLS we (at least in CLM setup) assume that Var(u)=σ2I, where I is the identity matrix – such that there are no off diagonal elements different from zero.
Is FGLS better than OLS?
For low and moderate amounts of serial correlation FGLS does significantly better than robust OLS.
Which is better OLS or GLS?
Whereas GLS is more efficient and consistent than OLS under heteroscedasticity (also spelled heteroskedasticity) or autocorrelation, this is not true for FGLS.
What is HAC test?
The estimator is used to try to overcome autocorrelation (also called serial correlation), and heteroskedasticity in the error terms in the models, often for regressions applied to time series data. The abbreviation “HAC,” sometimes used for the estimator, stands for “heteroskedasticity and autocorrelation consistent.”
How do you deal with cross-sectional dependence in panel data?
To deal with serial autocorrelation, hetroskedasticity and cross sectional dependence in panel data go for the Feasible Generalised Least Squares (FGLS) and the Panel Corrected Standard Error (PCSE). The former works well ifT>N, while the latter is feasible when N>T.
How do you choose between fixed and random effects Hausman?
1 Fixed or random. You can run a Hausman test (which tests whether the unique errors are correlated with the regressors, the null is they are not). If the p-value is significant, then you choose fixed effects (since the unique errors are correlated with the regressors).
How do you choose between random and fixed effects model?
What is two-way fixed effects regression?
Why the two-way fixed effects model is difficult to interpret?
Abstract. The two-way fixed effects (FE) model, an increasingly popular method for modeling time-series cross-section (TSCS) data, is substantively difficult to interpret because the model’s estimates are a complex amalgamation of variation in the over-time and cross-sectional effects.
Why is a random effect better than a fixed effect?
A fixed-effects model supports prediction about only the levels/categories of features used for training. A random-effects model, by contrast, allows predicting something about the population from which the sample is drawn.
What is the difference between fixed effect model and random effect model?
Under the fixed-effect model the only source of uncertainty is the within-study (sampling or estimation) error. Under the random-effects model there is this same source of uncertainty plus an additional source (between-studies variance).
Why is GLS better than OLS?
And the real reason, to choose, GLS over OLS is indeed to gain asymptotic efficiency (smaller variance for n →∞. It is important to know that the OLS estimates can be unbiased, even if the underlying (true) data generating process actually follows the GLS model. If GLS is unbiased then so is OLS (and vice versa).
How is OLS different from regression?
Yes, although ‘linear regression’ refers to any approach to model the relationship between one or more variables, OLS is the method used to find the simple linear regression of a set of data. Linear regression refers to any approach to model a LINEAR relationship between one or more variables.
Is FGLS efficient?
Interestingly note that FGLS is asymptotically efficient (among the class of linear unbiased estimators) even though we only require a consistent estimator of Ω, not necessarily an efficient one.
Why do we use GLS?
GLS is used when the modle suffering from heteroskedasticity. GLS is usefull for dealing whith both issues, heteroskedasticity and cross correlation, and as Georgios Savvakis pointed out it is a generalization of OLS. If you believe that the individual heterogeneity is random, you should use GLS instead of OLS.