Microeconometrics using stata cameron trivedi pdf download

Has PDF. Publication Type. More Filters. View 3 excerpts, cites background and methods. The Stata Journal. View 1 excerpt, cites methods.

Since it is common among households to use more than one form of microinsurance, this paper estimates the uptake of different kinds of microinsurance by the same population.

We use a multivariate … Expand. View 1 excerpt, cites background. Demasking the impact of micro nance. We reconsider data from a randomized control trial study in India.

The data reveal the impact of a microloan program. Methods in empirical economics - a selective review with applications. This paper presents some selective aspects of standard econometric methods and of new developments in econometrics that are important for applications with microeconomic data.

The range includes … Expand. View 2 excerpts, cites background. Introduction to Stata. We thank the many people who have assisted us in preparing this book. Then data viewed as clustered on the individual unit. Many panel methods also apply to clustered data such as cross-section individual-level surveys clustered at the village level.

IV: use data from other periods as instruments. Source: Baltagi and Khanti-Akom Complication: education is time-invariant in these data. Need to use IV methods Hausman-Taylor. Data here are already in long form. Min Max exp Allows use of panel commands and some time series operators. Percent Cum. Pattern Min Max Observations lwage overall 6. For individual-invariant variable t the between variation is zero.

Percent Freq. Percent Percent 0 Can also estimate correlations without imposing stationarity. Instead use specialized panel commands after xtset: xtdescribe: extent to which panel is unbalanced xtsum: separate within over time and between over individuals variation xttab: tabulations within and between for discrete data e.

Interval] exp. Assumes more information content from data then is the case. Cluster-robust t-statistics are half as large as default. Typical result. Yields wrong standard errors if heteroskedasticity or if errors not equicorrelated over time for a given individual. For short panel can relax and use cluster-robust inference. Allows heteroskedasticity and general correlation over time for given i. Independence over i is still assumed. For xtreg use option vce robust does cluster-robust For some other xt commands use option vce cluster And for some other xt commands there is no option but may be able to do a cluster bootstrap.

Solution: do a panel bootstrap of the Hausman test or use the Wooldridge robust version of Hausman test. For long panels asymptotics are for T! Models with nonstationary errors currently active area : As yet no Stata commands Add-on levinlin does Levin-Lin-Chu panel unit root test Add-on ipshin does Im-Pesaran-Shin panel unit root test in heterogeneous panels Add-on xtpmg for does Pesaran-Smith and Pesaran-Shin-Smith estimation for nonstationary heterogeneous panels with both N and T large.

Growth curve of child height or IQ as grow older use previous models with xit polynomial in time or age. Richer dynamic model regresses yit on lags of yit. Arellano-Bond is a variation that uses unbalanced set of instruments with further lags as instruments. Example: xtmixed lwage exp exp2 wks ed jj id: exp wks, covar unstructured mle A.

Here clustering is on village not individual and the repeated measures are over individuals not time. Use xtset village id Assuming equicorrelated errors can be more reasonable here than with panel data where correlation dampens over time. So perhaps less need for vce cluster after xtreg A. Here we consider short panels throughout. This is same model as in cross-section case, with adjustment for correlation over time for a given individual. Then the unconditional density for the i th observation is f yit , Instead use numerical integration only univariate integration is required.

Use re option for xtlogit, xtprobit Use normal option for xtpoisson and xtnbreg A. Nonlinear generalization of xtmixed Then higher-dimensional numerical integral. Use adaptive Gaussian quadrature Stata commands are: xtmelogit for binary data xtmepoisson for counts Stata add-on that is very rich: gllamm generalized linear and latent mixed models Developed by Sophia Rabe-Hesketh and Anders Skrondal. No Stata command. Conclusion Stata provides commands for panel models and estimators commonly used in microeconometrics and biostatistics.

Stata also provides diagnostics and postestimation commands, not presented here. The emphasis is on short panels. Some commands provide cluster-robust standard errors, some do not. Extensions to nonlinear panel models exist, though FE models may not be estimable with short panels. This presentation draws on two chapters in Cameron and Trivedi, Microeconometrics using Stata, forthcoming.