Stata Journal
1536-867X
1536-8734
Đức
Cơ quản chủ quản: SAGE Publications Inc. , DPC Nederland
Lĩnh vực:
Mathematics (miscellaneous)
Các bài báo tiêu biểu
How to do Xtabond2: An Introduction to Difference and System GMM in Stata The difference and system generalized method-of-moments estimators, developed by Holtz-Eakin, Newey, and Rosen (1988, Econometrica 56: 1371–1395); Arellano and Bond (1991, Review of Economic Studies 58: 277–297); Arellano and Bover (1995, Journal of Econometrics 68: 29–51); and Blundell and Bond (1998, Journal of Econometrics 87: 115–143), are increasingly popular. Both are general estimators designed for situations with “small T, large N″ panels, meaning few time periods and many individuals; independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; fixed effects; and heteroskedasticity and autocorrelation within individuals. This pedagogic article first introduces linear generalized method of moments. Then it describes how limited time span and potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it describes how to apply these estimators with xtabond2. It also explains how to perform the Arellano–Bond test for autocorrelation in a panel after other Stata commands, using abar. The article concludes with some tips for proper use.
Tập 9 Số 1 - Trang 86-136 - 2009
Estimation of Average Treatment Effects Based on Propensity Scores In this paper, we give a short overview of some propensity score matching estimators suggested in the evaluation literature, and we provide a set of Stata programs, which we illustrate using the National Supported Work (NSW) demonstration widely known in labor economics.
Tập 2 Số 4 - Trang 358-377 - 2002
Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence I present a new Stata program, xtscc, that estimates pooled ordinary least-squares/weighted least-squares regression and fixed-effects (within) regression models with Driscoll and Kraay (Review of Economics and Statistics 80: 549–560) standard errors. By running Monte Carlo simulations, I compare the finite-sample properties of the cross-sectional dependence–consistent Driscoll–Kraay estimator with the properties of other, more commonly used covariance matrix estimators that do not account for cross-sectional dependence. The results indicate that Driscoll–Kraay standard errors are well calibrated when cross-sectional dependence is present. However, erroneously ignoring cross-sectional correlation in the estimation of panel models can lead to severely biased statistical results. I illustrate the xtscc program by considering an application from empirical finance. Thereby, I also propose a Hausman-type test for fixed effects that is robust to general forms of cross-sectional and temporal dependence.
Tập 7 Số 3 - Trang 281-312 - 2007
Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects Many researchers and journals place a strong emphasis on the sign and statistical significance of effects—but often there is very little emphasis on the substantive and practical significance of the findings. As Long and Freese (2006, Regression Models for Categorical Dependent Variables Using Stata [Stata Press]) show, results can often be made more tangible by computing predicted or expected values for hypothetical or prototypical cases. Stata 11 introduced new tools for making such calculations—factor variables and the margins command. These can do most of the things that were previously done by Stata's own adjust and mfx commands, and much more. Unfortunately, the complexity of the margins syntax, the daunting 50-page reference manual entry that describes it, and a lack of understanding about what margins offers over older commands that have been widely used for years may have dissuaded some researchers from examining how the margins command could benefit them. In this article, therefore, I explain what adjusted predictions and marginal effects are, and how they can contribute to the interpretation of results. I further explain why older commands, like adjust and mfx, can often produce incorrect results, and how factor variables and the margins command can avoid these errors. The relative merits of different methods for setting representative values for variables in the model (marginal effects at the means, average marginal effects, and marginal effects at representative values) are considered. I shows how the marginsplot command (introduced in Stata 12) provides a graphical and often much easier means for presenting and understanding the results from margins, and explain why margins does not present marginal effects for interaction terms.
Tập 12 Số 2 - Trang 308-331 - 2012
Enhanced Routines for Instrumental Variables/Generalized Method of Moments Estimation and Testing We extend our 2003 paper on instrumental variables and generalized method of moments estimation, and we test and describe enhanced routines that address heteroskedasticity- and autocorrelation-consistent standard errors, weak instruments, limited-information maximum likelihood and k-class estimation, tests for endogeneity and Ramsey's regression specification-error test, and autocorrelation tests for instrumental variable estimates and panel-data instrumental variable estimates.
Tập 7 Số 4 - Trang 465-506 - 2007
Fitting Mixed Logit Models by Using Maximum Simulated Likelihood This article describes the mixlogit Stata command for fitting mixed logit models by using maximum simulated likelihood.
Tập 7 Số 3 - Trang 388-401 - 2007
Estimation of Nonstationary Heterogeneous Panels We introduce a new Stata command, xtpmg, for estimating nonstationary heterogeneous panels in which the number of groups and number of time-series observations are both large. Based on recent advances in the nonstationary panel literature, xtpmg provides three alternative estimators: a traditional fixed-effects estimator, the mean-group estimator of Pesaran and Smith (Estimating long-run relationships from dynamic heterogeneous panels, Journal of Econometrics 68: 79–113), and the pooled mean-group estimator of Pesaran, Shin, and Smith (Estimating long-run relationships in dynamic heterogeneous panels, DAE Working Papers Amalgamated Series 9721; Pooled mean group estimation of dynamic heterogeneous panels, Journal of the American Statistical Association 94: 621–634).
Tập 7 Số 2 - Trang 197-208 - 2007
Error-Correction–Based Cointegration Tests for Panel Data This article describes a new Stata command called xtwest, which implements the four error-correction–based panel cointegration tests developed by Westerlund (2007). The tests are general enough to allow for a large degree of heterogeneity, both in the long-run cointegrating relationship and in the short-run dynamics, and dependence within as well as across the cross-sectional units.
Tập 8 Số 2 - Trang 232-241 - 2008
Nonparametric Pairwise Multiple Comparisons in Independent Groups using Dunn's Test Dunn's test is the appropriate nonparametric pairwise multiple-comparison procedure when a Kruskal–Wallis test is rejected, and it is now implemented for Stata in the dunntest command. dunntest produces multiple comparisons following a Kruskal–Wallis k-way test by using Stata's built-in kwallis command. It includes options to control the familywise error rate by using Dunn's proposed Bonferroni adjustment, the Šidák adjustment, the Holm stepwise adjustment, or the Holm–Šidák stepwise adjustment. There is also an option to control the false discovery rate using the Benjamini–Hochberg stepwise adjustment.
Tập 15 Số 1 - Trang 292-300 - 2015
Goodness-of-fit Test for a Logistic Regression Model Fitted using Survey Sample Data After a logistic regression model has been fitted, a global test of goodness of fit of the resulting model should be performed. A test that is commonly used to assess model fit is the Hosmer–Lemeshow test, which is available in Stata and most other statistical software programs. However, it is often of interest to fit a logistic regression model to sample survey data, such as data from the National Health Interview Survey or the National Health and Nutrition Examination Survey. Unfortunately, for such situations no goodness-of-fit testing procedures have been developed or implemented in available software. To address this problem, a Stata ado-command, svylogitgof, for estimating the F-adjusted mean residual test after svy: logit or svy: logistic estimation has been developed, and this paper describes its implementation.
Tập 6 Số 1 - Trang 97-105 - 2006