Stata Journal

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.

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.

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.

Bài báo này giải thích lý do tại sao việc tính toán hiệu ứng giới hạn của sự thay đổi trong hai biến trở nên phức tạp hơn trong các mô hình phi tuyến so với các mô hình tuyến tính. Lệnh inteff tính toán hiệu ứng giới hạn chính xác của sự thay đổi trong hai biến tương tác cho mô hình logit hoặc probit, cũng như các sai số chuẩn chính xác. Lệnh inteff vẽ đồ thị hiệu ứng tương tác và lưu kết quả để cho phép điều tra thêm.

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.

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.

Bài báo này mô tả lệnh mixlogit trong Stata để khớp các mô hình logit hỗn hợp bằng cách sử dụng ước lượng tối đa thông qua mô phỏng.

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).

Bài báo này mô tả một lệnh Stata mới gọi là xtwest, thực hiện bốn thử nghiệm đồng hội tụ dựa trên sửa lỗi do Westerlund (2007) phát triển. Các thử nghiệm này đủ tổng quát để cho phép một mức độ lớn của sự không đồng nhất, cả trong mối quan hệ đồng hội tụ dài hạn và trong động lực ngắn hạn, cũng như sự phụ thuộc trong và giữa các đơn vị cắt ngang.

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.