Unit-root tests for explosive behavior

We present a new command, radf, that tests for explosive behavior in time series. The command computes the right-tail augmented Dickey and Fuller (1979, Journal of the American Statistical Association 74: 427–431) unitroot test and its further developments based on supremum statistics derived from augmented Dickey–Fuller-type regressions estimated using recursive windows (Phillips, Wu, and Yu, 2011, International Economic Review 52: 201–226) and recursive flexible windows (Phillips, Shi, and Yu, 2015, International Economic Review 56: 1043–1078). It allows for the lag length in the test regression and the width of rolling windows to be either specified by the user or determined using data-dependent procedures, and it performs the date-stamping procedures advocated by Phillips, Wu, and Yu (2011) and Phillips, Shi, and Yu (2015) to identify episodes of explosive behavior. It also implements the wild bootstrap proposed by Phillips and Shi (2020, Handbook of Statistics: Financial, Macro and Micro Econometrics Using R, Vol. 42, 61–80) to lessen the potential effects of unconditional heteroskedasticity and account for the multiplicity issue in recursive testing. The use of radf is illustrated with an empirical example.

Implementing quantile selection models in Stata

In this article, we describe qregsel, a community-contributed command that implements a copula-based sample-selection correction for quantile regression recently proposed by Arellano and Bonhomme (2017, Econometrica 85: 1–28). The command allows the user to model selection in quantile regressions by using either a Gaussian or a one-dimensional Frank copula. We illustrate the use of qregsel with two examples. First, we apply the method to the fictional dataset used in the Stata Base Reference Manual for the heckman command. Second, we replicate part of the empirical application of the original article using data for the United Kingdom that cover the period 1978–2000 to compare wages of males and females at different quantiles.

Implementing the panel event study

Many studies estimate the impact of exposure to some quasiexperimental policy or event using a panel event study design. These models, as a generalized extension of “difference-in-differences” designs or two-way fixed-effects models, allow for dynamic leads and lags to the event of interest to be estimated, while also controlling for fixed factors (often) by area and time. In this article, we discuss the setup of the panel event study design in a range of situations and lay out several practical considerations for its estimation. We describe a command, eventdd, that allows for simple estimation, inference, and visualization of event study models in a range of circumstances. We then provide several examples to illustrate eventdd’s use and flexibility, as well as its interaction with various native Stata commands, and other relevant community-contributed commands such as reghdfe and boottest.

Review of Michael N. Mitchell’s Interpreting and Visualizing Regression Models Using Stata, Second Edition

In this article, we review Interpreting and Visualizing Regression Models Using Stata, Second Edition, by Michael N. Mitchell (2021, Stata Press).

On identification and estimation of Heckman models

In this article, we present commands to enable fixing the value of the correlation between the unobservables in Heckman models. These commands can solve two practical issues. First, for situations in which a valid exclusion restriction is not available, these commands enable exploring how the results could be affected by sample-selection bias. Second, stepping through values of this correlation can verify whether the global maximum of the likelihood function has been found. We provide several commands to fit these and related models with a fixed value of the correlation between the unobservables.

Relative distribution analysis in Stata

In this article, I discuss the method of relative distribution analysis and present Stata software implementing various elements of the methodology. The relative distribution is the distribution of the relative ranks that the outcomes from one distribution take on in another distribution. The methodology can be used, for example, to compare the distribution of wages between men and women. The presented software, reldist, estimates the relative cumulative distribution and the relative density, as well as the relative polarization, divergence, and other summary measures of the relative ranks. It also provides functionality such as location and shape decompositions or covariate balancing. Statistical inference is implemented in terms of influence functions and supports estimation for complex samples.