We review the Stata statistical package and evaluate its suitability for applied research.

This tutorial will discuss a number of elementary Stata programming constructs and discuss how they may be used to automate and robustify common data manipulation, estimation and graphics tasks. Those used to the syntax of other statistical packages or programming languages must adopt a...

We extend our 2003 paper on instrumental variables (IV) and GMM estimation and testing and describe enhanced routines that address HAC standard errors, weak instruments, LIML and k-class estimation, tests for endogeneity and RESET and autocorrelation tests for IV estimates.

This paper discusses the use of Stata for the analysis of time series and panel data. The evolution of time-series capabilities in Stata is reviewed. Facilities for data management, graphics, and econometric analysis from both official Stata and the user community are discussed. A new routine to...

This paper is a review of the statistical package Stata, version 8.1, with special emphasis on its time series capabilities. The program which generates empirical results and graphics in the paper may also be downloaded.

We discuss instrumental variables (IV) estimation in the broader context of the generalized method of moments (GMM), and describe an extended IV estimation routine that provides GMM estimates as well as additional diagnostic tests. Stand-alone test procedures for heteroskedasticity,...

We describe the Stata software environment, and illustrate how it may be profitably employed for applied economic research. Stata stands between "point and click" statistical packages and matrix languages in terms of extensibility and ease of use, and provides web-accessible features that...

It is widely known that when there are negative moving average errors, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and BIC tend to select a truncation lag that is very small. Furthermore, size distortions...

This paper studies the error in forecasting a dynamic time series with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model which detrends the data before estimating the dynamic parameters are much less precise than those based on an...

We consider issues related to the order of an autoregression selected using information criteria. We study the sensitivity of the estimated order to i) whether the effective number of observations is held fixed when estimating models of different order, ii) whether the estimate of the variance...