We propose a two-stage estimation procedure to identify the effects of time-invariant regressors in a dynamic version of the Hausman-Taylor model. We first estimate the coefficients of the time-varying regressors and subsequently regress the first-stage residuals on the time-invariant regressors providing analytical standard error adjustments for the second-stage coefficients. The two-stage approach is more robust against misspecification than GMM estimators that obtain all parameter estimates simultaneously. In addition, it allows exploiting advantages of estimators relying on transformations to eliminate the unit-specific heterogeneity. We analytically demonstrate under which conditions the one-stage and two-stage GMM estimators are equivalent. Monte Carlo results highlight the advantages of the two-stage approach infinite samples. Finally, the approach is illustrated with the estimation of a dynamic gravity equation for U.S. outward foreign direct investment.

We propose a two-stage estimation procedure to identify the effects of time-invariant regressors in a dynamic version of the Hausman-Taylor model. We first estimate the coefficients of the time-varying regressors and subsequently regress the first-stage residuals on the time-invariant regressors providing analytical standard error adjustments for the second-stage coefficients . The two-stage approach is more robust against misspecification than GMM estimators that obtain all parameter estimates simultaneously. In addition, it allows exploiting advantages of estimators relying on transformations to eliminate the unit specific heterogeneity. We analytically demonstrate under which conditions the one-stage and two-stage GMM estimators are equivalent. Monte Carlo results highlight the advantages of the two-stage approach infinite samples. Finally, the approach is illustrated with the estimation of a dynamic gravity equation for U.S. outward foreign direct investment.

Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

Financial data are typically characterised by a time-series and cross-sectional dimension. Accordingly, econometric modelling in finance requires appropriate attention to these two – or occasionally more than two – dimensions of the data. Panel data techniques are developed to do exactly this. This book provides an overview of commonly applied panel methods for financial applications, including popular techniques such as Fama-MacBeth estimation, one-way, two-way and interactive fixed effects, clustered standard errors, instrumental variables, and difference-in-differences. Panel Methods for Finance: A Guide to Panel Data Econometrics for Financial Applications by Marno Verbeek offers the reader: Focus on panel methods where the time dimension is relatively small A clear and intuitive exposition, with a focus on implementation and practical relevance Concise presentation, with many references to financial applications and other sources Focus on techniques that are relevant for and popular in empirical work in finance and accounting Critical discussion of key assumptions, robustness, and other issues related to practical implementation

Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.

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