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Advances in Econometrics is a series of research annuals first published in 1982 by JAI Press. In this paper, we present a brief history of the series over its first 30 years. We describe key events in the history of the volume, and give information about the key contributors: editors, editorial board members, Advances in Econometrics Fellows, and authors who have contributed to the great success of the series.

Bayesian Econometrics is a volume in the series Advances in Econometrics that illustrates the scope and diversity of modern Bayesian econometric applications, reviews some recent advances in Bayesian econometrics, and highlights many of the characteristics of Bayesian inference and computations. This first paper in the volume is the Editors’ introduction in which we summarize the contributions of each of the papers.

This paper examines the diffusion of Jerry Hausman's econometric ideas using citation counts, citing authors, and source journals of his most referenced citers. Bibliographic information and citation counts of references to econometrics papers were retrieved from Thomson Reuters Web of Science and analyzed to determine the various ways in which Hausman's ideas have spread in econometrics and related disciplines. Econometric growth analysis (Gompertz and logistic functions) is used to measure the diffusion of his contributions. This analysis reveals that the diffusion of Hausman's ideas has been pervasive over time and disciplines. For example, his seminal 1978 paper continues to be strongly cited along exponential growth with total cites mainly in econometrics and other fields such as administrative management, human resources, and psychology. Some of the more recent papers have a growth pattern that resembles that of the 1978 paper. This leads us to conclude that Hausman's econometric contributions will continue to diffuse in years to come. It was also found that five journals have published the bulk of the top cited papers that list Hausman as a reference, namely, Econometrica, Journal of Econometrics, Review of Economic Studies, Academy of Management Journal, and the Journal of Economic Literature. “Specification tests in econometrics” is Hausman's dominant contribution in this citation analysis. We found no previous research on the econometric modeling of citation counts as done in this paper. Thus, we expect to stimulate methodological improvements in future work.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

The collection of chapters in this 30th volume of Advances in Econometrics provides a well-deserved tribute to Thomas B. Fomby and R. Carter Hill, who have served as editors of the Advances in Econometrics series for 25 and 21 years, respectively. Volume 30 contains a more varied collection of chapters than previous volumes, in essence mirroring the wide variety of econometric topics covered by the series over 30 years. Volume 30 starts with a chapter discussing the history of this series over the last 30 years. The next five chapters can be broadly categorized as focusing on model specification and testing. Following this section are three contributions that examine instrumental variables models in quite different settings. The next four chapters focus on applied macroeconomics topics. The final chapter offers a practical guide to conducting Monte Carlo simulations.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.

The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics. Please see the previous dedication page of this volume. The basic themes of this part of Volume 20 of Advances in Econometrics are time-varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modeling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, the application of the technique of boosting in volatility forecasting, the use of different time scales in Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) modeling, out-of-sample evaluation of the ‘Fed Model’ in stock price valuation, structural change as an alternative to long memory, the use of smooth transition autoregressions in stochastic volatility modeling, the analysis of the “balancedness” of regressions analyzing Taylor-type rules of the Fed Funds rate, a mixture-of-experts approach for the estimation of stochastic volatility, a modern assessment of Clive's first published paper on sunspot activity, and a new class of models of tail-dependence in time series subject to jumps. Of course, we are also pleased to include Rob's and Clive's remarks on their careers and their views on innovation in econometric theory and practice that were given at the Third Annual Advances in Econometrics Conference held at Louisiana State University, Baton Rouge, on November 5–7, 2004.