Volume 36 of Advances in Econometrics recognizes Aman Ullah's significant contributions in many areas of econometrics and celebrates his long productive career. The volume features original papers on the theory and practice of econometrics that is related to the work of Aman Ullah. Topics include nonparametric/semiparametric econometrics; finite sample econometrics; shrinkage methods; information/entropy econometrics; model specification testing; robust inference; panel/spatial models. Advances in Econometrics is a research annual whose editorial policy is to publish original research articles that contain enough details so that economists and econometricians who are not experts in the topics will find them accessible and useful in their research.

The Regression Discontinuity (RD) design is one of the most popular and credible research designs for program evaluation and causal inference. This volume 38 of Advances in Econometrics collects twelve innovative and thought-provoking contributions to the RD literature, covering a wide range of methodological and practical topics. Some chapters touch on foundational methodological issues such as identification, interpretation, implementation, falsification testing, estimation and inference, while others focus on more recent and related topics such as identification and interpretation in a discontinuity-in-density framework, empirical structural estimation, comparative RD methods, and extrapolation. These chapters not only give new insights for current methodological and empirical research, but also provide new bases and frameworks for future work in this area. This volume contributes to the rapidly expanding RD literature by bringing together theoretical and applied econometricians, statisticians, and social, behavioural and biomedical scientists, in the hope that these interactions will further spark innovative practical developments in this important and active research area.

The 'Advances in Econometrics' series aims to publish annual original scholarly econometrics papers on designated topics with the intention of expanding the use of developed and emerging econometric techniques by disseminating ideas on the theory and practice of econometrics throughout the empirical economic, business and social science literature.

The 30th Volume of Advances in Econometrics is in honor of the two individuals whose hard work has helped ensure thirty successful years of the series, Thomas Fomby and R. Carter Hill. This volume began with a history of the Advances series by Asli Ogunc and Randall Campbell summarizing the prior volumes. Tom Fomby and Carter Hill both provide discussions of the role of Advances over the years. The remaining articles include contributions by a number of authors who have played key roles in the series over the years and in the careers of Fomby and Hill. Overall, this leads to a more diverse mix of papers than a typical volume of Advances in Econometrics.

This volume of Advances in Econometrics contains articles that examine key topics in the modeling and estimation of dynamic stochastic general equilibrium (DSGE) models. Because DSGE models combine micro- and macroeconomic theory with formal econometric modeling and inference, over the past decade they have become an established framework for analyzing a variety of issues in empirical macroeconomics. The research articles make contributions in several key areas in DSGE modeling and estimation. In particular, papers cover the modeling and role of expectations, the study of optimal monetary policy in two-country models, and the problem of non-invertibility. Other interesting areas of inquiry include the analysis of parameter identification in new open economy macroeconomic models and the modeling of trend inflation shocks. The second part of the volume is devoted to articles that offer innovations in econometric methodology. These papers advance new techniques for addressing major inferential problems and include discussion and applications of Laplace-type, frequency domain, empirical likelihood and method of moments estimators.

This volume is a collection of methodological developments and applications of simulation-based methods that were presented at a workshop at Louisiana State University in November, 2009. The first two papers are extensions of the GHK simulator: one reconsiders the computation of the probabilities in a discrete choice model while another example uses an adaptive version of sparse-grids integration (SGI) instead of simulation. Two studies are focused specifically on the methodology: the first compares the performance of the maximum-simulated likelihood (MSL) approach with a proposed composite marginal likelihood (CML) approach in multivariate ordered-response situations, while the second examines methods of testing for the presence of heterogeneity in the heterogeneity model. Further topics examined include: education savings accounts, parent contributions and education attainment; estimating the effect of exchange rate flexibility on financial account openness; estimating a fractional response model with a count endogenous regressor; and modelling and forecasting volatility in a bayesian approach.

Often applied econometricians are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes econometric results are very fragile to the inclusion or omission of just a few observations in the sample. Papers in this volume discuss new econometric techniques for addressing these problems.

This book had its conception in 1975in a friendly tavern near the School of Businessand PublicAdministration at the UniversityofMissouri-Columbia. Two of the authors (Fomby and Hill) were graduate students of the third (Johnson), and were (and are) concerned about teaching econometrics effectively at the graduate level. We decided then to write a book to serve as a comprehensive text for graduate econometrics. Generally, the material included in the bookand itsorganization have been governed by the question, " Howcould the subject be best presented in a graduate class?" For content, this has meant that we have tried to cover " all the bases " and yet have not attempted to be encyclopedic. The intended purpose has also affected the levelofmathematical rigor. We have tended to prove only those results that are basic and/or relatively straightforward. Proofs that would demand inordinant amounts of class time have simply been referenced. The book is intended for a two-semester course and paced to admit more extensive treatment of areas of specific interest to the instructor and students. We have great confidence in the ability, industry, and persistence of graduate students in ferreting out and understanding the omitted proofs and results. In the end, this is how one gains maturity and a fuller appreciation for the subject in any case. It is assumed that the readers of the book will have had an econometric methods course, using texts like J. Johnston's Econometric Methods, 2nd ed.

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. 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, modelling 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 GARCH modelling, 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 auto-regressions in stochastic volatility modelling, the analysis of the balanced-ness 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 Clives first published paper on Sunspot activity, and a new class of models of tail-dependence in time series subject to jumps.
*This Series: Aids in the diffusion of new econometric techniques
* Emphasis is placed on expositional clarity and ease of assimilation for readers who are unfamiliar with a given topic of a volume
*Illustrates new concepts

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. 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, modelling 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 GARCH modelling, 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 auto-regressions in stochastic volatility modelling, the analysis of the balanced-ness 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 Clives first published paper on Sunspot activity, and a new class of models of tail-dependence in time series subject to jumps.
*This Series: Aids in the diffusion of new econometric techniques
*Emphasis is placed on expositional clarity and ease of assimilation for readers who are unfamiliar with a given topic of a volume
*Illustrates new concepts

This volume is the result of an Advances in Econometrics conference held in November of 2002 at Louisiana State University in recognition of Halbert White's pioneering work published in Econometrica in 1980 and 1982 on robust variance-covariance estimation and quasi-maximum likelihood estimation. It contains 11 papers on a range of related topics including the estimation of possibly misspecified error component and fixed effects panel models, estimation and inference in possibly misspecified quantile regression models, quasi-maximum likelihood estimation of linear regression models with bounded and symmetric errors and quasi-maximum likelihood estimation of models with parameter dependencies between the mean vector and error variance-covariance matrix. Other topics include GMM, HAC, Heckit, asymmetric GARCH, Cross-Entropy, and multivariate deterministic trend estimation and testing under various possible misspecifications.

The entropy concept was developed and used by Shannon in 1940 as a measure of uncertainty in the context of information theory. In 1957 Jaynes made use of Shannon's entropy concept as a basis for estimation and inference in problems that are ill-suited for traditional statistical procedures. This volume consists of two sections. The first section contains papers developing econometric methods based on the entropy principle. An interesting array of applications is presented in the second section of the volume.

1 Introduction.- 2 Review of Ordinary Least Squares and Generalized Least Squares.- 3 Point Estimation and Tests of Hypotheses in Small Samples.- 4 Large Sample Point Estimation and Tests of Hypotheses.- 5 Stochastic Regressors.- 6 Use of Prior Information.- 7 Preliminary Test and Stein-Rule Estimators.- 8 Feasible Generalized Least Squares Estimation.- 9 Heteroscedasticity.- 10 Autocorrelation.- 11 Lagged Dependent Variables and Autcorrelation.- 12 Unobservable Variables.- 13 Multicollinearity.- 14 Varying Coefficient Models.- 15 Models That Combine Time-Series and Cross-Section Data.- 16 The Analysis of Models with Qualitative or Censored Dependent Variables.- 17 Distributed Lags.- 18 Uncertainty in Model Specification and Selection.- 19 Introduction to Simultaneous Equations Models.- 20 Identification.- 21 Limited Information Estimation.- 22 Full Information Estimation.- 23 Reduced Form Estimation and Prediction in Simultaneous Equations Models.- 24 Properties of Dynamic Simultaneous Equations Models.- 25 Special Topics in Simultaneous Equations.- Appendix Estimation and Inference in Nonlinear Statistical Models.- A.1 Nonlinear Optimization.- A.1.1 Method of Steepest Ascent.- A.1.2 The Method of Newton.- A.1.3 Method of Quadratic Hill Climbing.- A.1.4 Numerical Differentiation.- A.2 Maximum Likelihood Estimation.- A.2.1 Use of the Method of Newton.- A.2.2 Method of Scoring.- A.2.3 The Method of Berndt, Hall, Hall, and Hausman.- A.2.4 Asymptotic Tests Based on the Maximum Likelihood Method.- A.2.4a The Wald Test.- A.2.4b The Lagrange-Multiplier Test.- A.2.4c The Likelihood Ratio Test Statistic.- A.2.4d Concluding Remarks.- A.3 Nonlinear Regression.- A.4 Summary and Guide to Further Readings.- A.5 References.

This Book Set consists of: *9781780525242 - Missing Data Methods: Cross-sectional Methods and Applications (Part A) *9781780525266 - Missing Data Methods: Time-series Methods and Applications (Part B) The papers in this volume cover topics in the econometric approach to missing data problems. Data can be missing because an individual failed to answer a question or because the laws of nature imply that an individual can only follow one of several possible paths. We refer to the first case as one of missing observations and to the second case as one of unobserved outcomes. This volume reflects the fact that econometricians have been very active in the development and use of methods for unobserved outcomes. The huge interest in these methods caused the volume to be split into parts A and B. The 12 chapters in Part A discuss cross-sectional methods. All the papers either derive, survey, or evaluate new methods for handling missing-data problems. Per the current interest in econometrics, 11 of the 12 papers address unobserved-outcome problems. The 4 chapters in Part B discuss time-series methods. Two chapters comprehensively survey the use of Markov switching models in finance. The third chapter surveys discrete-time and continuous-time models for volatility. The fourth chapter derives a new imputation method for nonstationary panel-data models and compares it to existing methods.