[PDF] Recent Advances In Panel Data Nonlinear And Nonparametric Models A Festschrift In Honor Of Peter Cb Phillips eBook

Author : Juan Rodriguez-Poo
Publisher :
Page : 0 pages
File Size : 44,96 MB
Release : 2017
Category :
ISBN :

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In this paper, we provide an intensive review of the recent developments for semiparametric and fully nonparametric panel data models that are linearly separable in the innovation and the individual-specific term. We analyze these developments under two alternative model specifications: fixed and random effects panel data models. More precisely, in the random effects setting, we focus our attention in the analysis of some efficiency issues that have to do with the so-called working independence condition. This assumption is introduced when estimating the asymptotic variance-covariance matrix of nonparametric estimators. In the fixed effects setting, to cope with the so-called incidental parameters problem, we consider two different estimation approaches: profiling techniques and differencing methods. Furthermore, we are also interested in the endogeneity problem and how instrumental variables are used in this context. In addition, for practitioners, we also show different ways of avoiding the so-called curse of dimensionality problem in pure nonparametric models. In this way, semiparametric and additive models appear as a solution when the number of explanatory variables is large.

Author : Kung-Sik Chan
Publisher : World Scientific
Page : 412 pages
File Size : 20,2 MB
Release : 2009
Category : Business & Economics
ISBN : 9812836284

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This festschrift is dedicated to Professor Howell Tong on the occasion of his 65th birthday. With a Foreword written by Professor Peter Whittle, FRS, it celebrates Tong's path-breaking and tireless contributions to nonlinear time series analysis, chaos and statistics, by reprinting 10 selected papers by him and his collaborators, which are interleaved with 17 original reviews, written by 19 international experts. Through these papers and reviews, readers will have an opportunity to share many of the excitements, retrospectively and prospectively, of the relatively new subject of nonlinear time series. Tong has played a leading role in laying the foundation of the subject; his innovative and authoritative contributions are reflected in the review articles in the volume, which describe modern and related developments in the subject, including applications in many major fields such as ecology, economics, finance and others. This volume will be useful to researchers and students interested in the theory and practice of nonlinear time series analysis. Sample Chapter(s). Foreword (68 KB). Chapter 1: Birth of the Threshold Time Series Model (269 KB). Contents: Reflections on Threshold Autoregression (P J Brockwell); The Threshold Approach in Volatility Modelling (W K Li); Dependence and Nonlinearity (M Rosenblatt); Recent Developments on Semiparametric Regression Model Selection (J Gao); Thoughts on the Connections Between Threshold Time Series Models and Dynamical Systems (D B H Cline); Crossing the Bridge Backwards: Some Comments on Early Interdisciplinary Efforts (C D Cutler); On Likelihood Ratio Tests for Threshold Autoregression (K-S Chan & H Tong); An Adaptive Estimation Method for Semiparametric Models and Dimension Reduction (C Leng et al.); On Howell Tong's Contributions to Reliability (M M Ali); and other papers. Readership: Graduate students and researchers in statistics and related fields of ecology, economics and finance.

Author : Cheng Hsiao
Publisher : Cambridge University Press
Page : 0 pages
File Size : 25,37 MB
Release : 2010-02-11
Category : Business & Economics
ISBN : 9780521131001

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This important collection brings together leading econometricians to discuss recent advances in the areas of the econometrics of panel data, limited dependent variable models and limited dependent variable models with panel data. The contributors focus on the issues of simplifying complex real world phenomena into easily generalizable inferences from individual outcomes. As the contributions of G. S. Maddala in the fields of limited dependent variables and panel data have been particularly influential, it is a fitting tribute that this volume is dedicated to him.

Author : Dalia A. Ghanem
Publisher :
Page : 233 pages
File Size : 37,93 MB
Release : 2013
Category : Econometrics
ISBN : 9781303193743

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Microeconomic panel data, also known as longitudinal data or repeated measures, allow the researcher to observe the same individual across time. One of the advantages of panel data is that they allow the researcher to control for unobservable individual heterogeneity. The linear fixed effects model is the most commonly used method in empirical work to control for unobservable heterogeneity. Chapter 1 reviews the special features of the linear fixed effects model in detail, giving special attention to the definition of fixed effects and correlated random effects. It discusses the issues that arise when we move from a linear model to fully nonseparable models and reviews the two strands of the literature that are relevant for this dissertation: (1) the literature on nonlinear parametric panel data models with fixed effects, (2) the literature on nonparametric identification in nonseparable panel data models. Chapter 2 falls under the parametric nonlinear panel data models with fixed effects. Nonlinear panel data models with fixed effects are an important example in econometrics where the incidental parameter problem arises and the maximum likelihood estimator (MLE) is asymptotically biased. Bias correction of the MLE achieves consistency without increasing the asymptotic variance. Chapter 2 proposes a shrinkage estimator that combines that is shown to lead to a higher-order mean-square error improvement over the analytical bias-corrected estimator. Chapter 3 falls under the literature on nonparametric identification in nonseparable panel data models. Starting from a general DGP that exhibits nonseparability of the structural function, arbitrary individual and time heterogeneity, I give a necessary and sufficient condition for the point-identification of the APE for a subpopulation. This condition is then used to characterize the trade-off between assumptions on unobservable heterogeneity and the structural function that achieve identification. The identifying assumptions here have clear testable implications on the distribution of observables. I hence propose bootstrap-adjusted Kolmogorv-Smirnov and Cramer-von-Mises statistics to test these implications. Chapter 4 is an empirical paper that studies the issue of manipulation of air pollution data by Chinese cities. It applies tests similar in spirit to the tests proposed in Chapter 3 to test the presence of manipulation.