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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
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.
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Seitenzahl: 546
Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Dedication
Preface
Acknowledgments
About the Companion Website
Chapter 1: Introduction
1.1 Panel Data Econometrics: A Gentle Introduction
1.2 R for Econometric Computing
1.3 plm for the Casual R User
1.4 plm for the Proficient R User
1.5 plm for the R Developer
1.6 Notations
Chapter 2: The Error Component Model
2.1 Notations and Hypotheses
2.2 Ordinary Least Squares Estimators
2.3 The Generalized Least Squares Estimator
2.4 Comparison of the Estimators
2.5 The Two‐ways Error Components Model
2.6 Estimation of a Wage Equation
Chapter 3: Advanced Error Components Models
3.1 Unbalanced Panels
3.2 Seemingly Unrelated Regression
3.3 The Maximum Likelihood Estimator
3.4 The Nested Error Components Model
Chapter 4: Tests on Error Component Models
4.1 Tests on Individual and/or Time Effects
4.2 Tests for Correlated Effects
4.3 Tests for Serial Correlation
4.4 Tests for Cross‐sectional Dependence
Chapter 5: Robust Inference and Estimation for Non‐spherical Errors
5.1 Robust Inference
5.2 Unrestricted Generalized Least Squares
Chapter 6: Endogeneity
6.1 Introduction
6.2 The Instrumental Variables Estimator
6.3 Error Components Instrumental Variables Estimator
6.4 Estimation of a System of Equations
6.5 More Empirical Examples
Chapter 7: Estimation of a Dynamic Model
7.1 Dynamic Model and Endogeneity
7.2 GMM Estimation of the Differenced Model
7.3 Generalized Method of Moments Estimator in Differences and Levels
7.4 Inference
7.5 More Empirical Examples
Chapter 8: Panel Time Series
8.1 Introduction
8.2 Heterogeneous Coefficients
8.3 Cross‐sectional Dependence and Common Factors
8.4 Nonstationarity and Cointegration
Chapter 9: Count Data and Limited Dependent Variables
9.1 Binomial and Ordinal Models
9.2 Censored or Truncated Dependent Variable
9.3 Count Data
9.4 More Empirical Examples
Chapter 10: Spatial Panels
10.1 Spatial Correlation
10.2 Spatial Lags
10.3 Individual Heterogeneity in Spatial Panels
10.4 Serial and Spatial Correlation
Bibliography
Index
End User License Agreement
List of Tables
Chapter 02
Table 2.1 Wage Equation.
Chapter 05
Table 5.1 Covariance structures as combinations of the basic building blocks.
Chapter 06
Table 6.1 Estimations of the gravity model.
List of Illustrations
Chapter 02
Figure 2.1 Imports in terms of the national product for the ForeignTrade data.
Figure 2.2 Cost in terms of output for the TurkishBanks data.
Figure 2.3 Cost and output for the TexasElectr data set.
Figure 2.4 Democracy and lagged income for the data DemocracyIncome25.
Chapter 03
Figure 3.1 Estimators of the variance components for unbalanced panels.
Figure 3.2 Error components estimators for the nested error component model.
Chapter 07
Figure 7.1 Relationship between income and democracy.
Figure 7.2 First step coefficient as a function of
.
Figure 7.3 The supplementary condition of th system‐
GMM
estimator.
Chapter 08
Figure 8.1 Individual coefficients, HousePriceUS.
Figure 8.2 Autoregressive processes with different
parameters.
Figure 8.3 Histogram of the Student statistic in case of a unit root.
Chapter 09
Limited dependent variable.
Figure 9.2 Distribution of
and
.
Figure 9.3
OLS
bias for the censored and the truncated samples.
Figure 9.4 Distribution of
and of
.
Figure 9.5 Symmetry of the distribution of
.
Figure 9.6 Average annual citations by age, BRC versus control articles.
Chapter 10
Figure 10.1 Growth of house prices indexes in the USA between 1980 and 2000.
Guide
Cover
Table of Contents
Begin Reading
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E1
Panel Data Econometrics with R
Edited by
Yves Croissant
Professor of Economics CEMOI Faculté de Droit et d'Economie Université de La Réunion France
Giovanni Millo
Senior Economist Group Insurance Research, Assicurazioni Generali S. p. A. Trieste, Italy
Copyright
This edition first published 2019
© 2019 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Yves Croissant and Giovanni Millo to be identified as the authors of this work has been asserted in accordance with law.
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While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging‐in‐Publication Data
Names: Croissant, Yves, 1969- author. | Millo, Giovanni, 1970- author.
Title: Panel data econometrics with R / Yves Croissant, Giovanni Millo.
Description: First edition. | Hoboken, NJ : John Wiley & Sons, 2018. |
Includes index. |
Identifiers: LCCN 2018006240 (print) | LCCN 2018014738 (ebook) | ISBN
9781118949177 (pdf) | ISBN 9781118949184 (epub) | ISBN 9781118949160
(cloth)
Subjects: LCSH: Econometrics. | Panel analysis. | R (Computer program
language)
Classification: LCC HB139 (ebook) | LCC HB139 .C765 2018 (print) | DDC
330.0285/5133-dc23
LC record available at https://lccn.loc.gov/2018006240
Cover Design: Wiley
Cover Image: ©Zffoto/Getty Images
Dedication
To Agnès, Fanny and Marion, to my parents - Yves
To the memory of my uncles, Giovanni and Mario - Giovanni
Preface
While R is the software of choice and the undisputed leader in many fields of statistics, this is not so in econometrics; yet, its popularity is rising both among researchers and in university classes and among practitioners. From user feedback and from citation information, we gather that the adoption rate of panel‐specific packages is even higher in other research fields outside economics where econometric methods are used: finance, political science, regional science, ecology, epidemiology, forestry, agriculture, and fishing.
This is the first book entirely dedicated to the subject of doing panel data econometrics in R, written by the very people who wrote most of the software considered, so it should be naturally adopted by R users wanting to do panel data analysis within their preferred software environment. According to the best practices of the R community, every example is meant to be replicable (in the style of package vignettes); all code is available from the standard online sources, as are all datasets. Most of the latter are contained in a dedicated companion package, pder. The book is supposed to be both a reasonably comprehensive reference on R functionality in the field of panel data econometrics, illustrated by way of examples, and a primer on econometric methods for panel data in general.
While we have tried to cover the vast majority of basic methods and much of the more advanced ones (corresponding roughly to graduate and doctoral level university courses), the book is still less exhaustive than main reference textbooks (one for all, Baltagi, 2013) the a priori being that the reader should be able to apply all the methods presented in the book through available R code from plm and related, more specialized packages.
One should note from the beginning that, from a computational viewpoint, the average R user tends to be more advanced than users of commercial statistical packages. R users will generally be interested in interactive statistical programming whereby they can be in full control of the procedures they use and eventually be looking forward to write their own code or adapt the existing one to their own purposes. All that said, despite its reputation, R lends itself nicely to standard statistical practice: issuing a command, reading output. Hence the potential readership spans an unusually broad spectrum and will be best identified by subject rather than by level of technical difficulty.
Examples are usually written without employing advanced features but still using a fair amount of syntax beyond what would be the plain vanilla “estimate, print summary” procedure sketched above; the reader replicating them will therefore be exposed to a number of simple but useful constructs—ranging from general purpose visualization to compact presentation of results—stemming from the fact that she is using a full‐featured programming language rather than a canned package.
The general level is introductory and aimed at both students and practitioners. Chapters 1–2, and to some extent 4–5, cover the basics of panel data econometrics as taught in undergraduate econometrics classes, if at all. With some overlapping, the main body of the book (Ch. 3–6) covers the typical subjects of an advanced panel data econometrics course at graduate level. Nevertheless, the coverage of the later chapters (especially 7–10) spans fields typical of current applied research; therefore it should appeal particularly to graduate students and researchers. For all this, the book might play two main roles: companion to advanced textbooks for graduate students taking a panel data course, with Chapters 1–7 covering the course syllabus and 8–10 providing more cutting‐edge material for extensions; and reference text for practitioners or applied researchers in the field, covering most of the methods they are ever likely to use, with applied examples from recent literature. Nevertheless, its first half can be used in an undergraduate course as well, especially considering the wealth of examples and the possibility to replicate all material. Symmetrically, the last chapters can appeal to researchers wanting to employ cutting‐edge methods—for which there is usually around only quite unfriendly code written in matrix language by methodologists—with the relative user‐friendliness of R. As an example, Ch. 10 is based on the R tutorials one of the authors gives at the Spatial Econometrics Advanced Institute in Rome, the world‐leading graduate school in applied spatial econometrics.
Econometrics is a late comer to the world of R, although of course much of basic econometrics employs standard statistical tools, which were present in base R. Typical functionality, addressing the emphasis on model assumptions and testing, which is characteristic of the discipline, started to appear with the lmtest package and the accompanying paper of Zeileis & Hothorn (2002); a review paper on the use of R in econometrics, focused on teaching, was published at about the same time (Racine & Hyndman, 2002). This was followed by further dedicated packages extending the scope of specialized methods to structural equation modeling, time series, stability testing, and robust covariance estimation, to name a few; while despite the availability of some online tutorials, no dedicated book would appear in print until Kleiber & Zeileis (2008).
In the wake of any organized and comprehensive R package for panel data econometrics, Yves Croissant started developing plm in 2006, presenting one early version of the software at the 2006 useR! Conference in Vienna. Giovanni Millo joined the project as coauthor shortly thereafter. Two years later, an accompanying paper to plm (Croissant & Millo, 2008) featured prominently in the econometrics special issue of the Journal of Statistical Software testifying the improved availability of econometric methods in R and the increased relevance of the R project for the profession.
More recently, Kevin Tappe has become the third author. Liviu Andronic, Arne Henningsen, Christian Kleiber, Ott Toomet, and Achim Zeileis importantly contributed to the package at various times. Countless users provided feedback, smart questions, bug reports, and, often, solutions.
Estimating the user base is no simple task, but the available evidence points at large and growing numbers. The 2008 paper describing an earlier version of the package has since been downloaded almost 100,000 times and peaked on Goggle Scholar's list as the 25th most cited paper in the Journal of Statistical Software, the leading outlet in the field, before hitting the five‐year reporting limit. At the time of writing, it counts over 400 citations on Google Scholar, despite the widespread bad habit of not citing software papers. The monthly number of package downloads from a leading mirror site has been recently estimated at 6,000.
Chapters 2, 3, 6, 7, and 8 have been written by Yves Croissant; 1, 5, 9 (except the first generation unit root testing section), and 10 by Giovanni Millo, chapter 4 being co‐written.
The book has been produced through Emacs+ESS (Rossini et al., 2004) and typeset in LaTeX using Sweave (Leisch, 2002) and later knitr (Xie, 2015). Plots have been made using ggplot2 (Wickham, 2009) and tikz (Tantau, 2013).
The companion package to this book is pder (Croissant & Millo, 2017); the methods described are mainly in the plm package (Croissant & Millo, 2008) but also in pglm (Croissant, 2017) and splm (Millo & Piras, 2012). General purpose tests and diagnostics tools of packages car (Fox & Weisberg, 2011), lmtest (Zeileis & Hothorn, 2002), sandwich (Zeileis, 2006b), and AER (Kleiber & Zeileis, 2008) have been used in the code, as have some more specialized tools available in MASS (Venables & Ripley, 2002), censReg (Henningsen, 2017), nlme (Pinheiro et al., 2017), survival (Therneau & Grambsch, 2000), truncreg (Croissant & Zeileis, 2016), pcse (Bailey & Katz, 2011), and msm (Jackson, 2011). dplyr (Wickham & Francois, 2016) has been used to work with data.frames and Formula with general formulas. stargazer (Hlavac, 2013) and texreg (Leifeld, 2013) were used to produce fancy tables, the fiftystater package (Murphy, 2016) to plot a United States map. The packages presented and the example code are entirely cross‐platform as being part of the R project.
Acknowledgments
We thank Kevin Tappe, now a coauthor of “plm,” for his invaluable help in improving, checking and extending the functionality of the package. It is difficult to overstate the importance of his contribution.
Achim Zeileis, Christian Kleiber, Ott Toomet, Liviu Andronic, and Nina Schoenfelder have contributed code, fixes, ideas, and interesting discussions at different stages of development. Too many users to list here have provided feedback, good words of encouragement, and bug reports. Often those reporting a bug have also provided, or helped in working out, a solution.
We thank the authors of all the papers that are replicated or simply cited here, for their inspiring research and for making their datasets available. Barbara Rossi (editor) and James MacKinnon (maintainer of the data archive) of the Journal of Applied Econometrics (JAE) are thanked together with the original authors for kindly sharing the JAE data archive datasets.
Personal thanks
Yves Croissant
The first drafts of several chapters of the book have been written while giving a panel data course in the applied economics master of the University of La Reunion. I thank the students of this course for their useful feedback, which helped improving the text. I've been working with Fabrizio Carlevaro on several projects for about 20 years. During this collaboration, he shared with me his deep knowledge of econometrics, and the endless discussions we had were an invaluable source of inspiration for me.
Giovanni Millo
I thank my parents, Luciano and Lalla, for lifelong support and inspiration; Roberta, for her love and patience; my uncle Marjan, for giving me my first electronic calculator—a TI30—when I was a child, sparking a lasting interest for automatic computing; my mentors Attilio Wedlin, Gaetano Carmeci, and Giorgio Calzolari, for teaching me econometrics; and Davide Fiaschi, Angela Parenti, Riccardo “Jack” Lucchetti, Eduardo Rossi, Giuseppe Arbia, Gianfranco Piras, Elisa Tosetti, Giacomo Pasini, and other friends from the “small world” of Italian econometrics—again, too many to list exhaustively here—for so many interesting discussions about econometrics, computing with R, or both.
About the Companion Website
This book is accompanied by a companion website:
www.wiley.com/go/croissant/data-econometrics-with-R
The website includes code for reproducing all examples in the book, which can be found below:
Examples Ch.1
Examples Ch.2
Examples Ch.3
Examples Ch.4
Examples Ch.5
Examples Ch.6
Examples Ch.7
Examples Ch.8
Examples Ch.9
Examples Ch.10
The datasets are to be found in the pder package in the below link:
https://cran.r-project.org/web/packages/pder/index.html
Scan this QR code to visit the companion website.
