Panel Data Econometrics

Theory

Mike Tsionas

• Publisher: Academic Press
• Year: 2019
• Print ISBN: 9780128143674
• eBook ISBN: 9780128144312
• Topic: Economics
• Subtopic: Econometrics
• Index: Economics

Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. - Provides a vast array of empirical applications useful to practitioners from different application environments - Accompanied by extensive case studies and empirical exercises - Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings - Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts

Chapter 1

A Synopsis of Econometrics

Stephen G. Hall Leicester University, Leicester, United Kingdom
Bank of Greece, Athens, Greece
University of Pretoria, Pretoria, South Africa

Abstract

In this chapter, we provide a broad synopsis and background to standard econometric techniques. The aim of this chapter is to act as a foundation for the rest of this book and to make it a self-contained reference book. Inevitably, this will mean a brief account of many of the issues we discuss, and we aim to provide references that will give more complete and comprehensive accounts of each section we address.

Keywords

Econometrics; Independent identical distribution; Sampling distribution; Maximum likelihood (ML); Generalized Method of Moments (GMM); Walde test

Chapter Outline

• 1 Introduction
• 2 Some Basic Concepts
• 3 Two Basic Approaches to Estimation
• 4 Maximum Likelihood (Ml)
• 5 Generalized Method of Moments (GMM)
• 6 Some Examples of Moment Conditions
• 7 The Standard Linear Model and Least Squares
• 8 Failure of E(uu′) = σ²I
• 9 The Vector x_t is Stochastic
• 10 Failure of $\begin{array}{c}\mathbf{lim}\\ \mathit{t}\mathbf{\to }\mathbf{\infty }\end{array}\left(\frac{{\mathit{x}}^{\prime }\mathit{u}}{\mathit{T}}\right)\mathbf{=}\mathbf{0}$
  
• 11 Failure of $\begin{array}{c}\mathbf{lim}\\ \mathit{t}\mathbf{\to }\mathbf{\infty }\end{array}\left(\frac{{\mathit{x}}^{\prime }\mathit{x}}{\mathit{T}}\right)\mathbf{=}\mathrm{\Omega }$
  
• 12 Cointegration
• 13 Conclusion
• Appendix Order of Magnitude and Convergence
• References

1 Introduction

In this chapter, we provide a broad synopsis and background to standard econometric techniques. The aim of this chapter is to act as a foundation for the rest of this book and to make it a self-contained reference book. Inevitably, this will mean a brief account of many of the issues we discuss, and we aim to provide references that will give more complete and comprehensive accounts of each section we address.

We begin by outlining some fundamental concepts that lie behind much of what goes on in econometrics: the idea of a population, random variables, random sampling, the sampling distribution, and the central limit theorem. We then explore two of the basic approaches to constructing an econometric estimator: the maximum likelihood principal and the general method of moments. We then go through the standard linear model, the basic workhorse of econometrics, and the various problems that can arise in this familiar case. We then explore the issue of nonstationarity, which has dominated many of the developments in econometrics during the last 30 years.

2 Some Basic Concepts

At its heart, econometrics is about quantifying effects in the real world and assessing these effects to gain some notion of their reliability. Economic theory often can suggest the direction of a causal effect, but it rarely suggests the exact magnitude of such an effect nor what the correct functional form should be. To make the realm of econometrics operational, we need a statistical framework that allows us to operate in a wide range of situations, at least to a good approximation of the real world. This framework begin...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Contributors
• Foreword
• General Introduction
• Chapter 1: A Synopsis of Econometrics
• Chapter 2: Testing and Correcting for Endogeneity in Nonlinear Unobserved Effects Models
• Chapter 3: Nonlinear and Related Panel Data Models
• Chapter 4: Nonparametric Estimation and Inference for Panel Data Models
• Chapter 5: Heterogeneity and Endogeneity in Panel Stochastic Frontier Models
• Chapter 6: Bayesian Estimation of Panel Count Data Models: Dynamics, Latent Heterogeneity, Serial Error Correlation, and Nonparametric Structures
• Chapter 7: Fixed Effects Likelihood Approach for Large Panels
• Chapter 8: Panel Vector Autoregressions With Binary Data
• Chapter 9: Implementing Generalized Panel Data Stochastic Frontier Estimators
• Chapter 10: Panel Cointegration Techniques and Open Challenges
• Chapter 11: Alternative Approaches to the Econometrics of Panel Data
• Chapter 12: Analysis of Panel Data Using R
• Index