book

Panel Methods for Finance

Name: Panel Methods for Finance
Author: Marno Verbeek
ISBN: 9783110660814

by Marno Verbeek

October 2021

Intermediate to advanced

296 pages

10h 42m

English

De Gruyter

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Preface
AcknowledgmentsAcronyms
1 Introduction
1.1 Some illustrative examples1.1.1 Predicting the cross-section of stock returns1.1.2 Explaining flows to mutual funds1.1.3 The impact of CEO compensation or board structure on firm value1.1.4 Explaining capital structure choice1.1.5 Governance and firm value1.1.6 Dividend policy1.2 The structure of this text
2 Linear static models
2.1 The linear regression model with cross-sectional data2.1.1 The linear model and ordinary least squares2.1.2 Heteroskedasticity2.1.3 Hypothesis testing2.2 The linear regression model with time-series data2.3 Introducing panel data2.3.1 Parameter heterogeneity2.3.2 Illustration: testing an asset pricing model2.4 The linear regression model with panel data2.5 Clustered standard errorsDouble clusteringSmall sample issuesThe choice of clustersCorrelation structuresTowards panel methodsBootstrapped standard errors2.6 Random effects estimators2.7 Fixed effects estimatorsThe Hausman testTesting for fixed effects2.8 Clustered standard errors with fixed effectsA robust Hausman test2.9 First-difference estimators2.10 Testing for heteroskedasticity and autocorrelation2.11 Models with fixed time effects2.12 The Fama-MacBeth approach2.12.1 Cross-sectional regressions2.12.2 Serial correlation and firm effects2.12.3 Pooled OLS versus Fama-MacBeth2.12.4 The errors-in-variables problem2.13 Goodness-of-fit
3 Dealing with heterogeneity and endogeneity: fixed effects, IV and GMM
3.1 Fixed effects and instrumental variablesThe Hausman-Taylor estimator3.2 Two-way and interactive fixed effectsGroup fixed effectsSome concerns and drawbacksClustered standard errorsSingletonsFixed effects that are not fixed3.3 How not to control for unobserved heterogeneityGroup-adjusting the dependent variableAdding group-specific averages as control variablesGroup fixed effects3.4 More on instrumental variables3.4.1 Two-stage least squares3.4.2 Weak instruments3.4.3 Standard errors3.4.4 IV estimators with panel data3.5 Instrumental variables and GMM3.5.1 Moment conditions3.5.2 The optimal weighting matrix3.5.3 Tests for overidentifying restrictions3.5.4 Panel estimators3.6 Strict exogeneityLong panelsPredictive regressionsEstimation without strict exogeneity
4 Outliers, missing values and other data issues
4.1 Outliers and influential observations4.1.1 Identifying and eliminating outliers4.1.2 Robust estimation methods4.2 Missing values4.3 Incomplete panels4.3.1 Estimation with randomly missing data4.3.2 Selection bias and some simple tests4.4 Measurement errorsClassical measurement errorFixed effectsNon-classical measurement errorPotential solutions
5 Linear dynamic models
5.1 The problem of unobserved heterogeneity5.2 The problem with the fixed effects estimator5.3 Instrumental variables5.4 The Arellano-Bond estimator5.4.1 Moment conditions5.4.2 Models with exogenous variables5.4.3 System GMM5.4.4 Specification tests5.4.5 When γ is close to one5.4.6 Too many instruments5.5 Applications
6 Models with limited dependent variables
6.1 Binary choice models6.1.1 Specification and interpretation6.1.2 Estimation: maximum likelihood6.1.3 Pooled estimation approaches6.1.4 Clustered standard errors6.1.5 Fama-MacBeth estimation6.1.6 Binary choice models with random effects6.1.7 Binary choice models with fixed effects6.1.8 Goodness-of-fit6.1.9 Binary choice models and instrumental variables6.2 Multiple outcomes6.2.1 Ordered probit and ordered logit6.2.2 Multinomial models6.3 Tobit models6.3.1 Introduction to censoring6.3.2 Pooled estimation6.3.3 A tobit model with random effects6.3.4 Extensions6.4 Dynamic models6.4.1 State dependence versus unobserved heterogeneity6.4.2 A dynamic probit model with unobserved heterogeneity6.5 Count dataRandom effects and fixed effects models6.6 Duration models6.6.1 Hazard rates and survival functions6.6.2 Estimation
7 Estimating average treatment effects
7.1 Potential outcomes7.1.1 Average treatment effects7.1.2 Randomised experiments7.1.3 Biases7.2 Conditional independence7.2.1 Weighting7.2.2 Regression-adjustment7.2.3 Regression with a treatment indicator7.2.4 Matching7.3 Regression discontinuity7.3.1 Sharp RDD7.3.2 Challenges to identification7.3.3 Extensions and variations7.4 A switching regression model7.5 Instrumental variablesExtensionsShock-based instruments7.6 Difference-in-differences7.6.1 The basic difference-in-differences approach7.6.2 ExtensionsBibliography
Subject Index

Overview

Financial data are typically characterised by a time-series and cross-sectional dimension. Accordingly, econometric modelling in finance requires appropriate attention to these two – or occasionally more than two – dimensions of the data. Panel data techniques are developed to do exactly this. This book provides an overview of commonly applied panel methods for financial applications, including popular techniques such as Fama-MacBeth estimation, one-way, two-way and interactive fixed effects, clustered standard errors, instrumental variables, and difference-in-differences.

Panel Methods for Finance: A Guide to Panel Data Econometrics for Financial Applications by Marno Verbeek offers the reader:

Focus on panel methods where the time dimension is relatively small

A clear and intuitive exposition, with a focus on implementation and practical relevance

Concise presentation, with many references to financial applications and other sources

Focus on techniques that are relevant for and popular in empirical work in finance and accounting

Critical discussion of key assumptions, robustness, and other issues related to practical implementation

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