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Guide to Modern Econometrics

ISBN-10: 0471899828

ISBN-13: 9780471899822

Edition: 2000

Authors: Jos Verbeek, Marno Verbeek

List price: $75.00
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Marno Verbeek2s intermediate level textbook explores a wide range of topics in modern econometrics and focuses on what is important for doing and understanding empirical work. A wide range of new topics are covered including time series analysis, limited dependent variables, cointegration and panel data analysis. FEATURES Concentrates on the intuition behind various techniques and their practical relevance rather than the formulae End of chapter exercises review key concepts in light of empircal examples discussed in the chapter Examples are drawn from a wide variety of fields including labour economics, environmental economics, finance, international economics and macroeconomics CONTENTS: Introduction; An Introduction to Linear Regression; Interpreting and Comparing Regression Models; Heteroskedasticity and Autocorrelation; Stochastic Regressors; Maximum Likelihood Estimation; Models with Limited Dependent Variables; Univariate Time Series Modelling; Multivariate Time Series Analysis; Models Based on Panel Data.
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Book details

List price: $75.00
Copyright year: 2000
Publisher: John Wiley & Sons, Incorporated
Publication date: 6/23/2000
Binding: Paperback
Pages: 400
Size: 6.67" wide x 9.78" long x 0.78" tall
Weight: 1.518
Language: English

About Econometrics
The Structure of this Book
Illustrations and Exercises
An Introduction to Linear Regression
Ordinary Least Squares as An Algebraic Tool
Ordinary Least Squares
Simple Linear Regression
Example: Individual Wages
Matrix Notation
The Linear Regression Model
Small Sample Properties of the OLS Estimator
The Gauss--Markov Assumptions
Properties of the OLS Estimator
Example: Individual Wages (Continued)
Hypothesis Testing
A Simple t-test
Example: Individual Wages (Continued)
Testing One Linear Restriction
A Joint Test of Significance of Regression Coefficients
Example: Individual Wages (Continued)
The General Case
Size, Power and p-values
Asymptotic Properties of the OLS Estimator
Asymptotic Normality
Illustration: the Capital Asset Pricing Model
The CAPM as a Regression Model
Estimating and Testing the CAPM
Example: Individual Wages (Continued)
Interpreting and Comparing Regression Models
Interpreting the Linear Model
Selecting the Set of Regressors
Misspecifying the Set of Regressors
Selecting Regressors
Comparing Non-nested Models
Misspecifying the Functional Form
Nonlinear Models
Testing the Functional Form
Illustration: Explaining House Prices
Illustration: Explaining Individual Wages
Linear Models
Loglinear Models
The Effects of Gender
Some Words of warning
Heteroskedasticity and Autocorrelation
Consequences for the OLS Estimator
Deriving an Alternative Estimator
Estimator Properties and Hypothesis Testing
When the Variances are Unknown
Heteroskedasticity-consistent Standard Errors for OLS
A Model with Two Unknown Variances
Multiplicative Heteroskedasticity
Testing for Heteroskedasticity
Testing Equality of Two Unknown Variances
Testing for Multiplicative Heteroskedasticity
The Breusch--Pagan Test
The White Test
Which Test?
Illustration: Explaining Labour Demand
First Order Autocorrelation
Unknown [rho]
Testing for First Order Autocorrelation
Asymptotic Tests
The Durbin--Watson Test
Illustration: The Demand for Ice Cream
Alternative Autocorrelation Patterns
Higher Order Autocorrelation
Moving Average Errors
What to do When you Find Autocorrelation?
Heteroskedasticity-and-autocorrelation-consistent Standard Errors for OLS
Illustration: Risk Premia in Foreign Exchange Markets
Tests for Risk Premia in the One-month Market
Tests for Risk Premia using Overlapping Samples
Endogeneity, Instrumental Variables and GMM
A Review of the Properties of the OLS Estimator
Cases Where the OLS Estimator Cannot be Saved
Autocorrelation with a Lagged Dependent Variable
An Example with Measurement Error
Simultaneity: the Keynesian Model
The Instrumental Variables Estimator
Estimation with a Single Endogenous Regressor and a Single Instrument
Back to the Keynesian Model
Back to the Measurement Error Problem
Multiple Endogenous Regressors
Illustration: Estimating the Returns to Schooling
The Generalized Instrumental Variables Estimator
Multiple Endogenous Regressors with an Arbitrary Number of Instruments
Two-stage Least Squares and the Keynesian Model Again
The Generalized Method of Moments
The Generalized Method of Moments
Some Simple Examples
Illustration: Estimating Intertemporal Asset Pricing Models
Concluding Remarks
Maximum Likelihood Estimation and Specification Tests
An Introduction to Maximum Likelihood
Some Examples
General Properties
An Example (Continued)
The Normal Linear Regression Model
Specification Tests
Three Test Principles
Lagrange Multiplier Tests
An Example (Continued)
Tests in the Normal Linear Regression Model
Testing for Omitted Variables
Testing for Heteroskedasticity
Testing for Autocorrelation
Quasi-maximum Likelihood and Moment Conditions Tests
Quasi-maximum Likelihood
Conditional Moment Tests
Testing for Normality
Models with Limited Dependent Variables
Binary Choice Models
Using Linear Regession?
Introducing Binary Choice Models
An Underlying Latent Model
Illustration: the Impact of Unemployment Benefits on Recipiency
Specification Tests in Binary Choice Models
Relaxing Some Assumptions in Binary Choice Models
Multi-response Models
Ordered Response Models
About Normalization
Illustration: Willingness to Pay for Natural Areas
Multinomial Models
Tobit Models
The Standard Tobit Model
Illustration: Expenditures on Alcohol and Tobacco (Part 1)
Specification Tests in the Tobit Model
Extensions of Tobit Models
The Tobit II Model
Further Extensions
Illustration: Expenditures on Alcohol and Tobacco (Part 2)
Sample Selection Bias
The Nature of the Selection Problem
Semi-parametric Estimation of the Sample Selection Model
Univariate Time Series Models
Some Examples
Stationarity and the Autocorrelation Function
General ARMA Processes
Formulating ARMA Processes
Invertibility of Lag Polynomials
Common Roots
Stationarity and Unit Roots
Testing for Unit Roots
Testing for Unit Roots in a First Order Autoregressive Model
Testing for Unit Roots in Higher Order Autoregressive Models
Illustration: Quarterly Disposable Income
Illustration: Long-run Purchasing Power Parity (Part 1)
Estimation of ARMA Models
Least Squares
Maximum Likelihood
Choosing a Model
The Autocorrelation Function
The Partial Autocorrelation Function
Diagnostic Checking
Criteria for Model Selection
Illustration: Modelling Quarterly Disposable Income
Predicting with ARMA Models
The Optimal Predictor
Prediction Accuracy
Illustration: The Expectations Theory of the Term Structure
Autoregressive Conditional Heteroskedasticity
ARCH and GARCH Models
Estimation and Prediction
Illustration: Volatility in Daily Exchange Rates
What about Multivariate Models?
Multivariate Time Series Models
Dynamic Models with Stationary Variables
Models with Nonstationary Variables
Spurious Regressions
Cointegration and Error-correction Mechanisms
Illustration: Long-run Purchasing Power Parity (Part 2)
Vector Autoregressive Models
Cointegration: The Multivariate Case
Cointegration in a VAR
Example: Cointegration in a Bivariate VAR
Testing for Cointegration
Illustration: Long-run Purchasing Power Parity (Part 3)
Illustration: Money Demand and Inflation
Concluding Remarks
Models Based on Panel Data
Advantages of Panel Data
Efficiency of Parameter Estimators
Identification of Parameters
The Static Linear Model
The Fixed Effects Model
The Random Effects Model
Fixed Effects or Random Effects?
Alternative Instrumental Variables Estimators
Alternative Error Structures
Testing for Heteroskedasticity and Autocorrelation
Illustration: Explaining Individual Wages
Dynamic Linear Models
An Autoregressive Panel Data Model
Dynamic Models with Exogenous Variables
Unit Roots and Cointegration
Illustration: Wage Elasticities of Labour Demand
Models with Limited Dependent Variables
Binary Choice Models
The Fixed Effects Logit Model
The Random Effects Probit Model
Tobit Models
Dynamics and the Problem of Initial Conditions
Incomplete Panels and Selection Bias
Estimation with Randomly Missing Data
Selection Bias and Some Simple Tests
Estimation with Nonrandomly Missing Data
Vectors and Matrices
Matrix Manipulations
Properties of Matrices and Vectors
Inverse Matrices
Idempotent Matrices
Eigenvalues and Eigenvectors
Some Least Squares Manipulations
Statistical and Distribution Theory
Discrete Random Variables
Continuous Random Variables
Expectations and Moments
Multivariate Distributions
Conditional Distributions
The Normal Distribution
Related Distributions