Econometric Modeling: A Likelihood Approach / Edition 1

by David F. Hendry, Bent Nielsen

Overview

Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques.


David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied.



Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research.

Product Details

ISBN-13:	9780691130897
Publisher:	Princeton University Press
Publication date:	03/25/2007
Edition description:	New Edition
Pages:	384
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

David F. Hendry is Professor of Economics at the University of Oxford and a Fellow of Nuffield College. Bent Nielsen is Reader in Econometrics at the University of Oxford and a Fellow of Nuffield College

Table of Contents

Preface     ix
Data and software     xi
The Bernoulli model     1
Sample and population distributions     1
Distribution functions and densities     4
The Bernoulli model     6
Summary and exercises     12
Inference In the Bernoulli model     14
Expectation and variance     14
Asymptotic theory     19
Inference     23
Summary and exercises     26
A first regression model     28
The US census data     28
Continuous distributions     29
Regression model with an intercept     32
Inference     38
Summary and exercises     42
The logit model     47
Conditional distributions     47
The logit model     52
Inference     58
Mis-specification analysis     61
Summary and exercises     63
The two-variable regression model     66
Econometric model     66
Estimation     69
Structural Interpretation     76
Correlations     78
Inference     81
Summary and exercises     85
The matrix algebra of two-variable regression     88
Introductory example     88
Matrix algebra     90
Matrix algebra in regression analysis     94
Summary and exercises     96
The multiple regression model     98
The three-variable regression model     98
Estimation     99
Partial correlations     104
Multiple correlations     107
Properties of estimators     109
Inference     110
Summary and exercises     118
The matrix algebra of multiple regression     121
More on inversion of matrices     121
Matrix algebra of multiple regression analysis     122
Numerical computation of regression estimators     124
Summary and exercises     126
Mis-specification analysis in cross sections     127
The cross-sectional regression model     127
Test for normality     128
Test for identical distribution     131
Test for functional form     134
Simultaneous application of mis-specification tests     135
Techniques for improving regression models     136
Summary and exercises      138
Strong exogeneity     140
Strong exogeneity     140
The bivariate normal distribution     142
The bivariate normal model     145
Inference with exogenous variables     150
Summary and exercises     151
Empirical models and modeling     154
Aspects of econometric modeling     154
Empirical models     157
Interpreting regression models     161
Congruence     166
Encompassing     169
Summary and exercises     173
Autoregressions and stationarity     175
Time-series data     175
Describing temporal dependence     176
The first-order autoregressive model     178
The autoregressive likelihood     179
Estimation     180
Interpretation of stationary autoregressions     181
Inference for stationary autoregressions     187
Summary and exercises     188
Mis-specification analysis in time series     190
Tine first-order autoregressive model     190
Tests for both cross sections and time series     190
Test for independence     192
Recursive graphics      195
Example: finding a model for quantities of fish     197
Mis-specification encompassing     200
Summary and exercises     201
The vector autoregressive model     203
The vector autoregressive model     203
A vector autoregressive model for the fish market     205
Autoregressive distributed-lag models     213
Static solutions and equilibrium-correction forms     214
Summary and exercises     215
Identification of structural models     217
Under-identified structural equations     217
Exactly-identified structural equations     222
Over-identified structural equations     227
Identification from a conditional model     231
Instrumental variables estimation     234
Summary and exercises     237
Non-stationary time series     240
Macroeconomic time-series data     240
First-order autoregressive model and its analysis     242
Empirical modeling of UK expenditure     243
Properties of unit-root processes     245
Inference about unit roots     248
Summary and exercises     252
Cointegration     254
Stylized example of cointegration      254
Cointegration analysis of vector autoregressions     255
A bivariate model for money demand     258
Single-equation analysis of cointegration     267
Summary and exercises     268
Monte Carlo simulation experiments     270
Monte Carlo simulation     270
Testing in cross-sectional regressions     273
Autoregressions     277
Testing for cointegration     281
Summary and exercises     285
Automatic model selection     286
The model     286
Model formulation and mis-specification testing     287
Removing irrelevant variables     288
Keeping variables that matter     290
A general-to-specific algorithm     292
Selection bias     293
Illustration using UK money data     298
Summary and exercises     300
Structural breaks     302
Congruence in time series     302
Structural breaks and co-breaking     304
Location shifts revisited     307
Rational expectations and the Lucas critique     308
Empirical tests of the Lucas critique     311
Rational expectations and Euler equations      315
Summary and exercises     319
Forecasting     323
Background     323
Forecasting in changing environments     326
Forecasting from an autoregression     327
A forecast-error taxonomy     332
Illustration using UK money data     337
Summary and exercises     340
The way ahead     342
References     345
Author index     357
Subject index     359

What People are Saying About This

Marius Ooms

This textbook is concise, up-to-date, and largely self-contained. The models it presents are just complicated enough to set out the main econometric ideas.
— Marius Ooms, Free University, Amsterdam

Douglas Steigerwald

Hendry and Nielsen's Econometric Modeling is a well-thought-out alternative to other introductory econometric textbooks. I especially like the decision to treat time-series and cross-section analysis simultaneously, since the dichotomy between them, which arises in most other texts, is artificial.
— Douglas Steigerwald, University of California, Santa Barbara

From the Publisher

"Hendry and Nielsen's Econometric Modeling is a well-thought-out alternative to other introductory econometric textbooks. I especially like the decision to treat time-series and cross-section analysis simultaneously, since the dichotomy between them, which arises in most other texts, is artificial."—Douglas Steigerwald, University of California, Santa Barbara

"This textbook is concise, up-to-date, and largely self-contained. The models it presents are just complicated enough to set out the main econometric ideas."—Marius Ooms, Free University, Amsterdam

Editorial Reviews

"Summing up: A remarkable achievement, a beautiful piece of work, engaging the reader quickly with the subject matter, Econometric Modeling provides a good introduction to the field for aspiring and advanced students and also contains valuable material and hints for experts already well versed in the subject. A must-buy for the library."

Current Engineering Practice

Hendry and Nielsen's somewhat unusual data-driven approach works well...providing genuine insights at a reasonably advanced level.

Times Higher Education - John Hudson

Hendry and Nielsen's somewhat unusual data-driven approach works well...providing genuine insights at a reasonably advanced level.
— John Hudson

Times Higher Education

"Hendry and Nielsen's somewhat unusual data-driven approach works well...providing genuine insights at a reasonably advanced level."—-John Hudson, Times Higher Education

From the Publisher