Econometric Modeling

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.

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

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

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

Summary

Author Biography

Table of Contents

Supplemental Materials