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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.
Industry Reviews
| 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. |
ISBN: 9780691130897
ISBN-10: 0691130892
Published: 5th June 2007
Format: Paperback
Language: English
Number of Pages: 384
Audience: College, Tertiary and University
Publisher: Princeton University Press
Country of Publication: US
Dimensions (cm): 25.4 x 17.7 x 254
Weight (kg): 0.65
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