{"product_id":"econometric-modeling-9780691130897","title":"Econometric Modeling","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. Focusing on modeling, this book aims to give students the statistical foundations of estimation and inference, and also presents a thorough understanding of econometric techniques.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Hendry and Nielsen's somewhat unusual data-driven approach works well...providing genuine insights at a reasonably advanced level.\"--John Hudson, Times Higher Education \"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\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface ix  Data and software xi      Chapter 1: The Bernoulli model 1  1.1 Sample and population distributions 1  1.2 Distribution functions and densities 4  1.3 The Bernoulli model 6  1.4 Summary and exercises 12      Chapter 2: Inference in the Bernoulli model 14  2.1 Expectation and variance 14  2.2 Asymptotic theory 19  2.3 Inference 23  2.4 Summary and exercises 26      Chapter 3: A first regression model 28  3.1 The US census data 28  3.2 Continuous distributions 29  3.3 Regression model with an intercept 32  3.4 Inference 38  3.5 Summary and exercises 42      Chapter 4: The logit model 47  4.1 Conditional distributions 47  4.2 The logit model 52  4.3 Inference 58  4.4 Mis-specification analysis 61  4.5 Summary and exercises 63      Chapter 5: The two-variable regression model 66  5.1 Econometric model 66  5.2 Estimation 69  5.3 Structural interpretation 76  5.4 Correlations 78  5.5 Inference 81  5.6 Summary and exercises 85      Chapter 6: The matrix algebra of two-variable regression 88  6.1 Introductory example 88  6.2 Matrix algebra 90  6.3 Matrix algebra in regression analysis 94  6.4 Summary and exercises 96      Chapter 7: The multiple regression model 98  7.1 The three-variable regression model 98  7.2 Estimation 99  7.3 Partial correlations 104  7.4 Multiple correlations 107  7.5 Properties of estimators 109  7.6 Inference 110  7.7 Summary and exercises 118      Chapter 8: The matrix algebra of multiple regression 121  8.1 More on inversion of matrices 121  8.2 Matrix algebra of multiple regression analysis 122  8.3 Numerical computation of regression estimators 124  8.4 Summary and exercises 126      Chapter 9: Mis-specification analysis in cross sections 127  9.1 The cross-sectional regression model 127  9.2 Test for normality 128  9.3 Test for identical distribution 131  9.4 Test for functional form 134  9.5 Simultaneous application of mis-specification tests 135  9.6 Techniques for improving regression models 136  9.7 Summary and exercises 138      Chapter 10: Strong exogeneity 140  10.1 Strong exogeneity 140  10.2 The bivariate normal distribution 142  10.3 The bivariate normal model 145  10.4 Inference with exogenous variables 150  10.5 Summary and exercises 151      Chapter 11: Empirical models and modeling 154  11.1 Aspects of econometric modeling 154  11.2 Empirical models 157  11.3 Interpreting regression models 161  11.4 Congruence 166  11.5 Encompassing 169  11.6 Summary and exercises 173      Chapter 12: Autoregressions and stationarity 175  12.1 Time-series data 175  12.2 Describing temporal dependence 176  12.3 The first-order autoregressive model 178  12.4 The autoregressive likelihood 179  12.5 Estimation 180  12.6 Interpretation of stationary autoregressions 181  12.7 Inference for stationary autoregressions 187  12.8 Summary and exercises 188      Chapter 13: Mis-specification analysis in time series 190  13.1 The first-order autoregressive model 190  13.2 Tests for both cross sections and time series 190  13.3 Test for independence 192  13.4 Recursive graphics 195  13.5 Example: finding a model for quantities of fish 197  13.6 Mis-specification encompassing 200  13.7 Summary and exercises 201      Chapter 14: The vector autoregressive model 203  14.1 The vector autoregressive model 203  14.2 A vector autoregressive model for the fish market 205  14.3 Autoregressive distributed-lag models 213  14.4 Static solutions and equilibrium-correction forms 214  14.5 Summary and exercises 215      Chapter 15: Identification of structural models 217  15.1 Under-identified structural equations 217  15.2 Exactly-identified structural equations 222  15.3 Over-identified structural equations 227  15.4 Identification from a conditional model 231  15.5 Instrumental variables estimation 234  15.6 Summary and exercises 237      Chapter 16: Non-stationary time series 240  16.1 Macroeconomic time-series data 240  16.2 First-order autoregressive model and its analysis 242  16.3 Empirical modeling of UK expenditure 243  16.4 Properties of unit-root processes 245  16.5 Inference about unit roots 248  16.6 Summary and exercises 252      Chapter 17: Cointegration 254  17.1 Stylized example of cointegration 254  17.2 Cointegration analysis of vector autoregressions 255  17.3 A bivariate model for money demand 258  17.4 Single-equation analysis of cointegration 267  17.5 Summary and exercises 268      Chapter 18: Monte Carlo simulation experiments 270  18.1 Monte Carlo simulation 270  18.2 Testing in cross-sectional regressions 273  18.3 Autoregressions 277  18.4 Testing for cointegration 281  18.5 Summary and exercises 285      Chapter 19: Automatic model selection 286  19.1 The model 286  19.2 Model formulation and mis-specification testing 287  19.3 Removing irrelevant variables 288  19.4 Keeping variables that matter 290  19.5 A general-to-specific algorithm 292  19.6 Selection bias 293  19.7 Illustration using UK money data 298  19.8 Summary and exercises 300      Chapter 20: Structural breaks 302  20.1 Congruence in time series 302  20.2 Structural breaks and co-breaking 304  20.3 Location shifts revisited 307  20.4 Rational expectations and the Lucas critique 308  20.5 Empirical tests of the Lucas critique 311  20.6 Rational expectations and Euler equations 315  20.7 Summary and exercises 319      Chapter 21: Forecasting 323  21.1 Background 323  21.2 Forecasting in changing environments 326  21.3 Forecasting from an autoregression 327  21.4 A forecast-error taxonomy 332  21.5 Illustration using UK money data 337  21.6 Summary and exercises 340      Chapter 22: The way ahead 342      References 345  Author index 357  Subject index 359","brand":"Princeton University Press","offers":[{"title":"Default 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