Econometric Modeling: A Likelihood Approach by David F. HendryEconometric Modeling: A Likelihood Approach by David F. Hendry

Econometric Modeling: A Likelihood Approach

byDavid F. Hendry

Paperback | March 25, 2007

Pricing and Purchase Info

$110.19 online 
$117.00 list price save 5%
Earn 551 plum® points

Prices and offers may vary in store

Quantity:

In stock online

Ships free on orders over $25

Not available in stores

about

Econometric Modelingprovides 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 Modelingis 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. Hendryis Professor of Economics at the University of Oxford and a Fellow of Nuffield College.Bent Nielsenis Reader in Econometrics at the University of Oxford and a Fellow of Nuffield College
Loading
Title:Econometric Modeling: A Likelihood ApproachFormat:PaperbackDimensions:384 pagesPublished:March 25, 2007Publisher:Princeton University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0691130892

ISBN - 13:9780691130897

Look for similar items by category:

Reviews

Table of Contents

Preface 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


Editorial Reviews

"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