Empirical model discovery and theory evaluation : automatic selection methods in econometrics / David F. Hendry and Jurgen A. Doornik.

Format:

Book

Author/Creator:

Hendry, David F., author.

Doornik, Jurgen A., author.

Series:

Arne Ryde memorial lectures

Arne Ryde Memorial Lectures Series

Language:

English

Subjects (All):

Econometrics--Computer programs.

Econometrics.

Econometrics--Methodology.

Physical Description:

1 online resource (xxviii, 358 pages) : illustrations, tables, graphs, charts.

Place of Publication:

Cambridge, Massachusetts ; London, England : The MIT Press, [2014]

System Details:

text file

Summary:

A synthesis of the authors' groundbreaking econometric research on automatic model selection, which uses powerful computational algorithms and theory evaluation.

Contents:

I Principles of Model Selection

1 Introduction 3

1.1 Overview 4

1.2 Why automatic methods? 6

1.3 The route ahead 8

2 Discovery 17

2.1 Scientific discovery 17

2.2 Evaluating scientific discoveries 20

2.3 Common aspects of scientific discoveries 21

2.4 Discovery in economics 22

2.5 Empirical model discovery in economics 25

3 Background to Automatic Model Selection 31

3.1 Critiques of data-based model selection 32

3.2 General-to-specific (Gets) modeling 33

3.3 What to include? 34

3.4 Single-decision selection 35

3.5 Impact of selection 36

3.6 Autometrics 38

3.7 Mis-specification testing 39

3.8 Parsimonious encompassing 40

3.9 Impulse-indicator saturation (IIS) 40

3.10 Integration and cointegration 41

3.11 Selecting lag length 43

3.12 Collinearity 44

3.13 Retaining economic theory 46

3.14 Functional form 49

3.15 Exogeneity 51

3.16 Selecting forecasting models 51

3.17 Progressive research strategies 52

3.18 Evaluating the reliability of the selected model 53

3.19 Data accuracy 54

3.20 Summary 55

4 Empirical Modeling Illustrated 57

4.1 The artificial DGP 57

4.2 A simultaneous equations model 58

4.3 Illustrating model selection concepts 61

4.4 Modeling the artificial data consumption function 62

4.5 Summary 69

5 Evaluating Model Selection 71

5.1 Introduction 71

5.2 Judging the success of selection algorithms 73

5.3 Maximizing the goodness of fit 75

5.4 High probability of recovery of the LDGP 76

5.5 Improved inference about parameters of interest 77

5.6 Improved forecasting 78

5.7 Working well for realistic LDGPs 78

5.8 Matching a theory-derived specification 79

5.9 Recovering the LDGP starting from the GUM or the LDGP 81

5.10 Operating characteristics 82

5.11 Finding a congruent undominated model of the LDGP 83

5.12 Our choice of evaluation criteria 83

6 The Theory of Reduction 85

6.1 Introduction 85

6.2 From DGP to LDGP 87

6.3 From LDGP to GUM 90

6.4 Formulating the GUM 92

6.5 Measures of no information loss 94

6.6 Summary 95

7 General-to-specific Modeling 97

7.1 Background 97

7.2 A brief history of Gets 99

7.3 Specification of the GUM 101

7.4 Checking congruence 102

7.5 Formulating the selection criteria 104

7.6 Selection under the null 104

7.7 Keeping relevant variables 106

7.8 Repeated testing 107

7.9 Estimating the GUM 108

7.10 Instrumental variables 109

7.11 Path searches 110

7.12 Parsimonious encompassing of the GUM 110

7.13 Additional features 111

7.14 Summarizing Gets model selection 113

II Model Selection Theory and Performance

8 Selecting a Model in One Decision 117

8.1 Why Gets model selection can succeed 117

8.2 Goodness of fit estimates 118

8.3 Consistency of the 1-cut selection 119

8.4 Monte Carlo simulation for N = 1000 120

8.5 Simulating MSE for N = 1000 123

8.6 Non-orthogonal regressors 123

8.7 Orthogonality and congruence 124

9 The 2-variable DGP 127

9.1 Introduction 127

9.2 Formulation 128

9.3 A fixed non-zero alternative 129

9.4 A fixed zero alternative 130

9.5 A local alternative 130

9.6 Interpreting non-uniform convergence 130

9.7 An alternative interpretation 132

10 Bias Correcting Selection Effects 133

10.1 Background 133

10.2 Bias correction after selection 134

10.3 Impact of bias correction on MSE 137

10.4 Interpreting the outcomes 138

11 Comparisons of 1-cut Selection with Autometrics 141

11.1 Introduction 141

11.2 Autometrics 142

11.3 Tree search 144

11.4 The impact of sequential search 146

11.5 Monte Carlo experiments for N = 10 147

11.6 Gauge and potency 147

11.7 Mean squared errors 149

11.8 Integrated data 150

12 Impact of Diagnostic Tests 151

12.1 Model evaluation criteria 151

12.2 Selection effects on mis-specification tests 152

12.3 Simulating Autometrics with diagnostic tracking 156

12.4 Impact of diagnostic tracking on MSE 157

12.5 Integrated data 158

13 Role of Encompassing 159

13.1 Introduction 159

13.2 Parsimonious encompassing 160

13.3 Encompassing the GUM 161

13.4 Iteration and encompassing 165

14 Retaining a Theory Model During Selection 167

14.1 Introduction 167

14.2 Selection when retaining a valid theory 168

14.3 Decision rules for rejecting a theory model 170

14.4 Rival theories 172

14.5 Implications 172

15 Detecting Outliers and Breaks Using IIS 175

15.1 Introduction 175

15.2 Theory of impulse-indicator saturation 177

15.3 Sampling distributions 180

15.4 Dynamic generalizations 181

15.5 Impulse-indicator saturation in Autometrics 182

15.6 IIS in a fat-tailed distribution 183

15.7 Potency for a single outlier 186

15.8 Location shift example 188

15.9 Impulse-indicator saturation simulations 192

16 Re-modeling UK Real Consumers' Expenditure 195

16.1 Introduction 195

16.2 Replicating DHSY 197

16.3 Selection based on Autometrics 198

16.4 Tests of DHSY 201

17 Comparisons of Autometrics with Other Approaches 203

17.1 Introduction 203

17.2 Monte Carlo designs 204

17.3 Re-analyzing the Hoover-Perez experiments 208

17.4 Comparing with step-wise regression 210

17.5 Information criteria 212

17.6 Lasso 215

17.7 Comparisons with RETINA 219

18 Model Selection in Underspecified Settings 223

18.1 Introduction 223

18.2 Analyzing under specification 224

18.3 Model selection for mitigating underspecification 225

18.4 Underspecification in a dynamic DGP 228

18.5 A dynamic artificial-data example 229

III Variables than Observations 233

19.1 Introduction 233

19.2 Autometrics expansion and reduction steps 234

19.3 Simulation evaluation of alternative block modes 235

19.4 Hoover-Perez experiments with N > T 237

19.5 Small samples with N > T 238

19.6 Modeling N > T in practice 239

19.7 Retaining a theory when k + n ≤ T 240

20 Impulse-indicator Saturation for Multiple Breaks 243

20.1 Impulse-indicator saturation experiments 243

20.2 IIS for breaks in the mean of a location-scale model 244

20.3 IIS for shifts in the mean of a stationary autoregression 246

20.4 IIS in unit-root models 247

20.5 IIS in autoregressions with regressors 249

21 Selecting Non-linear Models 253

21.1 Introduction 253

21.2 The non-linear formulation 255

21.3 Non-linear functions 256

21.4 The non-linear algorithm 256

21.5 A test-based strategy 257

21.6 Problems in directly selecting non-linear models 258

22 Testing Super Exogeneity 263

22.1 Background 263

22.2 Formulation of the statistical system 265

22.3 The conditional model 266

22.4 The test procedure 268

22.5 Monte Carlo evidence on null rejection frequencies 269

22.6 Non-null rejection frequency 270

22.7 Simulating the potency of the super-exogeneity test 272

22.8 Power of the optimal infeasible test 272

22.9 Testing exogeneity in DHSY 273

22.10 IIS and economic interpretations 276

23 Selecting Forecasting Models 279

23.1 Introduction 279

23.2 Finding good forecasting models 282

23.3 Prior specification then estimation 283

23.4 Conventional model selection 284

23.5 Model averaging 286

23.6 Factor models 290

23.7 Selecting factors and variables jointly 291

23.8 Using econometric models for forecasting 292

23.9 Robust forecasting devices 293

23.10 Using selected models for forecasting 296

23.11 Some simulation findings 297

23.12 Public-service case study 300

23.13 Improving data accuracy at the forecast origin 302

23.14 Conclusions 307

24 Epilogue 309

24.1 Summary 309

24.2 Implications 314

24.3 The way ahead 315.

Notes:

"Arne Ryde Memorial Lectures Series."

OCLC-licensed vendor bibliographic record.

ISBN:

9780262324410

0262324415

1306957869

9781306957861

OCLC:

889301867

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Restricted for use by site license.