Mittnik System-Theoretic Methods in Economic Modelling II

1. Auflage 2014
ISBN: 978-1-4832-9623-4
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band Volume 22, 216 Seiten, Web PDF

Reihe: International Series in Modern Applied Mathematics and Computer Science

System-Theoretic Methods in Economic Modelling II complements the editor's earlier volume, bringing together current research efforts integrating system-theoretic concepts with economic modelling processes. The range of papers presented here goes beyond the long-accepted control-theoretic contributions in dynamic optimization and focuses on system-theoretic methods in the construction as well as the application stages of economic modelling. This volume initiates new and intensifies existing debate between researchers and practitioners within and across the disciplines involved, with the objective of encouraging interdisciplinary research. The papers are split into four sections - estimation, filtering and smoothing problems in the context of state space modelling; applying the state space concept to financial modelling; modelling rational expectation; and a miscellaneous section including a follow-up case study by Tse and Khilnani on their integrated system model for a fishery management process, which featured in the first volume.

Autoren/Hrsg.

Mittnik, S.

Inhaltsverzeichnis

1;Front Cover;1
2;System-Theoretic Methods in Economic Modelling II;4
3;Copyright Page;5
4;Table of Contents;6
5;Preface;8
6;Chapter 1. A systems approach to recursive economic forecasting and seasonal adjustment;10
6.1;1. INTRODUCTION;10
6.2;2. THE COMPONENT TIME-SERIES MODEL;11
6.3;3. THE RECURSIVE FORECASTING AND SMOOTHING ALGORITHMS;16
6.4;4. IDENTIFICATION AND ESTIMATION OF THE COMPONENT MODELS;18
6.5;5. THE SPECTRAL PROPERTIES OF THE SMOOTHING ALGORITHMS;18
6.6;6. IDENTIFICATION AND ESTIMATION OF THE DETRENDED DATA;20
6.7;7. A PRACTICAL EXAMPLE: ANALYSIS OF THE LEAVERS DATA;22
6.8;8. CONCLUSIONS;28
6.9;REFERENCES;28
6.10;APPENDIX;29
7;Chapter 2. Non-Gaussian seasonal adjustment;32
7.1;1. INTRODUCTION;32
7.2;2. NON-GAUSSIAN SEASONAL ADJUSTMENT MODEL AND STATE ESTIMATION;33
7.3;3. NUMERICAL REALIZATION OF THE FORMULAS BY GAUSSIAN MIXTURE APPROXIMATION;34
7.4;4. REMARKS ON THE GAUSSIAN MIXTURE APPROXIMATION;35
7.5;5. NUMERICAL EXAMPLES AND DISCUSSION;37
7.6;6. CONCLUDING REMARKS;42
7.7;REFERENCES;43
8;Chapter 3. Filtering and smoothing algorithms for state space models;44
8.1;1. INTRODUCTION;44
8.2;2. THE STATE SPACE MODEL;45
8.3;3. THE KALMAN FILTER: THE NONDIFFUSE CASE;47
8.4;4. SMOOTHING: THE NONDIFFUSE CASE;48
8.5;5. KALMAN FILTER: PARTIALLY DIFFUSE INITIAL CONDITIONS;50
8.6;6. SMOOTHING: THE PARTIALLY DIFFUSE CASE;53
8.7;REFERENCES;56
9;Chapter 4. State-space approximation of multi-input multi-output systems with stochastic exogenous inputs;58
9.1;1. INTRODUCTION;58
9.2;2. BALANCED STATE-SPACE REALIZATIONS AND APPROXIMATIONS;58
9.3;3. QUASI-BALANCED STATE-SPACE APPROXIMATIONS FROM SAMPLES;62
9.4;4. APPROXIMATED QUASI-BALANCED REALIZATIONS OF TWO MACROECONOMIC EQUATIONS;63
9.5;5. CONCLUSIONS;67
9.6;REFERENCES;67
10;Chapter 5. Analytic derivatives for estimation of linear dynamic models;68
10.1;1. INTRODUCTION;68
10.2;2. STATE-SPACE FORM OF A LINEAR DYNAMIC MODEL;69
10.3;3. EXACT GAUSSIAN LOG-LIKELIHOOD FUNCTION;72
10.4;4. EXACT GRADIENT OF THE LOG-LIKELIHOOD FUNCTION;74
10.5;5. APPROXIMATEHESSIAN OF THE LOG-LIKELIHOOD FUNCTION;76
10.6;6. SAMPLE AND ASYMPTOTIC INFORMATION MATRICES;78
10.7;7. CONCLUDING REMARKS;80
10.8;REFERENCES;80
10.9;APPENDIX;82
11;Chapter 6. A state space model of the economic fundamentals;84
11.1;1. INTRODUCTION;84
11.2;2. A SIMPLE GENERAL EQUILIBRIUM MODEL;85
11.3;3. EMPIRICAL EVIDENCE;89
11.4;4. SUMMARY;92
11.5;REFERENCES;92
11.6;APPENDIX;93
12;Chapter 7. A dynamic view of the portfolio efficiency frontier;94
12.1;1. INTRODUCTION;94
12.2;2. INTERTEMPORAL EFFICIENCY FRONTIER;94
12.3;3. ROBUSTNESS ASPECTS;97
12.4;4. EFFICIENCY OF ALTERNATIVE ESTIMATORS;99
12.5;5. USING THE BOX–COX TRANSFORMATION;106
12.6;REFERENCES;108
13;Chapter 8. State space methods in asset pricing;110
13.1;1. INTRODUCTION;110
13.2;2. THE STATIC ARBITRAGE PRICING MODEL;110
13.3;3. A DYNAMIC FACTOR MODEL;113
13.4;4. EMPIRICAL RESULTS;116
13.5;5. CONCLUSIONS;118
13.6;REFERENCES;118
13.7;APPENDIX;119
14;Chapter 9. Some thoughts on rational expectations models, and alternate formulations;120
14.1;1. INTRODUCTION;120
14.2;2. THE SOLUTION TO THE POLICY OPTIMIZATION PROBLEM: THE PERFECT MEASUREMENT CASE;122
14.3;3. THE NOISY MEASUREMENT CASE;126
14.4;4. A DIRECT SOLUTION TO THE RATIONAL EXPECTATIONS MODEL;128
14.5;5. EXTENSIONS;130
14.6;REFERENCES;132
14.7;APPENDIX A;132
14.8;APPENDIX B;133
15;Chapter 10. The solution of dynamic linear rational expectations models;134
15.1;1. INTRODUCTION;134
15.2;2. THE WHITE NOISE CASE;134
15.3;3. THE MA (1) CASE;136
15.4;4 . SOME EXAMPLES;142
15.5;5. ON THE OPTIMAL CHOICE OF K;143
15.6;6. CONCLUSIONS;144
15.7;REFERENCES;145
16;Chapter 11. Controllability of economic systems under alternative expectations hypotheses—the discrete case;146
16.1;1. INTRODUCTION;146
16.2;2. CONTROLLABILITY CONCEPTS IN THE THEORY OF POLICY;147
16.3;3. THE SHORT-RUN MODEL;149
16.4;4. THE LONG-RUN MODEL;151
16.5;5. THE CHALLENGE OF THE RATIONAL EXPECTATIONS THEORY;153
16.6;6. CONCLUSIONS;155
16.7;REFERENCES;155
16.8;APPENDIX;155
17;Chapter 12. Extensions of linearisation to large econometric models with rational expectations;158
17.1;1. INTRODUCTION;158
17.2;2. LINEAR REDUCTIONS OF LARGE MODELS AND THE ISSUE OF EXPECTATIONS;159
17.3;3. A METHOD APPROXIMATING STEP MULTIPLIERS;162
17.4;4. THE TREATMENT OF CONSISTENT EXPECTATIONS;164
17.5;5. AN APPLICATION TO THE LBS MODEL;167
17.6;6. SYNOPSIS;171
17.7;REFERENCES;171
18;Chapter 13. Remarks on equilibrium state achievement in state space control;172
18.1;1. INTRODUCTION;172
18.2;2. FEEDFORWARD CONTROLLER;173
18.3;3. TWO ALGEBRAIC SOLUTIONS;174
18.4;4. APPLICATION OF SIMULATED STEP RESPONSES;176
18.5;5. CONCLUSIONS;178
18.6;REFERENCES;178
19;Chapter 14. Optimal feedback stabilization policy with asymmetric loss functions;180
19.1;1. INTRODUCTION;180
19.2;2. STATE-SPACE MODEL AND THE TRACKING PROBLEM;181
19.3;3. ASYMMETRIC LOSS CRITERION;186
19.4;4. IMPLEMENTABLE ALGORITHM FOR STABILIZATION PROBLEM WITH ASYMMETRIC LOSS;187
19.5;5. FINAL TIME SELECTION;189
19.6;6. STOCHASTIC ECONOMY MODELS;190
19.7;7. CONCLUSIONS;190
19.8;REFERENCES;191
20;Chapter 15. Globally optimal paths in the nonclassical growth model;192
20.1;1. INTRODUCTION;192
20.2;2. THE MODEL AND LOCAL OPTIMALITY CONDITIONS;193
20.3;3. CHARACTERIZATION OF GLOBAL OPTIMALITY;196
20.4;4. AN EXAMPLE;198
20.5;5. ASYMPTOTIC PROPERTIES OF GLOBALLY OPTIMAL PATHS AND CONCLUSION;201
20.6;REFERENCES;201
20.7;APPENDIX;201
21;Chapter 16. An integrated system model for a fishery management process—II. A case study;204
21.1;1. INTRODUCTION;204
21.2;2. MODEL STRUCTURE AND EQUATIONS;204
21.3;3. SIMULATION OF A BASE CASE;209
21.4;4. SIMULATING POLICY OPTIONS;212
21.5;5. OTHER ISSUES IN FISHERY MANAGEMENT;215
21.6;6. APPLICATION EXPERIENCES AND CONCLUDING REMARKS;217
21.7;REFERENCES;218