The Dynamics of Business Cycles: Stylized Facts, Economic Theory, Econometric Methodology and Applications

ISBN-13: 9783790808230 / Angielski / Miękka / 1994 / 215 str.

M. Reiter; Michael Reiter

Opis

This study is a revised version of my doctoral dissertation at the Economics Department of the University of Munich. I want to take the opportunity to express my gratitude to some people who have helped me in my work. My greatest thanks go to the supervisor of this dissertation, Professor Claude Billinger. Bis ideas have formed the basis of my work. Be permanently sup ported it with a host of ideas, criticism and encouragement. Furthermore, he provided a stimulating research environment at SEMECON. This study would not have been possible in this form without the help of my present and former colleagues at SEMECON. I am indebted to Rudolf Kohne-Volland, Monika Sebold-Bender and Ulrich Woitek for providing soft ware and guidance for the data analysis. Discussions with them and with Thilo Weser have helped me to take many hurdles, particularly in the early stages of the project. My sincere thanks go to them all. I had the opportunity to present a former version of my growth model at a workshop of Professor Klaus Zimmermann. I want to thank all the parti cipants for their helpful comments. I also acknowledge critical and constructive comments from an anonymous referee. Table of Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Part I. Methodology 1. Importance of Stylized Facts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.1 Limitations of statistical testing. . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Evaluating economic models. . . . . . . . . . . . . . . . . . .. . . . 11 . . . . . . 2. Further Methodological Issues . . . . . . . . . . . . . . . . . .. . . . 13 . . . . . ."

Spis treści

I. Methodology.- 1. Importance of Stylized Facts.- 1.1 Limitations of statistical testing.- 1.2 Evaluating economic models.- 2. Further Methodological Issues.- 2.1 Continuous versus discrete time models.- 2.2 Models of cyclical growth versus models of fluctuations.- 2.3 Detrending the data.- 2.4 Annual versus quarterly data.- 2.5 Applying models to more than one country.- II. Business Cycle Stylized Facts.- 3. Stylized Facts: Method.- 3.1 Characterizing deviations from trend: spectral analysis.- 3.2 Spectral estimation: the maximum-entropy spectrum.- 3.3 Cross spectral analysis: interpretation and estimation.- 4. Stylized Facts: Results.- 4.1 Main aggregates of national accounts.- 4.2 Longer series of fixed investment.- 4.3 Private consumption.- 4.4 Nominal variables.- 4.5 Relationship between real and nominal variables.- III. Business Cycle Models.- 5. SOA Models.- 5.1 The SOA of equipment investment.- 5.2 Recent research on inventories.- 5.3 The SOA of production and inventories.- 5.4 Errors in measurement.- 5.5 Empirical results.- 6. Consumption.- 6.1 Two models of consumption.- 6.2 Estimated consumption equations.- 6.3 Complete models.- 6.4 Empirical results.- 6.5 Appendix: computation of permanent income.- 7. Prices and Wages.- 7.1 Introduction.- 7.2 Price and consumption equations.- 7.3 Complete model.- 7.4 Empirical results.- 7.5 Summary: explaining business cycle stylized facts.- IV. Cyclical Growth.- 8. Determinants of Growth.- 8.1 Growth, saving and productivity.- 8.2 Design of the model.- 9. A Real Model of Cyclical Growth.- 9.1 Formulation of the cyclical growth model.- 9.2 Steady state.- 9.3 Deviations from steady state and stability.- 9.4 Time-varying productivity growth.- 9.5 Parameter restrictions and exogenous variables.- 9.6 Empirical results.- 9.7 Appendix I: Linearizations.- 9.8 Appendix II: Linearization error.- V. Continuous Time Econometrics.- 10. Estimating Continuous Time Models.- 10.1 Linear stochastic differential equations.- 10.2 Estimation of a first order system.- 11. The Discrete Kalman Filter.- 11.1 The state space model.- 11.2 The Kalman filter: recursive formulas and ML-estimates.- 11.3 Initialising the Kalman filter.- 12. An Exact Gaussian Estimator for General Linear Continuous Time Models.- 12.1 The exact discrete analogue.- 12.2 Evaluation of integrals.- 12.3 Efficient computation of the filter.- 12.4 Exogenous variables.- 12.5 Fixed-interval smoothing.- 13. Further Topics.- 13.1 Asymptotic properties of the estimators.- 13.2 Sensitivity analysis.- 13.3 A Monte-Carlo study.- 13.4 Spectral densities of continuous time models.- 13.5 Numerical maximization.- 13.6 Partial adjustment equations.- Conclusions.- A. Abbreviations.- B. Data.- References.