Unobserved. Components and. Time Series. Econometrics. Edited by. Siem Jan Koopman. and Neil Shephard OXFORD UNIVERSITY PRESS
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1 Unobserved Components and Time Series Econometrics Edited by Siem Jan Koopman and Neil Shephard OXFORD UNIVERSITY PRESS
2 CONTENTS LIST OF FIGURES LIST OF TABLES ix XV 1 Introduction 1 Siem Jan Koopman and Neil Shephard 1.1 An overview of the volume Andrew Harvey's main contributions 5 2 The development of a time series methodology: from recursive residual; to dynamic conditional score models 10 Andrew Harvey 3 A state-dependent model for Inflation forecasting 14 Andrea Stella and James H. Stock 3.1 Introduction The model Estimation strategy Data description and empirical results Condusions 28 4 Measuring the tracking error of exchange traded funds 30 Giuliano De Rossi 4.1 Introduction ETF tracking error in the existing literature An unobserved components model Empirical analysis Condusions 43 5 Measuring the dynamics of global business cyde connectedness 45 Francis X. Diebold and Kami! Yilmaz 5.1 Introduction Measuring connectedness Global business cycle connectedness Concluding remarks 69
3 6 Inferring and predicting global temperature trends 71 Craig Ansley and Piet de Jong 6.1 Introduction Data background Smoothirig approaches based on the Butterworth filter Butterworth filter and minimum variance trend estimation Variants and fits of the Butterworth model Joint Butterworth model Conclusions 88 7 Forecasting the Boat Race 90 Geert Mesters and Siem Jan Koopman 7.1 Introduction Models and parameter estimation A forty year forecasting assessment Conclusion Tests for serial dependence in static, non-gaussian factor models 118 Gabriele Fiorentini and Enrique Sentana 8.1 Introduction Static factor models Serial correlation tests for common and idiosyncratic factors Tests for ARCH effects in common and idiosyncratic factors Joint tests for serial dependence Monte Carlo analysis Empirical application Conclusions and extensions Appendix: Proofs Appendix: Local power calculations Inference for models with asymmetric a-stable noise processes 190 Tatjana Lemke and Simon J. Godsill 9.1 Introduction The a-stable distribution General scheme based upon conditional Gaussians Discrete-time state-space models Continuous-time state-space models Conclusions Appendix: Gaussian approximation of moments 211
4 Contents v# 9.8 Appendix: Modified Poisson series representation Appendix: Rao-Blackwellized particle filter for State estimation Martingale unobserved component models 218 Neil Shephard 10.1 Introduction Martingale unobserved component models Conditional properties Particle filter based analysis Illustration using Inflation data Conclusion More is not always better: Kaiman filtering in dynamic factor models 250 PHar Poncela and Esther Ruiz 11.1 Introduction Dynamic factor model Known parameters: filtering uncertainty Estimated parameters Condusions Appendix: Proof of Lemmas On detecting end-of-sample instabilities 272 Fabio Busetti 12.1 Introduction End-of-sample instability tests in a linear regression model Size and power properties of the tests Empirical illustrations Condusions Improved frequentist prediction intervals for autoregressive models by Simulation 291 Jouni Helske and Jukka Nyblom 13.1 Introduction Motivation Predictive distributions Priors Simulation experiments Annual gross domestic product growth Discussion 308
5 viii 14 The superiority of the LM test in a class of econometric models where the Wald test performs poorly 310 Jun Ma and Charles R. Nelson 14.1 Introduction The modified LM test as an approximation to a test that is exact in finite samples Small sample Performance of the modified LM test in four models Summary and conclusions Appendix: The linear regression case Appendix: The ARMA (1,1) case Appendix: The ARMA (p,q) case Generalized linear spectral models 331 Tommaso Proietti and Alessandra Luati 15.1 Introduction Generalized spectrum and autocovariances Direct Whittle estimation of the generalized autocovariances Spectral ARMA models Empirical applications Conclusions 346 BIBLIOGRAPHY INDEX
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