7 applied time series econometrics PETER c b PHILLIPS

Applied Time Series Econometrics Time series econometrics is a rapidly evolving field In particular, the cointegration revolution has had a substantial impact on applied analysis As a consequence of the fast pace of development, there are no textbooks that cover the full range of methods in current use and explain how to proceed in applied domains This gap in the literature motivates the present volume The methods are sketched out briefly to remind the reader of the ideas underlying them and to give sufficient background for empirical work The volume can be used as a textbook for a course on applied time series econometrics The coverage of topics follows recent methodological developments Unit root and cointegration analysis play a central part Other topics include structural vector autoregressions, conditional heteroskedasticity, and nonlinear and nonparametric time series models A crucial component in empirical work is the software that is available for analysis New methodology is typically only gradually incorporated into the existing software packages Therefore a flexible Java interface has been created that allows readers to replicate the applications and conduct their own analyses Helmut Lăutkepohl is Professor of Economics at the European University Institute in Florence, Italy He is on leave from Humboldt University, Berlin, where he has been Professor of Econometrics in the Faculty of Economics and Business Administration since 1992 He had previously been Professor of Statistics at the University of Kiel (1987–92) and the University of Hamburg (1985–87) and was Visiting Assistant Professor at the University of California, San Diego (198485) Professor Lăutkepohl is Associate Editor of Econometric Theory, the Journal of Applied Econometrics, Macroeconomic Dynamics, Empirical Economics, and Econometric Reviews He has published extensively in learned journals and books and is author, coauthor and editor of several books on econometrics and time series analysis Professor Lăutkepohl is the author of Introduction to Multiple Time Series Analysis (1991) and a Handbook of Matrices (1996) His current teaching and research interests include methodological issues related to the study of nonstationary, integrated time series, and the analysis of the transmission mechanism of monetary policy in the euro area Markus Krăatzig is a doctoral student in the Department of Economics at Humboldt University, Berlin Themes in Modern Econometrics Managing Editor PETER C.B PHILLIPS, Yale University Series Editors ERIC GHYSELS, University of North Carolina, Chapel Hill RICHARD J SMITH, University of Warwick Themes in Modern Econometrics is designed to service the large and growing need for explicit teaching tools in econometrics It will provide an organized sequence of textbooks in econometrics aimed squarely at the student population and will be the first series in the discipline to have this as its express aim Written at a level accessible to students with an introductory course in econometrics behind them, each book will address topics or themes that students and researchers encounter daily Although each book will be designed to stand alone as an authoritative survey in its own right, the distinct emphasis throughout will be on pedagogic excellence Titles in the Series Statistics and Econometric Models: Volumes and CHRISTIAN GOURIEROUX and ALAIN MONFORT Translated by QUANG VOUNG Time Series and Dynamic Models CHRISTIAN GOURIEROUX and ALAIN MONFORT Translated and edited by GIAMPIERO GALLO Unit Roots, Cointegration, and Structural Change G.S MADDALA and IN-MOO KIM Generalized Method of Moments Estimation ´ ´ MATY ´ ´ O AS Edited by LASZL Nonparametric Econometrics ADRIAN PAGAN and AMAN ULLAH Econometrics of Qualitative Dependent Variables CHRISTIAN GOURIEROUX Translated by PAUL B KLASSEN The Econometric Analysis of Seasonal Time Series ERIC GHYSELS and DENISE R OSBORN Semiparametric Regression for the Applied Econometrician ADONIS YATCHEW APPLIED TIME SERIES ECONOMETRICS Edited by ă HELMUT LUTKEPOHL European University Institute, Florence ă MARKUS KRATZIG Humboldt University, Berlin cambridge university press Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo Cambridge University Press The Edinburgh Building, Cambridge cb2 2ru, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521839198 © Cambridge University Press 2004 This publication is in copyright Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press First published in print format 2004 isbn-13 isbn-10 978-0-511-21739-5 eBook (NetLibrary) 0-511-21739-0 eBook (NetLibrary) isbn-13 isbn-10 978-0-521-83919-8 hardback 0-521-83919-x hardback isbn-13 isbn-10 978-0-521-54787-1 paperback 0-521-54787-3 paperback Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate HL To my delightful wife, Sabine MK To my parents Contents Preface Notation and Abbreviations List of Contributors Initial Tasks and Overview Helmut Lăutkepohl 1.1 1.2 1.3 1.4 1.5 Introduction Setting Up an Econometric Project Getting Data Data Handling Outline of Chapters Univariate Time Series Analysis Helmut Lăutkepohl 2.1 Characteristics of Time Series 2.2 Stationary and Integrated Stochastic Processes 2.2.1 Stationarity 2.2.2 Sample Autocorrelations, Partial Autocorrelations, and Spectral Densities 2.2.3 Data Transformations and Filters 2.3 Some Popular Time Series Models 2.3.1 Autoregressive Processes 2.3.2 Finite-Order Moving Average Processes 2.3.3 ARIMA Processes 2.3.4 Autoregressive Conditional Heteroskedasticity 2.3.5 Deterministic Terms 2.4 Parameter Estimation 2.4.1 Estimation of AR Models 2.4.2 Estimation of ARMA Models 2.5 Model Specification page xv xix xxv 1 5 8 11 11 12 17 22 22 25 27 28 30 30 30 32 33 ix ... hardback 0-521-83919-x hardback isbn-13 isbn-10 978 -0-521-5 478 7-1 paperback 0-521-5 478 7-3 paperback Cambridge University Press has no responsibility for the persistence or accuracy of urls for external... that the time series are generated by stochastic processes Roughly speaking, a stochastic process is a collection of random variables Each time series observation is assumed to be generated by a... a specific stationary stochastic process consisting of serially uncorrelated random variables Because most economic time series exhibit serial correlation, such a model is often insufficient