APPLIED TIME SERIES ECONOMETRICS

Transcription

1 APPLIED TIME SERIES ECONOMETRICS Edited by HELMUT LÜTKEPOHL European University Institute, Florence MARKUS KRÄTZIG Humboldt University, Berlin CAMBRIDGE UNIVERSITY PRESS

2 Contents Preface Notation and Abbreviations List of Contributors page xv xix xxv 1 Initial Tasks and Overview 1 Helmut Lütkepohl 1.1 Introduction Setting Up an Econometric Project Getting Data Data Handling Outline of Chapters 5 2 Univariate Time Series Analysis 8 Helmut Lütkepohl 2.1 Characteristics of Time Series Stationary and Integrated Stochastic Processes Stationarity Sample Autocorrelations, Partial Autocorrelations, and Spectral Densities Data Transformations and Filters Some Popular Time Series Models Autoregressive Processes Finite-Order Moving Average Processes AR1MA Processes Autoregressive Conditional Heteroskedasticity Deterministic Terms Parameter Estimation Estimation of AR Models Estimation of ARM A Models Model Specification 33 IX

3 X Contents AR Order Specification Criteria Specifying More General Models Model Checking Descriptive Analysis of the Residuals Diagnostic Tests of the Residuals Stability Analysis Unit Root Tests Augmented Dickey-Fuller (ADF) Tests Schmidt-Phillips Tests A Test for Processes with Level Shift KPSSTest Testing for Seasonal Unit Roots Forecasting Univariate Time Series Examples German Consumption Polish Productivity Where to Go from Here 85 3 Vector Autoregressive and Vector Error Correction Models 86 Helmut Liitkepohl 3.1 Introduction VARsandVECMs The Models Deterministic Terms Exogenous Variables Estimation Estimation of an Unrestricted VAR Estimation of VECMs Restricting the Error Correction Term Estimation of Models with More General Restrictions and Structural Forms Model Specification Determining the Autoregressive Order Specifying the Cointegrating Rank Choice of Deterministic Term Testing Restrictions Related to the Cointegration Vectors and the Loading Matrix Testing Restrictions for the Short-Run Parameters and Fitting Subset Models Model Checking Descriptive Analysis of the Residuals Diagnostic Tests Stability Analysis 131

4 Contents XI 3.6 Forecasting VAR Processes and VECMs Known Processes Estimated Processes Granger-Causality Analysis The Concept Testing for Granger-Causality An Example Extensions Structural Vector Autoregressive Modeling and Impulse Responses 159 Jörg Breitung, Ralf Brüggemann, and Helmut Lütkepohl 4.1 Introduction The Models Impulse Response Analysis Stationary VAR Processes Impulse Response Analysis of Nonstationary VARs and VECMs Estimation of Structural Parameters SVAR Models Structural VECMs Statistical Inference for Impulse Responses Asymptotic Estimation Theory Bootstrapping Impulse Responses An Illustration Forecast Error Variance Decomposition Examples A Simple AB-Model The Blanchard-Quah Model An SVECM for Canadian Labor Market Data Conclusions Conditional Heteroskedasticity 197 Helmut Herwartz 5.1 Stylized Facts of Empirical Price Processes Univariate GARCH Models Basic Features of GARCH Processes Estimation of GARCH Processes Extensions Blockdiagonality of the Information Matrix Specification Testing An Empirical Illustration with Exchange Rates Multivariate GARCH Models 212

5 xii Contents Alternative Model Specifications Estimation of Multivariate GARCH Models Extensions Continuing the Empirical Illustration Smooth Transition Regression Modeling 222 Timo Teräsvirta 6.1 Introduction The Model The Modeling Cycle Specification Estimation of Parameters Evaluation Two Empirical Examples Chemical Data Demand for Money (Ml) in Germany Final Remarks Nonparametric Time Series Modeling 243 Rolf Tschernig 7.1 Introduction Local Linear Estimation The Estimators Asymptotic Properties Confidence Intervals Plotting the Estimated Function Forecasting Bandwidth and Lag Selection Bandwidth Estimation Lag Selection Illustration Diagnostics Modeling the Conditional Volatility Estimation Bandwidth Choice Lag Selection ARCH Errors Local Linear Seasonal Modeling The Seasonal Nonlinear Autoregressive Model The Seasonal Dummy Nonlinear Autoregressive Model Seasonal Shift Nonlinear Autoregressive Model 271

6 Contents хш 7.7 Example I: Average Weekly Working Hours in the United States Example II: XETRA Dax Index 280 The Software JMulTi 289 Markus Krätzig 8.1 Introduction to JMulTi Software Concept Operating JMulTi Numbers, Dates, and Variables in JMulTi Numbers Numbers in Tables Dates Variable Names Handling Data Sets Importing Data Excel Format ASCII Format JMulTi.dat Format Selecting, Transforming, and Creating Time Series Time Series Selector Time Series Calculator Managing Variables in JMulTi Notes for Econometric Software Developers General Remark TheJStatCom Framework Component Structure Conclusion 299 References Index