Introduction to Modern Time Series Analysis

Transkript

1 Springer Texts in Business and Economics Introduction to Modern Time Series Analysis Bearbeitet von Gebhard Kirchgässner, Jürgen Wolters, Uwe Hassler 1. Auflage Buch. XII, 319 S. Hardcover ISBN Format (B x L): 15,5 x 23,5 cm Gewicht: 660 g Wirtschaft > Volkswirtschaft > Makroökonomie schnell und portofrei erhältlich bei Die Online-Fachbuchhandlung beck-shop.de ist spezialisiert auf Fachbücher, insbesondere Recht, Steuern und Wirtschaft. Im Sortiment finden Sie alle Medien (Bücher, Zeitschriften, CDs, ebooks, etc.) aller Verlage. Ergänzt wird das Programm durch Services wie Neuerscheinungsdienst oder Zusammenstellungen von Büchern zu Sonderpreisen. Der Shop führt mehr als 8 Millionen Produkte.

2 Preface... V 1 Introduction and Basics The Historical Development of Time Series Analysis Graphical Representations of Economic Time Series The Lag Operator Ergodicity and Stationarity The Wold Decomposition References Univariate Stationary Processes Autoregressive Processes First Order Autoregressive Processes Second Order Autoregressive Processes Higher Order Autoregressive Processes The Partial Autocorrelation Function Estimating Autoregressive Processes Moving Average Processes First Order Moving Average Processes MA(1) and Temporal Aggregation Higher Order Moving Average Processes Mixed Processes ARMA(1,1) Processes ARMA(p,q) Processes Forecasting Forecasts with Minimal Mean Squared Errors Forecasts of ARMA(p,q) Processes Evaluation of Forecasts The Relation between Econometric Models and ARMA Processes References IX

3 X 3 Granger Causality The Definition of Granger Causality Characterisation of Causal Relations in Bivariate Models Characterisation of Causal Relations Using the Autoregressive and Moving Average Representations Characterisation of Causal Relations Using the Residuals of the Univariate Processes Causality Tests The Direct Granger Procedure The Haugh-Pierce Test The Hsiao Procedure Applying Causality Tests in a Multivariate Setting The Direct Granger Procedure with More Than Two Variables Interpreting the Results of Bivariate Tests in Systems With More Than Two Variables Concluding Remarks References Vector Autoregressive Processes Representation of the System Granger Causality Impulse Response Analysis Variance Decomposition Concluding Remarks References Nonstationary Processes Forms of Nonstationarity Trend Elimination Unit Root Tests The Dickey-Fuller Test The Augmented Dickey-Fuller Test The Phillips-Perron Test Unit Root Tests and Structural Breaks A Test with the Null Hypothesis of Stationarity Decomposition of Time Series Further Developments Fractional Integration Seasonal Integration

4 XI 5.6 Deterministic versus Stochastic Trends in Economic Time Series References Cointegration Definition and Properties of Cointegrated Processes Cointegration in Single Equation Models: Representation, Estimation and Testing Bivariate Cointegration Cointegration with More Than Two Variables Testing Cointegration in Static Models Testing Cointegration in Dynamic Models Cointegration in Vector Autoregressive Models The Vector Error Correction Representation The Johansen Approach Analysis of Vector Error Correction Models Cointegration and Economic Theory References Nonstationary Panel Data Issues with Panel Data Omitted Variable Bias Estimation and Testing Mixed Panel Evidence Panel Unit Root Tests First Generation Tests Second Generation Tests The Null Hypothesis of Stationarity The Combination of Significance The Inverse Normal Method Bonferroni-Type Tests Panel Cointegration Single Equation Approaches System Approaches Concluding Remarks References Autoregressive Conditional Heteroscedasticity ARCH Models Definition and Representation Unconditional Moments Temporal Aggregation

5 XII 8.2 Generalised ARCH Models GARCH Models The GARCH(1,1) Process Nonlinear Extensions Estimation and Testing Multivariate Models VAR-Type Models Correlation Models ARCH/GARCH Models as Instruments of Financial Market Analysis References Index of Names and Authors Subject Index