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" Empirical Vector Autoregressive Modeling "
by Marius Ooms.
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BL
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| Record Number
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749033
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b568990
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| Main Entry
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by Marius Ooms.
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| Title & Author
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Empirical Vector Autoregressive Modeling\ by Marius Ooms.
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| Publication Statement
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Berlin, Heidelberg : Springer Berlin Heidelberg, 1994
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| Series Statement
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Lecture notes in economics and mathematical systems, 407.
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| Page. NO
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(xiii, 382 pages)
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| ISBN
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3642487920
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: 9783642487927
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| Contents
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1 Introduction --; 1.1 Integrating results --; 1.2 Goal of the study --; 1.3 Data and measurement model --; 1.4 Baseline model and methodology --; 1.5 Outline of the study --; 1.6 What is new? --; 2 The Unrestricted VAR and its components --; 2.1 Introduction --; 2.2 The model --; 2.3 Univariate processes and unit roots --; 2.4 Integrated processes --; 2.5 Alternative models for nonstationarity, long memory and persistence --; Appendix A2.1 MA representation integrated process --; Appendix A2.2 Univariate testing for unit root nonstationarity --; 3 Data Analysis by Vector Autoregression --; 3.1 Introduction --; 3.2 Data-oriented measures of influence --; 3.3 Diagnostic checking --; Appendix A3.1 Influence measures for the normal linear model --; Appendix A3.2 Influence measures for the multivariate general linear model --; Appendix A3.3 Influence measures in principal component analysis --; 4 Seasonality --; 4.1 Introduction --; 4.2 Application of the idea of unobserved components --; 4.3 Application of linear filters to estimate unobserved components --; 4.4 Data analysis of the seasonal component --; 4.5 Application of the Census X-11 filter in a VAR --; Appendix 4.1 Trigonometric seasonal processes in regression --; Appendix 4.2 Backforecasts and deterministic changes in mean --; 5 Outliers --; 5.1 Introduction --; 5.2 The outlier model --; 5.3 Some effects of outliers on VAR estimates --; 5.4 Derivation of the LM-statistics --; 5.5 An artificial example --; 5.6 Application to macroeconomic series --; 5.7 Two simple ways to study the influence of outliers --; Appendix 5.1 Some proofs concerning outlier test statistics --; Appendix 5.2 Subsample analysis outlier influence --; Appendix 5.3 Robust estimation by extraction of additive outliers --; 6 Restrictions on the VAR --; 6.1 Introduction --; 6.2 Cointegration, the number of unit roots, and common trends --; 6.3 Straightforward transformation formulae --; 6.4 Trend stationary processes and quadratic trends --; 6.5 Estimating pushing trends and pulling equilibria --; 6.6 Multivariate tests for unit roots --; Appendix 6.1 Computation and distribution multivariate unit root test statistics --; 7 Applied VAR Analysis for Aggregate Investment --; 7.1 Introduction --; 7.2 The variable of interest and some of its supposed relationships --; 7.3 Measurement model --; 7.4 Univariate analysis --; 7.5 Multivariate analysis --; Appendix 7.1 Data sources and construction --; Appendix 7.2 Results of final VECM model --; Appendix 7.3 Open economy stochastic dynamic general equilibrium models --; Summary --; References --; Name index.
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| Abstract
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The main subject of this book is empirical application of multivariate linear time series model on quarterly or month- ly economic data to discoverand describe important dynamic relationships between the variables of interest. The book stresses "real-life" application and the selection of data analytic tools. Simple numerical examples and some more al- gebraicexercises are used to illustrate major points. Rele- vant old and recent results from over 400 authors and refe- rences from econometrics, mathematical statistics, time se- ries analysis, economics and descriptve statistics are dis- cussed. Appropriate use of multivariate time series models requires an intimate knowledge of relevant characteristics of thedata. One can obtain this using a method that combines influence analysis (which data points contain the major part of the information?) and diagnostic checking (does the model describe the interesting part of the information well enough?). For economic time series these issuses are (the type of) nonstationarity of the trend and seasonal compo- nent, be it of the (fractional) "unit root" type or of the changing parameter type (structural breaks), both in a unva- riate and a multivariate context. The book introduces new graphical and statistical methodes to improve the understan- ding of seasonality, outliers, structural breaks, pushing trends and pulling equilibria in aparticular data set.
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| Subject
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Economics -- Statistics.
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Economics.
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| LC Classification
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HB141.B963 1994
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| Added Entry
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Marius Ooms
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