Causality and Regime Inference in a Markov Switching VAR
AbstractThis paper analyses three Granger noncausality hypotheses within a conditionally Gaussian MS-VAR model. Noncausality in mean is based on Granger´s original concept for linear predictors by defining noncausality from the 1-step ahead forecast error variance for the conditional expectation. Noncausality in mean-variance concerns the conditional forecast error variance, while noncausality in distribution refers to the conditional distribution of the forecast errors. Necessary and sufficient parametric conditions for noncausality are presented for all hypotheses. As an illustration, the hypotheses are tested using monthly postwar U.S. data on money and income. We find that money is not Granger causal in mean for income, but Granger causal in mean-variance, i.e there is unique information in money for predicting the next period regime and the regime affects the uncertainty about the income forecast.
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Bibliographic InfoPaper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 118.
Length: 41 pages
Date of creation: 01 Dec 2000
Date of revision:
Granger causality; Markov process; Regime switching; Vector autoregression;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-10-09 (All new papers)
- NEP-ECM-2001-10-09 (Econometrics)
- NEP-ETS-2001-10-09 (Econometric Time Series)
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