Browsing by Subject "SIGN RESTRICTIONS"

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  • Puonti, Paivi (2019)
    We apply a novel Bayesian structural vector autoregressive method to analyze the macroeconomic effects of unconventional monetary policy in Japan, the US and the euro area. The method exploits statistical properties of the data to uniquely identify the model without restrictions, and thus enables formal assessment of the plausibility of given sign restrictions. Unlike previous research, the data-based analysis reveals differences in the output and price effects of the Bank of Japan's, Federal Reserve's and European Central Bank's balance sheet operations.
  • Lanne, Markku; Luoto, Jani (2020)
    Theories often make predictions about the signs of the effects of economic shocks on observable variables, thus implying inequality constraints on the parameters of a structural vector autoregression (SVAR). We introduce a new Bayesian procedure to evaluate the probabilities of such constraints, and, hence, to validate the theoretically implied economic shocks. We first estimate a SVAR, where the shocks are identified by statistical properties of the data, and subsequently label these statistically identified shocks by the Bayes factors calculated from their probabilities of satisfying given inequality constraints. In contrast to the related sign restriction approach that also makes use of theoretically implied inequality constraints, no restrictions are imposed. Hence, it is possible that only a subset or none of the theoretically implied shocks can be labelled. In the latter case, we conclude that the data do not lend support to the theory implying the signs of the effects in question. We illustrate the method by empirical applications to the crude oil market, and U.S. monetary policy.