Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks
Ross Doppelt and
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Ross Doppelt: Penn State
Keith O'Hara: New York University
No 1212, 2018 Meeting Papers from Society for Economic Dynamics
We introduce a new method for Bayesian estimation of fractionally integrated vector autoregressions (FIVARs). The FIVAR, which nests a standard VAR as a special case, allows each series to exhibit long memory, meaning that low frequencies can play a dominant role — a salient feature of many macroeconomic and financial time series. Although the parameter space is typically high-dimensional, our inferential procedure is computationally tractable and relatively easy to implement. We apply our methodology to the identification of technology shocks, an empirical problem in which business-cycle predictions depend on carefully accounting for low-frequency fluctuations.
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Persistent link: https://EconPapers.repec.org/RePEc:red:sed018:1212
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