Distinguishing between macroeconomic and financial uncertainty : classification search in stochastic volatility in mean VARs

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Davidson, Sharada Nia and Hou, Chenghan and Koop, Gary (2021) Distinguishing between macroeconomic and financial uncertainty : classification search in stochastic volatility in mean VARs. In: 4th Annual Workshop on Financial Econometrics, 2021-11-15 - 2021-11-16, Orebro University (Online).

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Abstract

Stochastic Volatility in Mean Vector Autoregressive models (SVMVARs) are a popular tool for measuring macroeconomic and financial uncertainty and their economic impacts. SVMVARs estimate macroeconomic (financial) uncertainty using a large set of macroeconomic (financial) variables. But what if there is uncertainty regarding whether variables are classified as macroeconomic or financial? We address this question, developing scalable Markov chain Monte Carlo algorithms for classification search in large SVMVARs with unclassified variables. Using time-invariant or time-varying classification, the algorithm determines whether each unclassified variable should be treated as macroeconomic or financial. We show that allowing for data-driven classification improves model t. Our results also suggest that without data-driven classification, macroeconomic uncertainty, its adverse effects and its contribution to fluctuations in economic variables tend to be underestimated. Financial uncertainty is also underestimated but its effects on headline macroeconomic variables tend to be overestimated.

ORCID iDs

Davidson, Sharada Nia, Hou, Chenghan and Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X;
  • Item type:Conference or Workshop Item(Paper)
    ID code:78916
    Dates:
    Date
    Event
    15 November 2021
    Published
    15 October 2021
    Accepted
    24 September 2021
    Submitted
    Subjects:Social Sciences > Finance
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:15 Dec 2021 14:08
    Last modified:24 Nov 2024 01:50
    URI:https://strathprints.strath.ac.uk/id/eprint/78916
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