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Real-Time Macroeconomic Forecasting With a Heteroscedastic Inversion Copula

Bibliographic Data

ID19418799
AuthorsRubén Loaiza-Maya (0000-0003-4132-8355, The University of Melbourne), Rubén Loaiza‐Maya (The University of Melbourne), Michael Stanley Smith (Melbourne Business School, University of Melbourne, Carlton, Melbourne 3053, Australia ())
Year2020
Volume38
Issue2
Pages470-486
Publication date2020-04-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2018.1514309
OpenAlexW2775677714
LanguageEN
Citations received2
References cited45

There is a growing interest in allowing for asymmetry in the density forecasts of macroeconomic variables. In multivariate time series, this can be achieved with a copula model, where both serial and cross-sectional dependence is captured by a copula function, and the margins are nonparametric. Yet most existing copulas cannot capture heteroscedasticity well, which is a feature of many economic and financial time series. To do so, we propose a new copula created by the inversion of a multivariate unobserved component stochastic volatility model, and show how to estimate it using Bayesian methods. We fit the copula model to real-time data on five quarterly U.S. economic and financial variables. The copula model captures heteroscedasticity, dependence in the level, time-variation in higher moments, bounds on variables and other features. Over the window 1975Q1–2016Q2, the real-time density forecasts of all the macroeconomic variables exhibit time-varying asymmetry. In particular, forecasts of GDP growth have increased negative skew during recessions. The point and density forecasts from the copula model are competitive with those from benchmark models—particularly for inflation, a short-term interest rate and current quarter GDP growth. Supplementary materials for this article are available online

Autoregressive conditional heteroskedasticity · Econometrics · Economics · Heteroscedasticity · Multivariate statistics · Recession · Statistics · Stochastic volatility · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact

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Unique citing works2
Citations per year0.4
Citation span2021 - 2023 (3)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 2

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