Real-Time Forecasting With a Mixed-Frequency VAR

By Frank Schorfheide, DongHo Song. 2015

321 citations recorded by the source corpus.

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies—quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/674acb2cc664e3446127e6249ef8b4b9a1e35a98

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Citations in the corpus

Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections (2015, methodology): “Following Sims (1993) and Sims and Zha (1998), we complement such “inexact differencing” with an additional prior, known as “dummy-initial-observation” prior, that shrinks the forecast (10)Koop (2013) considers non-conjugate priors which allow for exclusion of certain variables from some equations, ”
Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work (2015, background): “One approach has been to construct mixed-frequency vector autoregressive (MF-VAR) forecasting models (e.g., Schorfheide and Song 2012).”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “Schorfheide and Song (2011) conduct a forecasting exercise on US data exploiting MFVAR models.”
Granger Causality: A Review and Recent Advances (2021, background): “Mixed frequency time series also present a challenge series (see e.g., Schorfheide and Song, 2015).”
What Central Bankers Need to Know about Forecasting Oil Prices (2014, unknown): “mixed-frequency VAR models (see Andreou, Ghysels, and Kourtellos 2011; Schorfheide and Song 2011).”
Testing for Granger Causality in Large Mixed-Frequency VARs (2016, unknown): “Bayesian methods within mixed-frequency VARs were also considered by Schorfheide and Song (2012).”
Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series. (2017, methodology): “The data set has been previously compiled and analyzed in the mixed frequency setting by [27] and is available on the author’s website.”
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts (2014, methodology): “For example, Schorfheide and Song (2015) and Giannone, Monti, and Reichlin (2014) developed mixed frequency BVAR and DSGE models, respectively.”