arXiv 10 Apr 2020 · Econometrics · publishedJournal of Forecasting (2023) · 2 citations (OpenAlex)
arXiv:2004.04984 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the sensitivity of forecast performance measures to taking a real time versus pseudo out-of-sample perspective. We use monthly vintages for the United States (US) and the Euro Area (EA) and estimate a set of vector autoregressive (VAR) models of different sizes with constant and time-varying parameters (TVPs) and stochastic volatility (SV). Our results suggest differences in the relative ordering of model performance for point and density forecasts depending on whether real time data or truncated final vintages in pseudo out-of-sample simulations are used for evaluating forecasts. No clearly superior specification for the US or the EA across variable types and forecast horizons can be identified, although larger models featuring TVPs appear to be affected the least by missing values and data revisions. We identify substantial differences in performance metrics with respect to whether forecasts are produced for the US or the EA.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McCracken MW, and Ng S (2016) FRED-MD: A Monthly Database for Macroeconomic Research | 0.737 | 5 | 3 | 40% |
| 2 | Giannone D, Henry J, Lalik M, and Modugno M (2012) An area-wide real-time database for the euro area | 0.737 | 4 | 3 | 50% |
| 3 | Huber F, Koop G, and Onorante L (in press), Inducing Sparsity and Sh… | 0.644 | 3 | 2 | 67% |
| 4 | Feldkircher M, Huber F, and Kastner G (2017) Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs? | 0.644 | 3 | 2 | 67% |
| 5 | Kastner G, and Frühwirth-Schnatter S (2014) Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models | 0.511 | 3 | 2 | 33% |
| 6 | Carvalho CM, Polson NG, and Scott JG (2010) The horseshoe estimator for sparse signals | 0.511 | 2 | 2 | 50% |
| 7 | Geweke J, and Amisano G (2010) Comparing and evaluating Bayesian predictive distributions of asset returns | 0.511 | 2 | 2 | 50% |
| 8 | Carriero A, Clark TE, and Marcellino M (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.511 | 2 | 1 | 100% |
| 9 | Bańbura M, and Rünstler G (2011) A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP | 0.405 | 1 | 1 | 100% |
| 10 | Frühwirth-Schnatter S, and Wagner H (2010) Stochastic model specification search for Gaussian and partial non-Gaussian state space models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 50 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques | 0.405 | 1 | 1 |