Florian Huber, Michael Pfarrhofer, Philipp Piribauer
arXiv 12 Jan 2020 · Econometrics · publishedJournal of Forecasting (2020) · 1 citations (OpenAlex)
arXiv:2001.03935 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a dynamic factor model that uses euro area (EA) country-specific information on output and inflation to estimate an area-wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country-specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by imposing a factor structure on the shocks to the latent states. We moreover introduce flexible stochastic volatility specifications to control for heteroscedasticity in the measurement errors and innovations to the latent states. Carefully specified shrinkage priors allow for pushing the model towards a homoscedastic specification, if supported by the data. Our measure of the output gap closely tracks other commonly adopted measures, with small differences in magnitudes and timing. To assess whether the model-based output gap helps in forecasting inflation, we perform an out-of-sample forecasting exercise. The findings indicate that our approach yields superior inflation forecasts, both in terms of point and density predictions.
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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 | Planas C, Rossi A, and Fiorentini G (2008) Bayesian analysis of the output gap | 1.000 | 9 | 3 | 100% |
| 2 | Jarocinski M, and Lenza M (2018) An Inflation-Predicting Measure of the Output Gap in the Euro Area | 1.000 | 5 | 3 | 100% |
| 3 | Stella A, and Stock J (2013) A state-dependent model for inflation forecasting | 0.928 | 4 | 3 | 100% |
| 4 | Stock JH, and Watson MW (2007) Why has US inflation become harder to forecast? | 0.928 | 4 | 3 | 100% |
| 5 | Hamilton JD (2018) Why You Should Never Use the Hodrick-Prescott Filter | 0.888 | 10 | 3 | 70% |
| 6 | Aguilar O, and West M (2000) Bayesian dynamic factor models and portfolio allocation | 0.843 | 4 | 3 | 75% |
| 7 | Kastner G, and Frühwirth-Schnatter S (2014) Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models | 0.843 | 4 | 3 | 75% |
| 8 | Stock JH, and Watson MW (1999) Forecasting inflation | 0.843 | 3 | 3 | 100% |
| 9 | Frühwirth-Schnatter S, and Wagner H (2010) Stochastic model specification search for Gaussian and partial non-Gaussian state space models | 0.811 | 4 | 2 | 100% |
| 10 | Bitto A, and Frühwirth-Schnatter S (2019) Achieving shrinkage in a time-varying parameter model framework | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 39 scored citations.