arXiv 28 Sep 2021 · Econometrics
arXiv:2109.13777 · PDF · DOI · OpenAlex · Extracted main text
This paper demonstrates the potentials of the long short-term memory (LSTM) when applyingwith macroeconomic time series data sampled at different frequencies. We first present how theconventional LSTM model can be adapted to the time series observed at mixed frequencies when thesame mismatch ratio is applied for all pairs of low-frequency output and higher-frequency variable. Togeneralize the LSTM to the case of multiple mismatch ratios, we adopt the unrestricted Mixed DAtaSampling (U-MIDAS) scheme (Foroni et al., 2015) into the LSTM architecture. We assess via bothMonte Carlo simulations and empirical application the out-of-sample predictive performance. Ourproposed models outperform the restricted MIDAS model even in a set up favorable to the MIDASestimator. For real world application, we study forecasting a quarterly growth rate of Thai realGDP using a vast array of macroeconomic indicators both quarterly and monthly. Our LSTM withU-MIDAS scheme easily beats the simple benchmark AR(1) model at all horizons, but outperformsthe strong benchmark univariate LSTM only at one and six months ahead. Nonetheless, we find thatour proposed model could be very helpful in the period of large economic downturns for short-termforecast. Simulation and empirical results seem to support the use of our proposed LSTM withU-MIDAS scheme to nowcasting application.
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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 | Foroni, C., Marcellino, M., & Schumacher, C (2015) Unrestricted mixed data sampling (midas): Midas regressions with unrestricted lag polynomials | 1.000 | 6 | 4 | 100% |
| 2 | Clements, M. P. & Galvão, A. B (2008) Macroeconomic forecasting with mixed-frequency data: Forecasting output growth in the united states | 1.000 | 5 | 3 | 100% |
| 3 | Ghysels, E., Kvedaras, V., & Zemlys, V (2016) Mixed frequency data sampling regression models: the r package midasr | 0.874 | 6 | 2 | 100% |
| 4 | Diebold, F. X. & Mariano, R. S (1995) Comparing predictive accuracy | 0.874 | 5 | 2 | 100% |
| 5 | Marcellino, M. & Schumacher, C (2010) Factor midas for nowcasting and forecasting with ragged-edge data: A model comparison for german gdp | 0.874 | 5 | 2 | 100% |
| 6 | Ghysels, E., Santa-Clara, P., & Valkanov, R (2006) Predicting volatility: getting the most out of return data sampled at different frequencies | 0.811 | 4 | 2 | 100% |
| 7 | Babii, A., Ghysels, E., & Striaukas, J (2021) Machine learning time series regressions with an application to nowcasting | 0.737 | 3 | 2 | 100% |
| 8 | Ghysels, E., Sinko, A., & Valkanov, R (2007) Midas regressions: Further results and new directions | 0.644 | 2 | 2 | 100% |
| 9 | Gers, F. A., Schmidhuber, J., & Cummins, F (2000) Learning to forget: Continual prediction with lstm | 0.585 | 3 | 1 | 100% |
| 10 | Fischer, T. & Krauss, C (2018) Deep learning with long short-term memory networks for financial market predictions | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 32 scored citations.