Saulius Jokubaitis, Dmitrij Celov, Remigijus Leipus
arXiv 19 Jun 2019 · Econometrics · publishedInternational Journal of Forecasting (2020) · 15 citations (OpenAlex)
arXiv:1906.07992 · PDF · DOI · OpenAlex · Extracted main text
This paper aims to examine the use of sparse methods to forecast the real, in the chain-linked volume sense, expenditure components of the US and EU GDP in the short-run sooner than the national institutions of statistics officially release the data. We estimate current quarter nowcasts along with 1- and 2-quarter forecasts by bridging quarterly data with available monthly information announced with a much smaller delay. We solve the high-dimensionality problem of the monthly dataset by assuming sparse structures of leading indicators, capable of adequately explaining the dynamics of analyzed data. For variable selection and estimation of the forecasts, we use the sparse methods - LASSO together with its recent modifications. We propose an adjustment that combines LASSO cases with principal components analysis that deemed to improve the forecasting performance. We evaluate forecasting performance conducting pseudo-real-time experiments for gross fixed capital formation, private consumption, imports and exports over the sample of 2005-2019, compared with benchmark ARMA and factor models. The main results suggest that sparse methods can outperform the benchmarks and to identify reasonable subsets of explanatory variables. The proposed LASSO-PC modification show further improvement in forecast accuracy.
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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 | Bai, J. and S. Ng (2008) Forecasting economic time series using targeted predictors | 1.000 | 7 | 3 | 100% |
| 2 | Bulligan, G., M. Marcellino, and F. Venditti (2015) Forecasting economic activity with targeted predictors | 1.000 | 5 | 3 | 100% |
| 3 | Zou, H (2006) The Adaptive Lasso and its Oracle Properties | 0.737 | 3 | 2 | 100% |
| 4 | Bańbura, M., D. Giannone, M. Modugno, and L. Reichlin (2013) Now-casting and the real-time data flow | 0.644 | 2 | 2 | 100% |
| 5 | Tibshirani, R (1996) Regression shrinkage and selection via the Lasso | 0.644 | 2 | 2 | 100% |
| 6 | Zou, H., T. Haste, and R. Tibshirani (2006) Sparse Principal Component Analysis | 0.511 | 2 | 1 | 100% |
| 7 | Liu, Z. Z (2014) The Doubly Adaptive LASSO methods for time series analysis | 0.511 | 2 | 1 | 100% |
| 8 | Meinshausen, N (2007) Relaxed Lasso | 0.511 | 2 | 1 | 100% |
| 9 | Medeiros, M. C. and E. F. Mendes (2015) $_1$-regularization of high-dimensional time-series models with flexible innovations | 0.511 | 2 | 1 | 100% |
| 10 | Belloni, A., V. Chernozhukov, and L. Wang (2011) Square-root Lasso: pivotal recovery of sparse signals via conic programming | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nowcasting using regression on signatures | 0.405 | 1 | 1 |