Stephan Smeekes, Etienne Wijler
arXiv 24 Sep 2018 · Econometrics · publishedJournal of Econometrics (2020) · 2 citations (OpenAlex)
arXiv:1809.08889 · PDF · DOI · OpenAlex · Extracted main text
In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number of potentially (co)integrated variables. By extending the classical single-equation error correction model, SPECS enables the researcher to model large cointegrated datasets without necessitating any form of pre-testing for the order of integration or cointegrating rank. Under an asymptotic regime in which both the number of parameters and time series observations jointly diverge to infinity, we show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP. In addition, SPECS is shown to enable the correct recovery of sparsity patterns in the parameter space and to posses the same limiting distribution as the OLS oracle procedure. A simulation study shows strong selective capabilities, as well as superior predictive performance in the context of nowcasting compared to high-dimensional models that ignore cointegration. An empirical application to nowcasting Dutch unemployment rates using Google Trends confirms the strong practical performance of our procedure.
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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 | Boswijk, H. P (1994) Testing for an unstable root in conditional and structural error correction models | 1.000 | 8 | 4 | 100% |
| 2 | Medeiros, M. C. and Mendes, E. F (2016) $_1$-regularization of high-dimensional time series models with non-gaussian and heteroskedastic errors | 0.843 | 4 | 3 | 75% |
| 3 | Bühlmann, P. and Van De Geer, S (2011) Statistics for High-Dimensional Data: Methods, Theory and Applications | 0.737 | 3 | 3 | 67% |
| 4 | Zhao, P. and Yu, B (2006) On model selection consistency of lasso | 0.737 | 3 | 3 | 67% |
| 5 | Liang, C. and Schienle, M (2019) Determination of vector error correction models in high dimensions | 0.737 | 3 | 2 | 100% |
| 6 | Zhang, R., Robinson, P., and Yao, Q (2019) Identifying cointegration by eigenanalysis | 0.659 | 7 | 2 | 43% |
| 7 | Banerjee, A., Dolado, J., and Mestre, R (1998) Error-correction mechanism tests for cointegration in a single-equation framework | 0.644 | 2 | 2 | 100% |
| 8 | Banerjee, A., Marcellino, M., and Masten, I (2014) Forecasting with factor-augmented error correction models | 0.644 | 2 | 2 | 100% |
| 9 | Johansen, S (1992) Cointegration in partial systems and the efficiency of single-equation analysis | 0.644 | 2 | 2 | 100% |
| 10 | Johansen, S (1995) Likelihood-based inference in cointegrated vector autoregressive models | 0.644 | 2 | 2 | 100% |
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