Stephan Smeekes, Etienne Wijler
arXiv 24 Nov 2019 · Econometrics · 2 citations (OpenAlex)
arXiv:1911.10552 · PDF · DOI · OpenAlex · Extracted main text
We investigate how the possible presence of unit roots and cointegration affects forecasting with Big Data. As most macroeoconomic time series are very persistent and may contain unit roots, a proper handling of unit roots and cointegration is of paramount importance for macroeconomic forecasting. The high-dimensional nature of Big Data complicates the analysis of unit roots and cointegration in two ways. First, transformations to stationarity require performing many unit root tests, increasing room for errors in the classification. Second, modelling unit roots and cointegration directly is more difficult, as standard high-dimensional techniques such as factor models and penalized regression are not directly applicable to (co)integrated data and need to be adapted. We provide an overview of both issues and review methods proposed to address these issues. These methods are also illustrated with two empirical applications.
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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 | Johansen, S (1995) Likelihood-Based Inference in Cointegrated Vector Autoregressive Models | 1.000 | 6 | 4 | 100% |
| 2 | Smeekes, S. and E. Wijler (2018) Macroeconomic forecasting using penalized regression methods self | 0.928 | 4 | 3 | 100% |
| 3 | Banerjee, A., M. Marcellino, and I. Masten (2014) Forecasting with factor-augmented error correction models | 0.874 | 8 | 2 | 100% |
| 4 | Liang, C. and M. Schienle (2019) Determination of vector error correction models in high dimensions | 0.874 | 7 | 2 | 100% |
| 5 | Smeekes, S (2015) Bootstrap sequential tests to determine the order of integration of individual units in a time series panel self | 0.874 | 7 | 2 | 100% |
| 6 | Smeekes, S. and E. Wijler (2018) An automated approach towards sparse single-equation cointegration modelling self | 0.874 | 7 | 2 | 100% |
| 7 | Barigozzi, M., M. Lippi, and M. Luciani (2017) Dynamic factor models, cointegration, and error correction mechanisms | 0.874 | 5 | 2 | 100% |
| 8 | Barigozzi, M., M. Lippi, and M. Luciani (2018) Non-stationary dynamic factor models for large datasets | 0.874 | 5 | 2 | 100% |
| 9 | Bai, J (2004) Estimating cross-section common stochastic trends in nonstationary panel data | 0.874 | 5 | 2 | 100% |
| 10 | Wilms, I. and C. Croux (2016) Forecasting using sparse cointegration | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 176 scored citations.