arXiv 9 Dec 2019 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:1912.04123 · PDF · DOI · OpenAlex · Extracted main text
We consider the estimation of approximate factor models for time series data, where strong serial and cross-sectional correlations amongst the idiosyncratic component are present. This setting comes up naturally in many applications, but existing approaches in the literature rely on the assumption that such correlations are weak, leading to mis-specification of the number of factors selected and consequently inaccurate inference. In this paper, we explicitly incorporate the dependent structure present in the idiosyncratic component through lagged values of the observed multivariate time series. We formulate a constrained optimization problem to estimate the factor space and the transition matrices of the lagged values {\em simultaneously}, wherein the constraints reflect the low rank nature of the common factors and the sparsity of the transition matrices. We establish theoretical properties of the obtained estimates, and introduce an easy-to-implement computational procedure for empirical work. The performance of the model and the implementation procedure is evaluated on synthetic data and compared with competing approaches, and further illustrated on a data set involving weekly log-returns of 75 US large financial institutions for the 2001-2016 period.
appendix boundary found by appendix_command · 60% of the source is main text. Read the extracted text to check this.
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 | Stock, J. H. and M. W. Watson (2005) Implications of dynamic factor models for VAR analysis | 1.000 | 6 | 4 | 100% |
| 2 | Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2005) The generalized dynamic factor model: one-sided estimation and forecasting | 1.000 | 6 | 3 | 100% |
| 3 | Agarwal, A., S. Negahban, and M. J. Wainwright (2012) Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions | 0.950 | 7 | 3 | 86% |
| 4 | Anderson, H. M. and F. Vahid (2007) Forecasting the volatility of Australian stock returns: Do common factors help? | 0.811 | 4 | 2 | 100% |
| 5 | Negahban, S., B. Yu, M. J. Wainwright, and P. K. Ravikumar (2012) A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers | 0.737 | 4 | 4 | 50% |
| 6 | Loh, P.-L. and M. J. Wainwright (2012) High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity | 0.737 | 4 | 2 | 75% |
| 7 | Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2000) The generalized dynamic-factor model: Identification and estimation | 0.737 | 3 | 2 | 100% |
| 8 | Billio, M., M. Getmansky, A. W. Lo, and L. Pelizzon (2012) Econometric measures of connectedness and systemic risk in the finance and insurance sectors | 0.693 | 7 | 1 | 100% |
| 9 | Stock, J. H. and M. W. Watson (2002) Forecasting using principal components from a large number of predictors | 0.644 | 3 | 2 | 67% |
| 10 | Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 39 scored citations.