Efficient Iterative Maximum Likelihood Estimation of High-Parameterized Time Series Models
AbstractWe propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields computationally tractable, consistent and asymptotic efficient estimates of all parameters. We show the asymptotic normality and derive the estimator's asymptotic covariance in dependence of the number of iteration steps. To mitigate the curse of dimensionality in high-parameterized models, we combine the procedure with a penalization approach yielding sparsity and reducing model complexity. Small sample properties of the estimator are illustrated for two time series models in a simulation study. In an empirical application, we use the proposed method to estimate the connectedness between companies by extending the approach by Diebold and Yilmaz (2014) to a high-dimensional non-Gaussian setting.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2014-010.
Length: 34 pages
Date of creation: Jan 2014
Date of revision:
Multi-Step estimation; Sparse estimation; Multivariate time series; Maximum likelihood estimation; Copula;
Other versions of this item:
- Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," CFS Working Paper Series 450, Center for Financial Studies (CFS).
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-02-02 (All new papers)
- NEP-ECM-2014-02-02 (Econometrics)
- NEP-ETS-2014-02-02 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Francis X. Diebold & Kamil Yilmaz, 2011.
"On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms,"
NBER Working Papers
17490, National Bureau of Economic Research, Inc.
- Diebold, Francis X. & YÄ±lmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, Elsevier, vol. 182(1), pages 119-134.
- Francis X. Diebold & Kamil YÄ±lmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," PIER Working Paper Archive 11-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Francis X. Diebold & Kamil Yilmaz, 2011. "On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms," KoÃ§ University-TUSIAD Economic Research Forum Working Papers, Koc University-TUSIAD Economic Research Forum 1124, Koc University-TUSIAD Economic Research Forum.
- Francis X. Diebold & Kamil Yilmaz, 2011. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Working Papers 11-45, Federal Reserve Bank of Philadelphia.
- Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, 06.
- Song, Peter X.K. & Fan, Yanqin & Kalbfleisch, John D., 2005. "Maximization by Parts in Likelihood Inference," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 100, pages 1145-1158, December.
- Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
- repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
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