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2012
- handle: 10670/1.r20lqi
- hal: hal-00638440
- doi: 10.1162/REST_a_00225
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info:eu-repo/semantics/altIdentifier/doi/10.1162/REST_a_00225
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Time Series Misspecification Factor Analysis C - Mathematical and Quantitative Methods/C.C3 - Multiple or Simultaneous Equation Models • Multiple Variables/C.C3.C32 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes • State Space ModelsSujets proches
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Catherine Doz et al., « A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models », HAL-SHS : économie et finance, ID : 10.1162/REST_a_00225
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Résumé
Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and the sample size (T) going to in¯nity along any path and that maximum likelihood is viable for n large. The estimator is robust to misspecification of cross-sectional and time series correlation of the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis.