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on Econometric Time Series |
| By: | Meijer, Erik; Gilbert, Paul D. (Groningen University) |
| Abstract: | Time series factor analysis (TSFA) and its associated statistical theory is developed. Unlike dynamic factor analysis (DFA), TSFA obviates the need for explicitly modeling the process dynamics of the underlying phenomena. It also differs from standard factor analysis (FA) in important respects: the factor model has a nontrivial mean structure, the observations are allowed to be dependent over time, and the data does not need to be covariance stationary as long as differenced data satisfies a weak boundedness condition. The effects on the estimation of parameters and prediction of the factors is discussed. The statistical properties of the factor score predictor are studied in a simulation study, both over repeated samples and within a given sample. Some apparent anomalies are found in simulation experiments and explained analytically. |
| Date: | 2005 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:rugsom:05f10&r=ets |
| By: | George Kapetanios (Queen Mary, University of London); Zacharias Psaradakis (Birkbeck College, University of London) |
| Abstract: | This paper studies the properties of the sieve bootstrap for a class of linear processes which exhibit strong dependence. The sieve bootstrap scheme is based on residual resampling from autoregressive approximations the order of which increases slowly with the sample size. The first-order asymptotic validity of the sieve bootstrap is established in the case of the sample mean and sample autocovariances. The finite-sample properties of the method are also investigated by means of Monte Carlo experiments. |
| Keywords: | Autoregressive approximation, Linear process, Strong dependence, Sieve bootstrap, Stationary process |
| JEL: | C10 C22 C50 |
| Date: | 2006–01 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:wp552&r=ets |