Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/30382
Title: Lag-one autocorrelation in short series: Estimation and hypothesis testing
Author: Solanas Pérez, Antonio
Manolov, Rumen
Sierra, Vicenta
Keywords: Mètode de Montecarlo
Estadística
Psicologia experimental
Monte Carlo method
Statistics
Experimental psychology
Issue Date: 2010
Publisher: Universitat de València
Abstract: In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is studied. The performance of the ten lag-one autocorrelation estimators is compared in terms of Mean Square Error (combining bias and variance) using data series generated by Monte Carlo simulation. The results show that there is not a single optimal estimator for all conditions, suggesting that the estimator ought to be chosen according to sample size and to the information available of the possible direction of the serial dependence. Additionally, the probability of labelling an actually existing autocorrelation as statistically significant is explored using Monte Carlo sampling. The power estimates obtained are quite similar among the tests associated with the different estimators. These estimates evidence the small probability of detecting autocorrelation in series with less than 20 measurement times.
Note: Reproducció del document publicat a: http://www.uv.es/revispsi/paraARCHIVES/2010.html
It is part of: Psicologica, 2010, vol. 31, num. 2, p. 357-381
URI: https://hdl.handle.net/2445/30382
ISSN: 0211-2159
Appears in Collections:Articles publicats en revistes (Psicologia Social i Psicologia Quantitativa)

Files in This Item:
File Description SizeFormat 
570049.pdf550.13 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.