Biases of Correlograms and of AR Representations of Stationary Series
AbstractWe derive the relation between the biases of correlograms and of estimates of auto-regressive AR(k) representations of stationary series, and we illustrate it with a simple AR example. The new relation allows for k to vary with the sample size, which is a representation that can be used for most stationary processes. As a result, the biases of the estimators of such processes can now be quantified explicitly and in a unified way.
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Bibliographic InfoPaper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 24_12.
Date of creation: Jun 2012
Date of revision:
Auto-correlation function (ACF) and correlogram; autoregressive (AR) representation; least-squares bias;
Other versions of this item:
- Abadir Karim M. & Larsson Rolf, 2012. "Biases of Correlograms and of AR Representations of Stationary Series," Journal of Time Series Econometrics, De Gruyter, vol. 4(1), pages 1-11, May.
- K Abadir & R Larsson, . "Biases of correlograms and of AR representations of stationary series," Discussion Papers 05/21, Department of Economics, University of York.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-06-25 (All new papers)
- NEP-ECM-2012-06-25 (Econometrics)
- NEP-ETS-2012-06-25 (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.:
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