Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/119542
Authors: 
Biørn, Erik
Year of Publication: 
2014
Series/Report no.: 
Memorandum, Department of Economics, University of Oslo 28/2014
Abstract: 
The measurement error problem in linear time series regression, with focus on the impact of error memory, modeled as nite-order MA processes, is considered. Three prototype models, two bivariate and one univariate ARMA, and ways of handling the problem by using instrumental variables (IVs) are discussed as examples. One has a bivariate regression equation that is static, although with dynamics, entering via the memory of its latent variables. The examples illustrate how 'structural dynamics' interacting with measurement error memory create bias in Ordinary Least Squares (OLS) and illustrate the potential of IV estimation procedures. Supplementary Monte Carlo simulations are provided for two of the example models.
Subjects: 
Errors in variables
ARMA
Error memory
Simultaneity bias
Attenuation
Monte Carlo
JEL: 
C22
C26
C32
C36
C52
C53
Document Type: 
Working Paper

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