Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan

For the problem of estimation of Money demand model of Pakistan, money supply (M1) shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namel...

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Bibliographic Details
Published in:Pakistan journal of statistics and operation research 2007-07, Vol.3 (2), p.109
Main Authors: Aslam, Muhammad, Pasha, G R
Format: Article
Language:English
Subjects:
Demand
Estimators
Kernels
Least squares method
Marketing
Regression
Standard error
Statistics
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Summary:For the problem of estimation of Money demand model of Pakistan, money supply (M1) shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons are made on the basis standard errors of the estimated coefficients, standard error of regression, Akaike Information Criteria (AIC) value, and the Durban-Watson statistic for autocorrelation. We further show that nearest neighbour regression estimator performs better when comparing with the other nonparametric kernel estimator.
ISSN:1816-2711
2220-5810
DOI:10.18187/pjsor.v3i2.65