Abstract
Diagnostic or specification tests are typically used as a means of indicating model inadequacy or failure. For example in the case of a linear regression model which is estimated by ordinary least squares (OLS), a series of diagnostic tests could be used to indicate whether any of the assumptions required for OLS to be the best linear unbiased estimator (BLUE) appear to be violated. These assumptions include a serially uncorrelated and homoscedastic error term, absence of correlation between the error term and the regressors and correct specification of the conditional mean function, i.e. no omitted variables and appropriate functional form.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Editor information
Editors and Affiliations
Copyright information
© 1994 B. Bhaskara Rao
About this chapter
Cite this chapter
Otto, G. (1994). Diagnostic Testing: An Application to the Demand for M1. In: Rao, B.B. (eds) Cointegration. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-23529-2_6
Download citation
DOI: https://doi.org/10.1007/978-1-349-23529-2_6
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-0-333-61625-3
Online ISBN: 978-1-349-23529-2
eBook Packages: Palgrave Economics & Finance CollectionEconomics and Finance (R0)