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Murphy, Anthony
Preferred name
Murphy, Anthony
Official Name
Murphy, Anthony
Research Output
Now showing 1 - 10 of 19
- PublicationA simple artificial regression based test of the fit of binary choice modelsA simple artificial regression based test of the fit of the binary choice models is derived. the test statistic is likely to have reasonable small sample properties since it is not based on the outer product gradient form of the conditional moment test.
141 - PublicationArtificial regression based LM tests of mis-specification for ordered probit modelsLagrange Multiplier (LM) tests for omitted variables, heteroscedasticity, incorrect functional form, and non-normality in the ordered probit model may be readily calculated using an artificial regression. The proposed artificial regression is both convenient and likely to have better small sample properties than the more common outer product gradient (OPG) form.
177 - PublicationArtificial regression based LM test of mis-specification for ordered logit modelsLagrange Multiplier tests for omitted variables , heteroscedasticity, incorrect functional form and asymmetry in the ordered logit model may be readily calculated using an artificial regression. The proposed artificial regression is both convenient and likely to have better small sample properties than the more common outer product gradient based artificial regression.
175 - PublicationModelling winners and losers in contingent valuation of public goods : appropriate welfare measures and econometric analysis.Contingent Valuation is now the most widely used method for valuing non-marketed goods in cost benefit analysis. Yet, despite the fact that many externalities manifest themselves as costs to some and benefits to others, most studies restrict willingness to pay (WTP) to being non-negative. This paper explores appropriate welfare measures for assessing losses and gains and demonstrates how these can be elicited explicitly. Statistical / econometric methods are presented for modelling such responses. Median WTP is estimated non-parametrically. Grouped regression / Tobit and grouped regression / hurdle models are used to identify the determinants of WTP and to estimate mean WTP.
758 - Publication
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