Multinomial logit

Compare the fully parametric and semiparametric approaches to estimation of a discrete choice model such as the multinomial logit model discussed in Chapter 21. What are the benefits and costs of the semiparametric approach ... 

This standard result has been the basis for numerous discrete choice applications, and leads to what is usually called the conditional (or multinomial) logit model see, for example, Greene, 1993, pp. 664-72). An estimation of such a model presupposes an empirical version of in terms of a linear combination of explanatory variables. 

Bonnet, C., and Simioni, M., 2001, Assessing Consumer Response to Protected Designation of Origin Labelling A Mixed Multinomial Logit Approach, European Review of Agricultural Economics, 28(4) 433-449. 

Butler, Durbin, and Helvacian (1996) use this distinction between diffieult-to-monitor and easy-to-monitor injuries to explore whether soft-tissue injury elaims correlate with level of benefits and spread of HMOs. They find in their 10-year, 15-state sample of workers compensation claims that the proportion of claims attributable to soft-tissue injuries rose from 44.7 percent of all claims in 1980 to 50.6 percent in 1989. Concurrently, the share of costs attributable to soft-tissue injuries rose from 41 pereent to 48.8 percent. The share of costs for injuries that crush or fracture a bone—easy-to-monitor claims—is the only category that declined between 1980 and 1989. Using a multinomial logit model, the authors determine that most of the increase in soft-tissue injury is attributable to the expansion of HMOs. Specifically, they ascribe the rise in such injuries to moral hazard response by HMO providers, who increase their revenue by classifying as woik-related injuries as many health conditions as possible. ... 

Since data are available only on workers who report claims, the noninjured state is omitted. The stochastic specification employed below implies that the parameter estimates will be unchanged by such an omission the odds ratio implied by a multinomial logit model maintains the independence of irrelevant alternatives, thus the parameter estimates will be consistent. The categorical dependent variable identifies one of the four groups of injuries above. Although the parameters for other sprains and strains (nonback) are necessarily normalized, the implied impact of the HRM variables on nonback sprains and strains is given in Table 4.4. 

The multinomial logit model used in this analysis assumes that the injured worker s perceived wellness, or utility, is given by... 

Note that the coefficients of the multinomial logit fimction do not represent the marginal effects of the independent variables on claim choice. However, the coefficients may be converted to measures of marginal effects and, for ease of comparison among the variables, expressed as semi-elasticities using well-established formulas ... 

Policies on the l pe of Injury Multinomial Logit Regression (standard error in parentheses)... 

This doesn t mean that moral hazard isn t important, only that the claims denial and multinomial logit results indicate that HRM practices do not reduce workers compensation solely, or even mainly, through reductions in claims-reporting moral hazard response. 

Policies on the Type of Injrrry Multinomial Logit Regression... 

The choice data for the profiling exercise were analysed using a multinomial logit (MNL), as described by Thomson et al. (2010). The output is a set of scale values, one for each conceptual term for each retailer. Within each retailer, the scale values for the conceptual terms were then transformed using a unique rescaling factor derived from the calibration data according to the procedure described by Crocker and Thomson (2014). The rescaled data for each of the three retailers were plotted on a common difference scale, thereby facilitating direct comparison of the scale values for the conceptual terms across retailers (Fig. 5.5). 

Table 8 Multinomial logit, Dependent Variable DISC = 0, 1, 2 for projects discontinued in clinicals I, II, III. Sample = Discontinued Projects (Standard errors in parenthesis)...

Table 9 Estimated Changes in Probabilities for NBF as Originators from the Multinomial logit Model in Table 8 (Change in Probability of failing at Stage I, II, III given that Project was...

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