Discrete Choice

In the panel data models estimated in Example 21.5.1, neither the logit nor the probit model provides a framework for applying a Hausman test to determine whether fixed or random effects is preferred. Explain. (Hint Unlike our application in the linear model, the incidental parameters problem persists here.) Look at the two cases. Neither case has an estimator which is consistent in both cases. In both cases, the unconditional fixed effects effects estimator is inconsistent, so the rest of the analysis falls apart. This is the incidental parameters problem at work. Note that the fixed effects estimator is inconsistent because in both models, the estimator of the constant terms is a function of 1/T. Certainly in both cases, if the fixed effects model is appropriate, then the random effects estimator is inconsistent, whereas if the random effects model is appropriate, the maximum likelihood random effects estimator is both consistent and efficient. Thus, in this instance, the random effects satisfies the requirements of the test. In fact, there does exist a consistent estimator for the logit model with fixed effects - see the text. However, this estimator must be based on a restricted sample observations with the sum of the ys equal to zero or T muust be discarded, so the mechanics of the Hausman test are problematic. This does not fall into the template of computations for the Hausman test. 

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Discontinuities and Discrete Choices. The Need to Differentiate Between Pharmaceuticals for Acute and Chronic Diseases... 

In the case of chronic processes, the patient can, at any time, take the decision to stop the treatment, as a reaction to changes in price or other variables. Whereas the econometric technique for analysing the demand for pharmaceuticals for acute processes is that of discrete choice models, here we will apply duration or survival models. In addition, the technology of drags for chronic diseases can have non-constant returns to scale. For example, the consumption of anxiolytics raises the tolerance and reduces the effect, resulting in an increase in the necessary dose. 

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 ... 

The discrete choice framework established by McFadden (1974) among others may offer a procedure for estimating the probability that a certain combination of characteristics is selected. Such a combination does not have to describe an existing treatment. There would therefore be a possibility of estimating the probability that a new type of treatment can be used. 

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. 

Scarpa, R., Philippidis, G., and Spalatro, F., 2005, Product-Country Images and Preference Heterogeneity for Mediterranean Food Products A Discrete Choice Framework, Agribusiness, 21(3) 329-349. 

Train, K.E., 2003, Discrete choice methods with simulation, Cambridge University Press, Cambridge, U.K. 

Then at the solution point of the NLP subproblem, the nonlinear constraints are linearized and the disjunction is relaxed by convex hull to build a master MILP subproblem which will yield a new discrete choice of (y, T) for the next iteration. 

Choice of CA formats Different CA formats are available. Discrete choice provides subjects with several different products or programs simultaneously and simply asks them to identify the most-preferred opinion in each choice set. DCE is based on random utility theory (Thurstone 1927 McFadden and Train 2000). 

Johnson FR, Lancsar E, Marshall D, Kilambi V, Miihlbacher A, Regier DA, et al. Constructing exp>eriinental designs for discrete-choice experiments Report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health 2013 January 16(1) 3-13. 

Ryan M, Bate A, Eastmond CJ, Ludbrook A. Use of discrete choice experiments to elicit preference. Quality in Health Care September 2001 10 155-160. 

Ryan M, Gerard K, Amaya-Amaya M. Using Discrete Choice Experiments to Value Health and Health Care. Dordrecht, the Netherlands Springer, 2008. 

Discrete choice approach In this approach, one monetary amount—called a bid—is presented to each respondent. The respondents are asked whether they are willing to pay this bid which may vary across subsamples (Hammerschmidt et al. 2003). In our case we may choose the bid corresponding to the market price for implementing the cable system (about 200). 

Extended discrete choice approach Respondents are asked to choose between different ranges of monetary amounts which are presented to them as their WTP, for example [O-SlOO], [ 100-150], [ 150-300S], Smore than 300. 

In the case study, we recommend the adoption of the extended discrete choice approach. Considering the market price for implementing the cable system about 200 for each student, respondents are given different possible statements to choose, [0- 10], [ 10-50], [ 50-100], [ 50-100 ], [ 100-150], [ 150-200], [ 200-250], [ 250-300 ], and they are also asked to specify the amoimt in case that the amoimt exceeds 300. 

Hammerschmidt, T, Zeitler, H.R and Leidl, R. (2003) Unexpected yes- and no-answering behaviour in the discrete choice approach to elicit willingness to pay A methodological comparison with payment cards. International Journal of Health Care Finance and Economics, 3 147-166. 

The process streams and separation units require individual cooling/heating for the temperature changes. We consider heat integration with a number of available utility streams with different temperature. Due to the discrete choices of the temperature and... 

Verma and Pullman (1998) The study examines the difference between managers rating of the perceived importance of different supplier attributes and their actual choice of suppliers using the Likert scale set of questions and a discrete choice analysis. 

Train, K. (2003). Discrete Choice Methods with Simulation. Cambridge University Press, Available online at http //elsa.berkeley.edu/books. Accessed June 2011. 

Hall, R. W., ed. 2003. Handbook of Transportation Science, 2nd ed. Boston, MA Kluwer Academic. Print and electronic. Focuses on the properties and characteristics common to all modes of transportation. Organized by snbject, chapters written by different authors address several broad topics discrete choice, travel demand, and vehicle operation flows and congestion, inclnding traffic control and system interactions spatial models used in network analysis and design and network assignment and routing and network models. Extensive use of tables, figures, and mathematical models references at the end of each chapter. The index and table of contents lack detail. 

For a typical flowsheet, such as the DME (dimethyl ether) PFD in Figure B.1.1 i Appendix B), there are many decision variables. The temperature and pressure of each unit can be varied. The size of each piece of equipment involves decision variables (usually several per unit). The reflux in tower T-201 and the purity of the distillate fromT-202 are decision variables. There are many more. Clearly, the simultaneous optimization of all of these decision variables is a difficult problem However, some subproblems are relatively easy. If Stream 4 (the exit from the methanol preheater) must be at 154°C, for example, the choice of which heat source to use (Ips, mps, or hps) is easy. There is only a sin e decision variable, there are only three discrete choices, and the choice has no direct impact on the rest of the process. The problem becomes more difficult if the temperature of Stream 4 is not constrained. 

Vol. 247 Optimization and Discrete Choice in Urban Systems. Proceedings, 1983. Edited by B.G. Hutchinson, P. Nijkamp and M. Batty. VI, 371 pages. 1985. 

Vd. 296 A. Borsch-Supan, Econometric Analysis of Discrete Choice. VIII, 211 pages. 1987. 

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