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on Discrete Choice Models |
| By: | Jason R. Blevins (Department of Economics, Ohio State University) |
| Abstract: | This paper develops methods for estimating dynamic structural microeconomic models with serially correlated latent state variables. The proposed estimators are based on sequential Monte Carlo methods, or particle filters, and simultaneously estimate both the structural parameters and the trajectory of the unobserved state variables for each observational unit in the dataset. We focus two important special cases: single agent dynamic discrete choice models and dynamic games of incomplete information. The methods are applicable to both discrete and continuous state space models. We first develop a broad nonlinear state space framework which includes as special cases many dynamic structural models commonly used in applied microeconomics. Next, we discuss the nonlinear filtering problem that arises due to the presence of a latent state variable and show how it can be solved using sequential Monte Carlo methods. We then turn to estimation of the structural parameters and consider two approaches: an extension of the standard full-solution maximum likelihood procedure (Rust, 1987) and an extension of the two-step estimation method of Bajari, Benkard, and Levin (2007), in which the structural parameters are estimated using revealed preference conditions. Finally, we introduce an extension of the classic bus engine replacement model of Rust (1987) and use it both to carry out a series of Monte Carlo experiments and to provide empirical results using the original data. |
| Keywords: | dynamic discrete choice, latent state variables, serial correlation, sequential Monte Carlo methods, particle filtering |
| JEL: | C13 C15 |
| Date: | 2011–05 |
| URL: | http://d.repec.org/n?u=RePEc:osu:osuewp:11-01&r=dcm |
| By: | Edoardo Marcucci (University of Roma Tre, Italy); Valerio Gatta (Sapienza University of Rome, Italy) |
| Abstract: | This paper investigates alternative methods to account for preference heterogeneity in choice experiments. The main interest lies in assessing the different results obtainable when investigating heterogeneity in various ways. This comparison can be performed on the basis of model performance and, more interesting, by evaluating willingness to pay measures. Preference heterogeneity analysis relates to the methods used to search for it. Socioeconomic variables can be interacted with attributes and/or alternative-specific constants. Similarly one can consider different subsets of data (strata variables) and estimate a multinomial logit model for each of them. Heterogeneity in preferences can be investigated by including it in the systematic component of utility or in the stochastic one. Mixed logit and latent class models are examples of the first approach. The former, in its random variable specification, allows for random taste variations assuming a specific distribution of the attribute coefficients over the population and permit to capture additional heterogeneity by consenting parameters to vary across individuals both randomly and systematically with observable variables. In other words it accounts for heterogeneity in the mean and in the variance of the distribution of the random parameters due to individual characteristics. Latent class models capture heterogeneity by considering a discrete underlying distribution of tastes. The small number of mass points are the unobserved segments or behavioral groups within which preferences are assumed homogeneous. The probability of membership in a latent class can be additionally made a function of individual characteristics. Alternatively, heterogeneity can be incorporated in terms of the random component of utility. The covariance heterogeneity model adopts the second approach representing a generalization of the nested logit model and can be used to explain heteroscedastic error structures in the data. It allows the inclusive value parameter to be a function of choice alternative attributes and/or individual characteristics. An alternative method refers to an extension of the multinomial logit model in which the integration of unobserved heterogeneity is performed through random error components distributed according to a tree. An interesting improvement in modeling preference heterogeneity is related to its simultaneous inclusion in both systematic and stochastic parts. A valid example is the inclusion of an error component part in a random coefficient specification of the mixed multinomial logit model. The empirical data used for comparing the various methods tested relates to departure airport choice in a multi-airport region. The area of study includes two regions in central Italy, Marche and Emilia-Romagna, and four airports: Ancona, Rimini, Forlì and Bologna. A fractional factorial experimental design was adopted to construct a four alternative choice set and five hypothetical choice exercises in each questionnaire. The selection of the potentially most important attributes and their relative levels was developed on the basis of previous research. |
| Keywords: | heterogeneity, airport choice, stated preferences, discrete choice model. |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:rcr:wpaper:03_11&r=dcm |
| By: | Gitto, Lara (University of Catania, Department of Economics and Quantitative Methods) |
| Abstract: | Multiple sclerosis (MS) is a chronic, disabling, and progressive illness, representing one of the most common causes of neurological disability in young and middle-aged adults. There is not a definitive treatment for MS yet. However, disease-modifying drugs (DMDs) for MS, which include interferon-beta and copolymer-1 have shown to be effective in reducing the frequency and severity of relapses and the progression of disability. The clinical efficacy of such therapies has been well documented in the medical literature. Instead, the factors underlying the decision to start the pharmacological treatment, to continue it or to drop out, have not been studied so far. Adverse drug effects, as well as patients’ emotional states, therapeutic expectations, the need to assume the medicines very often, and lack of communication with medical staff, are some of the elements affecting patients’ adherence to the therapy. Data from medical records of 567 MS patients referred to the MS Centre of the IRCCS Centro Studi Neurolesi (Messina) between the years 2001-2008 have been retrospectively analyzed in a first phase. Factors influencing patient decision to start a pharmacological treatment with DMDs, in agreement with the neurologist suggestion, have been evaluated by applying a multinomial logit model. The second phase of the study was cross-sectional and analyzed the data obtained through a questionnaire administered to consecutive outpatients referred to Centro Studi Neurolesi within March and May 2009 (n = 143). The probability to proceed in the treatment or to drop out was estimated through a probit model. The present research constitutes a novelty among the existing economic and medical literature: in fact, there are no, so far, studies evaluating factors underlying MS patients’ decision to undergo a pharmacological treatment and to proceed it according to medical protocols. Moreover, a significant expenditure for health care systems is associated to MS treatment, both for patients who undergo the treatment (cost of medicines, productivity losses for patients who experience severe side effects, etc.) and for those who do not take the medicine or take it discontinuously. Given the documented evidence of augmenting costs (direct and indirect) with increasing disease severity, the ability of the DMDs to reduce relapse rates and slow the progression of MS may help to offset the cost of these therapies. Conversely, delayed treatment or poor compliance can dramatically increase costs and reduce benefits. |
| Keywords: | disease modifying drugs (DMDs); compliance; multinomial logit; probit. |
| JEL: | C35 D89 I19 |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:ris:demqwp:2010_003&r=dcm |