nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2020‒01‒13
four papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Solving dynamic discrete choice models using smoothing and sieve methods By Dennis Kristensen; Patrick K. Mogensen; Jong-Myun Moon; Bertel Schjerning
  2. Willingness to Pay for Community-Based Health Insurance among Rural Households of Southwest Ethiopia By Melaku Haile Likka; Shimeles Ololo Sinkie; Berhane Megerssa
  3. Heterogeneous Choice Sets and Preferences By Levon Barseghyan; Maura Coughlin; Francesca Molinari; Joshua C. Teitelbaum
  4. Identi?cation and estimation of dynamic structural models with unobserved choices By Yingyao Hu; Yi Xin

  1. By: Dennis Kristensen (Institute for Fiscal Studies and University College London); Patrick K. Mogensen (Institute for Fiscal Studies); Jong-Myun Moon (Institute for Fiscal Studies and University College London); Bertel Schjerning (Institute for Fiscal Studies and University of Copenhagen)
    Abstract: We propose to combine smoothing, simulations and sieve approximations to solve for either the integrated or expected value function in a general class of dynamic discrete choice (DDC) models. We use importance sampling to approximate the Bellman operators defining the two functions. The random Bellman operators, and therefore also the corresponding solutions, are generally non-smooth which is undesirable. To circumvent this issue, we introduce a smoothed version of the random Bellman operator and solve for the corresponding smoothed value function using sieve methods. We show that one can avoid using sieves by generalizing and adapting the “self-approximating” method of Rust (1997b) to our setting. We provide an asymptotic theory for the approximate solutions and show that they converge with vN-rate, where N is number of Monte Carlo draws, towards Gaussian processes. We examine their performance in practice through a set of numerical experiments and find that both methods perform well with the sieve method being particularly attractive in terms of computational speed and accuracy.
    Keywords: Dynamic discrete choice; numerical solution; Monte Carlo; sieves
    Date: 2019–04–03
  2. By: Melaku Haile Likka; Shimeles Ololo Sinkie; Berhane Megerssa
    Abstract: Use of healthcare services is inadequate in Ethiopia in spite of the high burden of diseases. User-fee charges are the most important factor for this deficiency in healthcare utilization. Hence, the country is introducing community based and social health insurances since 2010 to tackle such problems. This study was conducted cross-sectionally, in March 2013, to assess willingness of rural households to pay for community-based health insurance in Debub Bench district of Southwest Ethiopia. Two-stage sampling technique was used to select 845 households. Selected households were contacted using simple random sampling technique. Double bounded dichotomous choice method was used to illicit the willingness to pay. Data were analyzed with STATA 11. Krinsky and Rob method was used to calculate the mean/median with 95% CI willingness to pay after the predictors have been estimated using Seemingly Unrelated Bivariate Probit Regression. Eight hundred and eight (95.6%) of the sampled households were interviewed. Among them 629(77.8%) households were willing to join the proposed CBHI scheme. About 54% of the households in the district were willing to pay either the initial or second bids presented. On average, these households were willingness to pay was 162.61 Birr per household (8.9 US$) annually. If the community based health insurance is rolled out in the district, about half of households will contribute 163 Birr (8.9 US$) annually. If the premium exceeds the amount specified, majority of the households would not join the scheme. Key words: community based health insurance, willingness to pay, contingent valuation method, double bounded dichotomous choice, Krinsky and Robb, rural households, Ethiopia.
    Date: 2019–12
  3. By: Levon Barseghyan (Institute for Fiscal Studies); Maura Coughlin (Institute for Fiscal Studies); Francesca Molinari (Institute for Fiscal Studies and Cornell University); Joshua C. Teitelbaum (Institute for Fiscal Studies)
    Abstract: We propose a robust method of discrete choice analysis when agents’ choice sets are unobserved. Our core model assumes nothing about agents’ choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between agents’ choice sets and their preferences. We ?rst establish that the model is partially identi?ed and characterize its sharp identi?cation region. We also show how the model can be used to assess the welfare cost of limited choice sets. We then apply our theoretical ?ndings to learn about households’ risk preferences and choice sets from data on their deductible choices in auto collision insurance. We ?nd that the data can be explained by expected utility theory with relatively low levels of risk aversion and heterogeneous choice sets. We also ?nd that a mixed logit model, as well as some familiar models of choice set formation, are rejected in our data.
    Date: 2019–07–05
  4. By: Yingyao Hu (Institute for Fiscal Studies and Johns Hopkins University); Yi Xin (Institute for Fiscal Studies)
    Abstract: This paper develops identi?cation and estimation methods for dynamic structural models when agents’ actions are unobserved by econometricians. We provide conditions under which choice probabilities and latent state transition rules are nonparametrically identi?ed with a continuous state variable in a single-agent dynamic discrete choice model. Our identi?cation results extend to (1) models with serially correlated unobserved heterogeneity and continuous choices, (2) cases in which only discrete state variables are available, and (3) dynamic discrete games. We apply our method to study moral hazard problems in US gubernatorial elections. We ?nd that the probabilities of shirking increase as the governors approach the end of their terms.
    Date: 2019–06–18

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