nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2017‒01‒29
five papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance By Boneva, Lena; Linton, Oliver
  2. Estimation for dynamic and static panel probit models with large individual effects By Wei Gao; Wicher Bergsma; Qiwei Yao
  3. Essays on the demand for ethanol in the United States: willingness to pay for E85 By Liao, Kenneth
  4. Sequential Probability Ration Tests : Conservative and Robust By Kleijnen, J.P.C.; Shi, Wen
  5. Playing by the rules? Agreement between predicted and observed binary choices Cycle: A Bayesian Evaluation By Stephanie Thomas

  1. By: Boneva, Lena (Bank of England); Linton, Oliver (University of Cambridge)
    Abstract: What is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5,610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the sample behaviour of this estimator are documented. We find that for non-financial firms, yields are negatively related to bond issuance but that effect is larger in the pre-crisis period.
    Keywords: Heterogeneous panel data; discrete choice models; capital structure
    JEL: C23 C25 G32
    Date: 2017–01–20
  2. By: Wei Gao; Wicher Bergsma; Qiwei Yao
    Abstract: For discrete panel data, the dynamic relationship between successive observations is often of interest. We consider a dynamic probit model for short panel data. A problem with estimating the dynamic parameter of interest is that the model contains a large number of nuisance parameters, one for each individual. Heckman proposed to use maximum likelihood estimation of the dynamic parameter, which, however, does not perform well if the individual effects are large. We suggest new estimators for the dynamic parameter, based on the assumption that the individual parameters are random and possibly large. Theoretical properties of our estimators are derived, and a simulation study shows they have some advantages compared with Heckman's estimator and the modified profile likelihood estimator for fixed effects.
    Keywords: Dynamic probit regression; generalized linear models; panel data; probit models; static probit regression
    JEL: C1
    Date: 2016
  3. By: Liao, Kenneth
    Date: 2016–01–01
  4. By: Kleijnen, J.P.C. (Tilburg University, Center For Economic Research); Shi, Wen
    Abstract: In practice, most computers generate simulation outputs sequentially, so it is attractive to analyze these outputs through sequential statistical methods such as sequential probability ratio tests (SPRTs). We investigate several SPRTs for choosing between two hypothesized values for the mean output (response). One SPRT is published in Wald (1945), and allows general distribution types. For a normal (Gaussian) distribution this SPRT assumes a known variance, but in our modified SPRT we estimate the variance. Another SPRT is published in Hall (1962), and assumes a normal distribution with an unknown variance estimated from a pilot sample. We also investigate a modification, replacing this pilot-sample estimator by a fully sequential estimator. We present a sequence of Monte Carlo experiments for quantifying the performance of these SPRTs. In experiment #1 the simulation outputs are normal. This experiment suggests that Wald (1945)’s SPRT with estimated variance gives significantly high error rates. Hall (1962)’s original and modified SPRTs are conservative; i.e., the actual error rates are much smaller than the prespecified (nominal) rates. The most efficient SPRT is our modified Hall (1962) SPRT. In experiment #2 we examine the robustness of the various SPRTs in case of nonnormal output. If we know that the output has a specific nonnormal distribution such as the exponential distribution, then we may also apply Wald (1945)’s original SPRT. Throughout our investigation we pay special attention to the design and analysis of these experiments.
    Keywords: sequential test; Wald; Hall; robustness; lognormal; gamma distribution; Monte Carlo
    JEL: C00 C10 C90 C15 C44
    Date: 2017
  5. By: Stephanie Thomas
    Abstract: Empirical economics frequently involves testing whether the predictions of a theoretical model are realized under controlled conditions. This paper proposes a new method for assessing whether binary (‘Yes’/‘No’) observations ranging over a continuous covariate exhibit a discrete change which is consistent with an underlying theoretical model. An application using observations from a controlled laboratory environment illustrates the method, however, the methodology can be used for testing for a discrete change in any binary outcome variable which occurs over a continuous covariate such as medical practice guidelines, firm entry and exit decisions, labour market decisions and many others. The observations are optimally smoothed using a nonparametric approach which is demonstrated to be superior, judged by four common criteria for such settings. Next, using the smoothed observations, two novel methods for assessment of a step pattern are proposed. Finally, nonparametric bootstrapped confidence intervals are used to evaluate the match of the pattern of the observed responses to that predicted by the theoretical model. The key methodological contributions are the two innovative methods proposed for assessing the step pattern. The promise of this approach is illustrated in an application to a controlled experimental lab data set, while the methods are easily extendable to many other settings. Further, the results generated can be easily communicated to diverse audiences.
    Keywords: Evaluation of theoretical predictions, binary outcome data, applied nonparametric analysis, data from experiments
    JEL: C18 C14 C4 C9
    Date: 2016–12

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