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In discrete choice experiments (DCEs), differences between respondents’ preferences may be associated with observable or unobservable factors. Unobservable heterogeneity, related to latent factors associated with the choices of individuals, may be modelled using correlated (i.e. informative heterogeneity) or uncorrelated (i.e. uninformative heterogeneity) individual-specific parameters of a logit model. In this study, we simulated unobservable heterogeneity among DCE respondents and compared the results of the maximum simulated likelihood (MSL) estimation of the mixed logit model when correctly specified and mis-specified. These results show that the MSL estimates are biased and can differ greatly from the true parameters, even when correctly specified. Before estimating a mixed logit model, we highly recommend that choice modellers conduct simulation analyses to assess the potential extent of biases before relying on the MSL estimates, particularly their variances and correlations, and then ultimately determine which model specification produces the least bias.

We propose a new approach to simulate the likelihood of the sequential search model. By allowing search costs to be heterogeneous across consumers and products, we directly compute the joint probability of the search and purchase decisions when consumers are searching for the idiosyncratic preference shocks in their utility functions. Under the assumptions of Weitzman’s sequential search algorithm, the proposed procedure recursively makes random draws for each quantity that requires numerical integration while enforcing the conditions stipulated by the algorithm. In an extensive simulation study, we compare the proposed method with existing likelihood simulators that have recently been used to estimate the sequential search model. The proposed method attributes the uncertainty in the search order to the consumer-product-level distribution of search costs and the uncertainty in the purchase decision to the distribution of match values across consumers and products. This results in more precise estimation and an improvement in prediction accuracy. We also show that the proposed method allows for different assumptions on the search cost distribution and that it recovers consumers’ relative preferences even if the utility function and/or the search cost distribution is mis-specified. We then apply our approach to online search data from Expedia for field-data validation. From a substantive perspective, we find that search costs and “position” effects affect products in the lower part of the product listing page more than they do those in the upper part of the page.

The two-tier stochastic frontier model has seen widespread application across a range of social science domains. It is particularly useful in examining bilateral exchanges where unobserved side-specific information exists on both sides of the transaction. These buyer and seller specific informational aspects offer opportunities to extract surplus from the other side of the market, in combination also with uneven relative bargaining power. Currently, this model is hindered by the fact that identification and estimation relies on the potentially restrictive assumption that these factors are statistically independent. We present three different models for empirical application that allow for varying degrees of dependence across these latent informational/bargaining factors.

In this paper, we introduce a new model to estimate efficiency by generalizing the state-of-the-art panel stochastic frontier model, the salient feature of which is decomposition of inefficiency into a persistent and a transient component. The proposed model introduces an autoregressive process to allow for temporal dependence in transient inefficiency. Both firm heterogeneity and persistent inefficiency components are allowed to be correlated with some exogenous and endogenous covariates in the model. Our model solves the endogeneity problem and it also introduces determinants of both persistent and transient inefficiency. Since the transient component is autoregressive, the likelihood function is not available in closed form. To address this problem we use the Maximum Simulated Likelihood and (Simulated or Bayes) Generalized Method of Moments method to estimate the parameters and several other quantities of interest, including transient and persistent inefficiency. Since the model is dynamic and accommodates determinants of inefficiency, it is useful to production managers who wish to identify how much of their present inefficiency is affected by past inefficiency, as well as how and in what ways efficiency can be improved. We use Norwegian electricity distribution data to showcase an application of our model.

The spread of over-qualification is a consequence of individuals having acquired more credentials than required at the workplace. In some cases, it may be that this mismatch plays a role in allowing workers to compensate for the lack of some other skills, to escape from unemployment, or to achieve job stability in the labour market. Consequently, workers may feel no less satisfied, at least in some aspects, than adequately-matched workers. The aim of this paper is to analyse the relationship between over-qualification and the various dimensions of job satisfaction in Spain, a country characterised by a strongly-segmented labour market with high unemployment levels, and a significant number of mismatched employees. Using micro data for a representative sample of Spanish workers, we carry out simultaneous maximum likelihood estimations on a two-equation system to control for potential endogeneity. The results obtained provide evidence that does not reject the hypothesis that mismatched workers do not necessarily feel less satisfied than adequately-matched workers in the dimensions of job satisfaction related to extrinsic domains or social relations.

The simulated choice probabilities in mixed logit models are usually approximated numerically using Halton or random draws from a multivariate mixing distribution for the random parameters. Theoretically, the order in which the estimated variables enter the model should not matter. However, in practice, simulation “noise” inherent in the numerical procedure leads to differences in the magnitude of the estimated coefficients depending on the arbitrary order in which the random variables are estimated. The problem is exacerbated when a low number of draws are used or if correlation among coefficients is allowed. In particular, the Cholesky factorization procedure, which is used to incorporate correlation into the model, propagates simulation noise in the estimate of one coefficient to estimates of all subsequent coefficients in the model. Ignoring the potential ordering effects in simulated maximum likelihood estimation methods may seriously compromise the ability of replicating the results and can inadvertently influence policy recommendations. We find that better estimation accuracy is achieved with Halton draws using small prime numbers as it is the case for small integrating dimensions; but random draws provide better accuracy than Halton draws from large prime numbers as it is normally the case in high integrating dimensions. With correlation, the standard deviations have very large fluctuations depending on the order of the variables, affecting the conclusions regarding heterogeneity of preferences.

In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated bivariate normal structures, which include several commonly used specifications of the mixed logit (MIXL) models. This paper conducts a simulation study analyzing the MSL estimation of the error components (EC) MIXL. We find that the MSL estimator produces significant biases in the estimated parameters. The problem becomes worse when the true value of the variance parameter is small and the correlation parameter is large in magnitude. In some cases, the biases in the estimated marginal effects are as large as 12% of the true values. These biases are largely invariant to increases in the number of Halton draws.

Received stochastic frontier analyses with panel data have relied on traditional fixed and random effects models. We propose extensions that circumvent two shortcomings of these approaches. The conventional panel data estimators assume that technical or cost inefficiency is time invariant. Second, the fixed and random effects estimators force any time invariant cross unit heterogeneity into the same term that is being used to capture the inefficiency. Inefficiency measures in these models may be picking up heterogeneity in addition to or even instead of inefficiency. A fixed effects model is extended to the stochastic frontier model using results that specifically employ the nonlinear specification. The random effects model is reformulated as a special case of the random parameters model. The techniques are illustrated in applications to the U. S. banking industry and a cross country comparison of the efficiency of health care delivery.

Using the first seven waves of the Understanding Society data, this study estimates the effects of health on the extensive and intensive margins of the labour supply of the UK workers. Earlier studies on the effects of health on labour supply tend to focus on a binary measure of labour force participation or early retirement. The results show that health affects both the margins of labour supply for both males and females. From the preferred model, the results indicate that the effects of health on both the margins of labour supply are larger for females than for males. Furthermore, the results show that while at the extensive margin there does not appear to be a difference in the effect of health between the old and the young for both genders, for males the effect of health on hours worked, conditional on being employed, is larger for the old than for the young, but for females the effect on hours worked is larger for the young than for the old. The results also show that inadequately exploiting the longitudinal features of panel data might have led to bias estimates.

This paper illustrates the importance of referring to a dynamic approach when forecasting firms bankruptcies, paying a particular attention to French SMEs. Based on Shummay’s (J Bus 74:101–124, 2001), we build a duration model and extend it by incorporating unobservable heterogeneity. Moreover, we resort to a dynamic dichotomous specification in which “right side” censored data are taken into account. We emphasize the complexity of the calculations of integrals that must be implemented and show how to overcome this challenge by applying the Geweke, Hajivassiliou and Keane algorithm which involves the technique of the simulated maximum likelihood. The findings prove that our dynamic approach, which integrates macroeconomic variables and takes account of both random effects and exogenous shocks, provides credible results. Besides, our method provides the predictive content of macroeconomic variables and the unobservable heterogeneity, which is helpful in forecasting firms bankruptcies.