On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.     

COMPARING THE EFFECTIVENESS OF REGULATION AND PRO‐SOCIAL EMOTIONS TO ENHANCE COOPERATION: EXPERIMENTAL EVIDENCE FROM FISHING COMMUNITIES IN COLOMBIA

This paper presents the results from a series of framed field experiments conducted in fishing communities off the Caribbean coast of Colombia. The goal is to investigate the relative effectiveness of exogenous regulatory pressure and pro‐social emotions in promoting cooperative behavior in a public goods context. The random public revelation of an individual's contribution and its consequences for the rest of the group leads to significantly higher public good contributions and social welfare than regulatory pressure, even under regulations that are designed to motivate fully efficient contributions. (JEL C93, H41, Q20, Q28)     

Decision approach to workload distribution

Abstract. This paper deals with decisions of workload temporal distribution in scheduling discrete and diversified productions. A new way of formulating the scheduling problem is proposed, from which some concepts and tools are presented. The notion of time resource interval objects, TRIs, allows the management of technical (time and resource) aspects at the different levels of a hierarchical structuring of the set of decisions taken in the workshop, from ‘load distribution’ types, to ‘effective realization of the operations’ types. Constraint-based reasoning handles different TRIs corresponding to given kinds of decisions. It helps to highlight the bounds or limits to be respected while deciding, to remain consistent with an initial set of constraints, issued for example from an upper level of decisions. Decisions of load temporal distribution consist in readjustments of some time constraints on a set of planned operations, by taking into account the (or some more detailed) constraints on the resource(s) on which they have been planned, such as finite capacity and/or minimal profitability. The analysis on temporal proximities of the planned operations involves some particular structuring of the time axis into successive time intervals: these structures are associated with sets of temporal bounds, and are called adjacent decompositions of the time axis. Such a decomposition introduces some specific TRIs, associated with load constraints (coming from the planned operations), and resource constraints (coming from limited quantities of resource, or profitability concerns). By respecting the given time and resource constraints, they can ‘exchange’ some quantities of load according to communicating vessels processes. These phenomena have been modelled as bounded flows in a temporal network, and offer new flexible curves of load with finite capacities, to help the decision.     

THE DURATION OF FIRM‐DESTINATION EXPORT RELATIONSHIPS: EVIDENCE FROM SPAIN, 1997–2006

This article uses survival analysis to investigate the duration of Spanish firms' trade relationships by destination over 1997–2006. Whereas firm export status is highly persistent, firms' destination portfolio is very dynamic: a typical firm‐country exporting relationship has a median duration of 2 years. Yet, if a firm manages to export to a country beyond 2 years the risk of exiting that market sharply falls afterwards. The results indicate that not only firm heterogeneity but also destination heterogeneity are crucial to explain survival in export markets. In particular, country (political) risk heavily shapes the effect of firm, product, and other destination characteristics on the length of trade relationships. Whereas firm productivity, comparative advantage, partners' GDP, and proximity enhance duration of trade with low‐risk countries, they have no effect on trade survival with high‐risk countries. On the contrary, information spillovers are particularly relevant to enhance survival of trade relationships with high‐risk countries. (JEL C41, F10, F14)     

Nonlinear Censored Regression Using Synthetic Data

Abstract.  The problem of estimating a nonlinear regression model, when the dependent variable is randomly censored, is considered. The parameter of the model is estimated by least squares using synthetic data. Consistency and asymptotic normality of the least squares estimators are derived. The proofs are based on a novel approach that uses i.i.d. representations of synthetic data through Kaplan–Meier integrals. The asymptotic results are supported by a small simulation study.     

Empirical Likelihood for Non-Smooth Criterion Functions

Abstract.  Suppose that X ₁ ,…,  X _n is a sequence of independent random vectors, identically distributed as a d -dimensional random vector X . Let     be a parameter of interest and     be some nuisance parameter. The unknown, true parameters ( μ ₀ , ν ₀ ) are uniquely determined by the system of equations E { g ( X , μ ₀ , ν ₀ )} =   0 , where g  =  ( g ₁ ,…, g _{p + q} ) is a vector of p + q functions. In this paper we develop an empirical likelihood (EL) method to do inference for the parameter μ ₀ . The results in this paper are valid under very mild conditions on the vector of criterion functions g . In particular, we do not require that g ₁ ,…, g _{p + q} are smooth in μ or ν . This offers the advantage that the criterion function may involve indicators, which are encountered when considering, e.g. differences of quantiles, copulas, ROC curves, to mention just a few examples. We prove the asymptotic limit of the empirical log-likelihood ratio, and carry out a small simulation study to test the performance of the proposed EL method for small samples.