On bivariate generalized binomial and negative binomial distributions

The present paper deals with two types of generalized general binomial (binomial or negative binomial) distributions: (i) a univariate general binomial generalized by a bivariate distribution and (ii) a bivariate general binomial generalized by two independent univariate distributions. The probabilities, moments, conditional distributions and regression functions for these distributions are obtained in terms of bipartitional polynomials. Moreover recurrence relations for the probabilities and moments, independent of the bipartitional polynomials, are given. Finally these general results are applied to the (i) Binomial-Bivariate Poisson and (ii) Bivariate Binomial-Poissons distributions.     

Estimating U.S. consumer preferences for meat with a flexible utility function

Direct and indirect translog utility functions provide budget share equations which are both flexible and consistent with the theory of utility maximization. These forms are attractive for modelling consumer behavior. Because of their flexibility they are ideal for testing hypotheses such as additivity of preferences. In this paper we use the translog methodology to analyze U.S. consumption of the four principal categories of meat-fish, beef, poultry, and pork. We decisively reject the hypothesis of additivity. However, further testing for partial additivity reveals that (beef) and (fish, poultry, pork) are additively separable subgroups of meat.     

Inverse S-shaped probability weighting functions in first-price sealed-bid auctions

It is often observed in first-price sealed-bid auction experiments that subjects tend to bid above the risk neutral Nash equilibrium predictions. One possible explanation for this overbidding phenomenon is that bidders subjectively weight their winning probabilities. In the relevant literature, the probability weighting functions (PWFs) suggested to explain overbidding imply the underweighting of all probabilities. However, such functions are not in accordance with the PWFs commonly used in the literature (i.e., inverse S-shaped functions). In this paper we introduce inverse S-shaped PWFs into first-price sealed-bid auctions and investigate the extent to which such weighting functions explain overbidding. Our results indicate that bidders with low valuations underbid, whereas those with high valuations overbid. We accordingly conclude that inverse S-shaped PWFs provide a partial explanation for overbidding.     

Optimal monetary policy in a regime-switching economy: The response to abrupt shifts in exchange rate dynamics

This paper examines the trade-offs that a central bank faces when the exchange rate can experience sustained deviations from fundamentals and occasionally collapse. The economy is modelled as switching randomly between different regimes according to time-invariant transition probabilities. We compute both the optimal regime-switching control rule for this economy and optimised linear Taylor rules, in the two cases where the transition probabilities are known with certainty and where they are uncertain. The simple algorithms used in the computation are also of independent interest as tools for the study of monetary policy under general forms of (asymmetric) additive and multiplicative uncertainty. An interesting finding is that policies based on robust (minmax) values of the transition probabilities are usually more conservative.     

Valuation of piecewise linear barrier options

This paper discusses the valuation of piecewise linear barrier options that generalize classical barrier options. We establish formulas for joint probabilities of the logarithmic returns of the underlying asset and its partial running maxima when the process has a piecewise constant drift. In particular, we show that our results embrace the famous reflection principle as a special case, and that our established proposition delivers useful scalability for computing desired probabilities related to various types of barriers. We derive the closed-form prices of piecewise linear barrier options under the Black–Scholes framework, which are obtainable with little effort by relying on the derived probabilities. In addition, we provide numerical examples and discuss how option prices respond to several types of piecewise linear barriers.     

Spline Regression in the Presence of Categorical Predictors

We consider the problem of estimating a relationship nonparametrically using regression splines when there exist both continuous and categorical predictors. We combine the global properties of regression splines with the local properties of categorical kernel functions to handle the presence of categorical predictors rather than resorting to sample splitting as is typically done to accommodate their presence. The resulting estimator possesses substantially better finite‐sample performance than either its frequency‐based peer or cross‐validated local linear kernel regression or even additive regression splines (when additivity does not hold). Theoretical underpinnings are provided and Monte Carlo simulations are undertaken to assess finite‐sample behavior; and two illustrative applications are provided. An implementation in R is available; see the R package ‘crs’ for details. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimating Nonlinear Models with Multiple Fixed Effects: A Computational Note*

In this paper we consider estimation of nonlinear panel data models that include multiple individual fixed effects. Estimation of these models is complicated both by the difficulty of estimating models with possibly thousands of coefficients and also by the incidental parameters problem; that is, noisy estimates of the fixed effects when the time dimension is short contaminate the estimates of the common parameters due to the nonlinearity of the problem. We propose a simple variation of existing bias‐corrected estimators, which can exploit the additivity of the effects for numerical optimization. We exhibit the performance of the estimators in simulations.     

Mathematical methods of optimization for multi-objective transportation systems

Transportation systems have multi-objective functions and there are multi-factor decision situations. A general mathematical optimization model for such systems is developed which has broad applications for the planning, system design and evaluation of many transportation systems. Three types of solution techniques are discussed. For multi-objective linear programs, a solution is obtained which satisfies the decision maker's preferences and optimization from the decision maker's point of view is considered. A goal programming solution technique is given when goals for the system can be defined. If this is not possible, an overall utility function is defined on the various objective functions and a concept of additive utilities is explored and a parametric programming solution is given.     

Sub additive measures of relative information and inaccuracy

Summary In the study of information theoretic measures, additivity has been the basic requirement. However it is quite interesting to investigate the sub-additive measures. Starting from subadditivity for measures associated with a pair of distributions of a discrete random variable, it has been changed into an equality relation using another function of a pair of distributions., Under the sum property of the function and the measures, the relation is expressed in terms of a functional equation of which the most general complex solutions have been obtained. In terms of the real continuous solutions of the functional equation, the sub-additive measures of Relative information and Inaccuracy have been defined and characterized. Particular cases and simple properties particularly the convexities of some of these new measures have also been studied.     

Second‐Price Auctions with Different Participation Costs

This paper studies equilibria of second‐price auctions in independent private value environments with different participation costs. Two types of equilibria are identified: monotonic equilibria in which a bidder with a lower participation cost results in a lower cutoff for submitting a bid, and nonmonotonic equilibria in which a lower participation cost results in a higher cutoff. We show that there always exists a monotonic equilibrium, and further, that the monotonic equilibrium is unique for either concave distribution functions or strictly convex distribution functions with nonincreasing reverse hazard rates. There exist nonmonotonic equilibria when the distribution functions are strictly convex and the difference of the participation costs is sufficiently small. We also provide comparative static analysis and study the limiting properties of equilibria when the difference in bidders’ participation costs approaches zero.     

On generalizedBellman's functional equation

We consider a functional equation arising in game or decision theory. It is a supremum type functional equation in three unknown functions. We give the general solution, in a particular case, imposing endpoint conditions on the functions.     

New Results for additive and multiplicative risk apportionment

We provide new characterizations of the preference for additive and multiplicative risk apportionment when risk ordering relies on stochastic dominance. We then point out a simple property of risk apportionment with additive risks: Quite generally, an observed preference for additive risk apportionment in a specific risk environment is preserved when the decision-maker is confronted to other risk situations, so long as the total order of stochastic dominance relationships among risk couples remains the same. The main objective of this paper is to check whether this simple property also holds for multiplicative risks environments. We explain why this is not the case in general, and then provide a set of conditions under which this property holds. We also show that it holds – and even more strongly – in the case of CRRA utility functions due to a particular feature of this family of utility functions.     

Scale efficiency and homotheticity: equivalence of primal and dual measures

We address the issue of equivalence of primal and dual measures of scale efficiency in general production theory framework. We find that particular types of homotheticity of technologies, which we refer to here as scale homotheticity, provide necessary and sufficient condition for such equivalence. We also identify the case when the scale homotheticity is equivalent to the homothetic structures from Shephard (Theory of cost and production functions, Princeton studies in mathematical economics. Princeton University Press, Princeton, 1970).     

Functional coefficient instrumental variables models

We consider estimation of nonparametric structural models under a functional coefficient representation for the regression function. Under this representation, models are linear in the endogenous components with coefficients given by unknown functions of the predetermined variables, a nonparametric generalization of random coefficient models. The functional coefficient restriction is an intermediate approach between fully nonparametric structural models that are ill posed when endogenous variables are continuously distributed, and partially linear models over which they have appreciable flexibility. We propose two-step estimators that use local linear approximations in both steps. The first step is to estimate a vector of reduced forms of regression models and the second step is local linear regression using the estimated reduced forms as regressors. Our large sample results include consistency and asymptotic normality of the proposed estimators. The high practical power of estimators is illustrated via both a Monte Carlo simulation study and an application to returns to education.     

Disentangling preferences and limited attention: Random-utility models with consideration sets

This paper presents a model of choice with limited attention. The decision-maker forms a consideration set, from which she chooses her most preferred alternative. Both preferences and consideration sets are stochastic. While we present axiomatisations for this model, our focus is on the following identification question: to what extent can an observer retrieve probabilities of preferences and consideration sets from observed choices? Our first conclusion is a negative one: if the observed data are choice probabilities, then probabilities of preferences and consideration sets cannot be retrieved from choice probabilities. We solve the identification problem by assuming that an “enriched” dataset is observed, which includes choice probabilities under two frames. Given this dataset, the model is “fully identified”, in the sense that we can recover from observed choices (i) the probabilities of preferences (to the same extent as in models with full attention) and (ii) the probabilities of consideration sets. While a number of recent papers have developed models of limited attention that are, in a similar sense, “fully identified”, they obtain this result not by using an enriched dataset but rather by making a restrictive assumption about the default option, which our paper avoids.     

Implementation with partial verification

This paper examines the implementability of social choice functions when only partial verification of private information is possible. Green and Laffont (1986) used this framework to derive a necessary and sufficient condition for the revelation principle to continue to hold with partial verification. We provide economically interesting characterizations of this condition, which suggest that it may be too restrictive. This leads us to consider implementation (not necessarily truthful) in general, when there is partial verification. We consider the case where compensatory transfers are allowed, giving the mechanism designer further leeway. We show how partial verification may allow efficient implementation of bilateral trade, where it would otherwise not be possible. Received: 1 August 1998 / Accepted: 5 September 2000     

Maximum entropy autoregressive conditional heteroskedasticity model

In many applications, it has been found that the autoregressive conditional heteroskedasticity (ARCH) model under the conditional normal or Student’s t distributions are not general enough to account for the excess kurtosis in the data. Moreover, asymmetry in the financial data is rarely modeled in a systematic way. In this paper, we suggest a general density function based on the maximum entropy (ME) approach that takes account of asymmetry, excess kurtosis and also of high peakedness. The ME principle is based on the efficient use of available information, and as is well known, many of the standard family of distributions can be derived from the ME approach. We demonstrate how we can extract information functional from the data in the form of moment functions. We also propose a test procedure for selecting appropriate moment functions. Our procedure is illustrated with an application to the NYSE stock returns. The empirical results reveal that the ME approach with a fewer moment functions leads to a model that captures the stylized facts quite effectively.     

GEL statistics under weak identification

The central concern of this paper is the provision in a time series moment condition framework of practical recommendations of confidence regions for parameters whose coverage probabilities are robust to the strength or weakness of identification. To this end we develop Pearson-type test statistics based on GEL implied probabilities formed from general kernel smoothed versions of the moment indicators. We also modify the statistics suggested in Guggenberger and Smith (2008) for a general kernel smoothing function. Importantly for our conclusions, we provide GEL time series counterparts to GMM and GEL conditional likelihood ratio statistics given in Kleibergen (2005) and Smith (2007). Our analysis not only demonstrates that these statistics are asymptotically (conditionally) pivotal under both classical asymptotic theory and weak instrument asymptotics of Stock and Wright (2000) but also provides asymptotic power results in the weakly identified time series context. Consequently, the empirical null rejection probabilities of the associated tests and, thereby, the coverage probabilities of the corresponding confidence regions, should not be affected greatly by the strength or otherwise of identification. A comprehensive Monte Carlo study indicates that a number of the tests proposed here represent very competitive choices in comparison with those suggested elsewhere in the literature.     

Existence of equilibria in economies with externalities and non-convexities in an infinite-dimensional commodity space

We prove an equilibrium existence theorem for economies with externalities, general types of non-convexities in the production sector, and infinitely many commodities. The consumption sets, the preferences of the consumers, and the production possibilities are represented by set-valued mappings to take into account the external effects. The firms set their prices according to general pricing rules which are supposed to have bounded losses and may depend upon the actions of the other economic agents. The commodity space is L_∞(M,M,μ), the space of all μ-essentially bounded M-measurable functions on M.As for our existence result, we consider the framework of Bewley (1972). However, there are four major problems in using this technique. To overcome two of these difficulties, we impose strong lower hemi-continuity assumptions upon the economies. The remaining problems are removed when the finite economies are large enough.Our model encompasses previous works on the existence of general equilibria when there are externalities and non-convexities but the commodity space is finite dimensional and those on general equilibria in non-convex economies with infinitely many commodities when no external effect is taken into account.