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.     

Risk preference heterogeneity in group contests

We analyze the first model of a group contest with players that are heterogeneous in their risk preferences. In our model, individuals’ preferences are represented by a utility function exhibiting a generalized form of constant absolute risk aversion, allowing us to consider any combination of risk-averse, risk-neutral, and risk-loving players. We begin by proving equilibrium existence and uniqueness under both linear and convex investment costs. Then, we explore how the sorting of a compatible set of players by their risk attitudes into competing groups affects aggregate investment. With linear costs, a balanced sorting (i.e., minimizing the variance in risk attitudes across groups) always produces an aggregate investment level that is at least as high as an unbalanced sorting (i.e., maximizing the variance in risk attitudes across groups). Under convex costs, however, identifying which sorting is optimal is more nuanced and depends on preference and cost parameters.     

Uncertain equilibria and incomplete preferences

This paper studies the tendency for incomplete preference structures to be associated with equilibrium price indeterminacies in an Arrow–Debreu–McKenzie state-contingent setting. It is shown that the presence of a sufficiently smooth stochastic production technology is inconsistent with equilibrium price indeterminacies even if all individuals have incomplete preference structures. A particularly convenient characterization of Paretian equilibria in the presence of incomplete preferences, which allows Paretian equilibrium to be characterized using simple principles of convex optimization and (sub)differential analysis, is also developed.     

Production Risk and Productivity Growth: Some Findings for Norwegian Salmon Aquaculture

This paper deals with estimation of primal panel data models of production risk, focusing on measurement of risk properties of inputs and productivity growth. Under production risk one should estimate technical change separately for the deterministic part and risk part of the technology, since risk averse producers will take into account both the mean and variance of output when they rank alternative technologies. For a panel of Norwegian salmon farms fish feed and fish input are found to increase output risk, while labor has a risk-decreasing effect on output. In the analysis of technical change by the first order stochastic dominance criterion the increase in mean output dominates the increase in output risk.     

Technical efficiency with state-contingent production frontiers using maximum entropy estimators

Although the theory of state-contingent production is well-established, the empirical implementation of this approach is still in an infancy stage. The possibility of finding a large number of states of nature, few observations per state and models affected by collinearity have led some researchers to claim the urgent need to develop robust estimation techniques. In this paper, we investigate the performance of some maximum entropy estimators to assess technical efficiency with state-contingent production frontiers. The methodological discussion and the simulation study provided in the paper reveal some of the potential of these estimators. Small mean squared error loss and small differences between the true and the estimated mean of technical efficiency show that the maximum entropy can be a powerful tool in the estimation of state-contingent production frontiers.     

Consumer surplus analysis under uncertainty: A general equilibrium perspective

This paper derives an exact form of partial equilibrium efficiency measure under uncertainty which is consistent with expected utility maximization in a general equilibrium situation with ex-post spot markets for many goods and asset markets which are in general incomplete.We consider that the good under consideration tends to be negligibly small compared to the entire set of commodity characteristics which is assumed to be a continuum, and look into the limit property of preferences over state-contingent consumption of the good and state-contingent income transfer associated to it. We show that the limit preference exhibits risk neutrality, not only that it exhibits no income effect, meaning that the two conditions are tied together. We also show that the marginal rate of substitution between extra income transfers at different states of the world converges to the ratio between the Lagrange multipliers associated to those states. When the asset markets are complete such ratios are equalized between consumers, but it is not the case in general when the asset markets are incomplete. This means that using the aggregate expected consumer surplus as the welfare measure will be in general inconsistent with individuals’ expected utility maximization in the general equilibrium environment or with ex-ante Pareto efficiency.     

Premia for correlated default risk

Using data on corporate default experience in the U.S. and market rates of CDX index and tranche swaps of various maturities, we estimate reduced-form models of correlated default timing in the CDX High Yield and Investment Grade portfolios under actual and risk-neutral probabilities. The striking contrast between the estimated processes followed by the actual and risk-neutral arrival intensities of defaults, and between the parameters governing the actual and risk-neutral dynamics of the risk-neutral intensities, indicates the presence of substantial default risk premia in CDX swap market rates. The effects of risk premia on swap rates covary strongly across maturities, and depend on general stock market volatility and several measures of credit spreads. Large moves in the effects of these premia on swap rates have natural interpretations in terms of economic and financial market developments during the sample period, April 2004 to October 2007. Our results suggest that a large portion of the movements in CDX swap market rates observed during the sample period may be caused by changing attitudes toward correlated default risk rather than changes in the economic factors affecting the actual risk of clustered defaults, which ultimately governs swap payoffs.     

Efficiency analysis in the presence of uncertainty

In a stochastic decision environment, differences in information can lead rational decision makers facing the same stochastic technology and the same markets to make different production choices. Efficiency and productivity measurement in such a setting can be seriously and systematically biased by the manner in which the stochastic technology is represented. For example, conventional production frontiers implicitly impose the restriction that information differences have no effect on the way risk-neutral decision makers utilize the same input bundle. The result is that rational and efficient ex ante production choices can be mistakenly characterized as inefficient—informational differences are mistaken for differences in technical efficiency. This paper uses simulation methods to illustrate the type and magnitude of empirical errors that can emerge in efficiency analysis as a result of overly restrictive representations of production technologies.     

Global identification of risk preferences with revealed preference data

The concept of parameter identification (for a given specification) is differentiated from global identification (which specification is right). First-order conditions for production under risk are shown to admit many alternative specification pairs representing risk preferences and either perceived price risk, production risk, or the deterministic production structure. Imposing an arbitrary specification on any of the latter three determines which risk preference specification fits a given dataset, undermining global identification even when parameter identification is suggested by typical statistics. This lack of identification is not relaxed by increasing the number of observations. Critical implications for estimation of mean-variance specifications are derived.     

The estimation of utility‐consistent labor supply models by means of simulated scores

We consider a utility‐consistent static labor supply model with flexible preferences and a nonlinear and possibly non‐convex budget set. Stochastic error terms are introduced to represent optimization and reporting errors, stochastic preferences, and heterogeneity in wages. Coherency conditions on the parameters and the support of error distributions are imposed for all observations. The complexity of the model makes it impossible to write down the probability of participation. Hence we use simulation techniques in the estimation. We compare our approach with various simpler alternatives proposed in the literature. Both in Monte Carlo experiments and for real data the various estimation methods yield very different results. Copyright © 2008 John Wiley & Sons, Ltd.     

Joint Bayesian Analysis of Parameters and States in Nonlinear non‐Gaussian State Space Models

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non‐Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis–Hastings algorithm or in importance sampling. Our method provides a computationally more efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic intensity and stochastic volatility models based on Ornstein–Uhlenbeck processes. For our empirical study, we analyse the performance of our methods for corporate default panel data and stock index returns. Copyright © 2016 John Wiley & Sons, Ltd.     

Productive efficiency of French grain producers: A latent variable model

In this article we propose to implement a covariance structure analysis to deal with the estimation of a stochastic frontier production function on panel data and the measurement of a time-varying technical efficiency. First, this method solves the potential problem of correlations between input quantities and individual effects. Second, individual effects and efficiency measures can be recovered as a byproduct of the analysis through the so-called factor scores. We implement this approach by fitting to a balanced panel of French grain producers, a parsimonious version of the Cornwell, Schmidt, and Sickles [1990]'s model where technical efficiencies are individual-specific linear functions of time. A specification search shows that this model is preferred to the traditional production function. Results shed light on the temporal pattern of efficiency in the French grain production sector.The authors thank Jacques Mairesse, Quang Vuong, two referees, the editor, and session participants at the Econometric Society European Meeting, Cambridge, September 1991, at the Second European Workshop on Efficiency and Productivity Analysis, Louvain-la-Neuve, October 1991, at the Conference on Current Issues in Productivity, Newark, December 1991, and at the ENSAE-EHESS seminar, Paris, March 1992, for helpful comments and suggestions.     

Volatility risk and economic welfare

This paper examines the effects of time-varying volatility on welfare. I construct a tractable endogenous growth model with recursive preferences, stochastic volatility, and capital adjustment costs. The model shows that a rise in volatility can decelerate growth in the absence of any level shocks. In contrast to level risk, which is always welfare reducing for a risk-averse household, volatility risk can increase or decrease welfare, depending on model parameters. When calibrated to U.S. data, the model finds that the welfare cost of volatility risk is largely negligible under plausible model parameterizations.     

Identification and estimation with contaminated data: When do covariate data sharpen inference?

Contaminated or corrupted data typically require strong assumptions to identify parameters of interest. However, weaker assumptions often identify bounds on these parameters. This paper addresses when covariate data—variables in addition to the one of interest—tighten these bounds. First, we construct the identification region for the distribution of the variable of interest. This region demonstrates that covariate data are useless without knowledge about the distribution of erroneous data conditional on the covariates. Then, we develop bounds both on probabilities and on parameters of this distribution that respect stochastic dominance.     

Asset prices with non-permanent shocks to consumption

Most standard asset-pricing models assume that all shocks to consumption are permanent. We relax this assumption and allow also for non-permanent shocks. In our specification, the long-run mean of consumption growth is constant; consumption levels are subject to short-run deviations from their long-run trend. The implications of our model are dramatically different from those obtained in the prior literature. A canonical and parsimonious asset pricing model with CRRA preferences and non-permanent shocks can reproduce the equity premium, high return volatility and return predictability with a coefficient of relative risk aversion below ten. This finding suggests that non-permanent shocks can play an important role in explaining asset pricing puzzles.     

Estimation and decomposition of inefficiency when producers maximize return to the outlay: an application to Norwegian fishing trawlers

This paper deals with estimation of a production technology where endogeneous choice of input and output variables is explicitly recognized. In particular, we assume that producers maximize return to the outlay (RO). For simplicity and tractability we start with a Cobb–Douglas transformation function with multiple inputs and outputs and show how the first-order conditions of RO maximization can be used to derive an estimating equation which is nothing but a partial input productivity equation. This equation does not suffer from the econometric endogeneity problem although the output and input variables are endogenous. First, we consider the case where producers are fully efficient allocatively but technically inefficient. The model is estimated using a single equation stochastic frontier approach. The model is then extended to allow allocative inefficiency and it is estimated as a system using generalized method of moment. Algebraic expressions are derived to decompose the effect of technical and allocative inefficiencies on RO. We also consider translog specifications that are estimated as (1) a single equation frontier model as well as (2) a system. We use a panel of Norwegian fishing trawlers data to estimate the model. Outputs are different species caught while inputs are labor and vessel size. We also control for number of days of operation, age of the vessel and year effects. Empirical results show that the average rate of RO is reduced by about 20 to 30 % due to technical inefficiency. On the other hand, average allocative efficiency is found to be about 78 %. The average overall efficiency is found to be around 60 %.     

Costly State Verification with Varying Risk Preferences and Liability

Abstract.  In the scenario of loan contracts with costly state verification, we examine how the properties of the set of states, different risk preferences of debtors and varying liability of lenders affect the structure of optimal repayments. In particular, we show that with risk‐averse debtors, a general set of states, a constant observation cost and both unlimited and limited lender liability, the debtor is strictly better off revealing the true state of nature when his realized revenue is low, which implies that optimal debtor consumption has a downward jump around the single switch from observed to unobserved states. If the debtor can destroy revenue or if the debtor is risk neutral, this non‐monotonicity of consumption disappears. Moreover, given the loan size, there is more monitoring under debtor‐risk aversion than risk neutrality. We present simulations showing that a contract with unlimited lender liability and debtor‐risk aversion has a higher expected observation cost but a lower variance of consumption than a contract with limited lender liability. Finally, we discuss the problems of commitment to verification and contract renegotiation in this framework.     

Asset pricing with loss aversion

The use of standard preferences for asset pricing has not been very successful in matching asset price characteristics, such as the risk-free interest rate, equity premium and the Sharpe ratio, to time series data. Behavioral finance has recently proposed more realistic preferences such as those with loss aversion. Research is starting to explore the implications of behaviorally founded preferences for asset price characteristics. Encouraged by some studies of Benartzi and Thaler [1995. Myopic loss aversion and the equity premium puzzle. The Quarterly Journal of Economics 110 (1), 73–92] and Barberis et al. [2001. Prospect theory and asset prices. Quarterly Journal of Economics CXVI (1), 1–53] we study asset pricing with loss aversion in a production economy. Here, we employ a stochastic growth model and use a stochastic version of a dynamic programming method with an adaptive grid scheme to compute the above mentioned asset price characteristics of a model with loss aversion in preferences. As our results show using loss aversion we get considerably better results than one usually obtains from pure consumption-based asset pricing models including the habit formation variant.     

Optimal full-hedging under state-dependent preferences

This paper examines the hedging behavior of a risk-averse individual who faces uncertainty in spot-market prices and has a state-dependent utility function. The model demonstrates how risk aversion, state-dependency of preferences, and dependence structure of spot-market prices and states of nature jointly determine the individual’s optimal hedge position. We stipulate two sets of necessary and sufficient conditions, one on the utility function and the other on the dependence structure of spot-market prices and states of nature, which yield the celebrated full-hedging theorem originally derived under state-independent preferences.     

A model of Social Security Disability Insurance using matched SIPP/Administrative data

We study Disability Insurance (DI) application behavior in the US using matched SIPP and administrative data over 1989–1995. Certain state-contingent earnings projections and eligibility probabilities are central to the analysis. We find evidence for a small work disincentive effect of DI that seems to be restricted to a subset of the DI beneficiaries, including low earning groups such as blue collar workers and those subject to economic dislocation. Processing time, Medicare value, unemployment, private health insurance, and health shocks are some of the major factors that affect application propensity. The behavioral response of female workers to various parameters of the DI program is found to be quite different from that of males.