Bertrand Melenberg

Estimating Risk Attitudes Using Lotteries: A Large Sample Approach

Attitudes towards risk play a major role in many economic decisions. In empirical studies it is quite often assumed that attitudes towards risk do not vary across individuals. This paper questions this assumption and analyses which factors influence an individuals risk attitude. Based on questions on lotteries in a large household survey we first semiparametrically estimate an index for risk aversion. We only make weak assumptions about the underlying decision process and our estimation method allows for generalisations of expected utility. We then estimate a structural model based on Cumulative Prospect Theory. Expected utility is strongly rejected and both the value function and the probability weighting function vary significantly with (among other things) age, income, and wealth of the individual.

Parametric and semi-parametric modelling of vacation expenditures

We analyse several limited dependent variable models explaining the budget share that Dutch families spend on vacations. To take account of the substantial number of zero shares, two types of models are used. The first is the single-equation censored regression model. We estimate and test several parametric and semiparametric extensions of the Tobit model. Second, we consider two-equation models, in which the participation decision and the decision on the amount to spend are treated separately. The first decision is modelled as a binary choice model; the second as a conditional regression. We estimate and test parametric and semiparametric specifications. Copyright 1996 by John Wiley & Sons, Ltd.

An analysis of housing expenditure using semiparametric models and panel data

In this paper we model expenditure on housing for owners and renters by means of endogenous switching regression models for panel data. We explain the share of housing in total expenditure from a household specific effect, family characteristics and total expenditure, where the latter is allowed to be endogenous. We consider both random and fixed effects panel data models. We compare estimates for the random effects model with estimates for the linear panel data model in which selection only enters through the fixed effects and with estimates allowing for fixed effects and a more general type of selectivity. Differences appear to be substantial. The results imply that the random effects model as well as the linear panel data model are too restrictive.

Testing the predictive value of subjective labour supply data

Empirical implementation of labour supply theories is usually based on realized labour market behaviour. This requires strong assumptions about the impact of labour demand. A possibility to avoid these assumptions is to make use of subjective data on desired labour supply. In this paper we investigate whether respondents’ answers to survey questions on the desired number of working hours contain additional information on the preferences of the individuals. Using panel data for the Netherlands, we analyze whether deviations between desired hours and actual hours of work help to predict future job changes or changes in hours worked. We use parametric and recently developed nonparametric tests. The results suggest that subjective information on desired working hours are helpful in explaining female labour supply. For males the evidence is mixed.

Environmental Kuznets Curves for CO2: Heterogeneity versus Homogeneity

We explore the emissions income relationship for CO2 in OECD countries using various modelling strategies.Even for this relatively homogeneous sample, we find that the inverted-U-shaped curve is quite sensitive to the degree of heterogeneity included in the panel estimations.This finding is robust, not only across different model specifications but also across estimation techniques, including the more flexible non-parametric approach.Differences in restrictions applied in panel estimations are therefore responsible for the widely divergent findings for an inverted-U shape for CO2.Our findings suggest that allowing for enough heterogeneity is essential to prevent spurious correlation from reduced-form panel estimations.Moreover, this inverted U for CO2 is likely to exist for many, but not for all, countries.

Estimation of a censored regression panel data model using conditional moment restrictions efficiently

A new semiparametric estimator for the censored regression panel data model with fixed effects is introduced. It is based upon an estimator proposed by Honore for the case of two time periods combined with ideas of Newey to improve the efficiency. The estimator is more efficient than Honores and is generalized to the case of a balanced or unbalanced panel of more than two waves. Estimation is performed in two steps. Using Honores estimator in a first step, efficient GMM using conditional moment restrictions is applied. The performance of this estimator is compared to that of Honores and other existing estimators in an empirical example concerning labour income of married females, using panel data from the Dutch Socio-Economic Panel, 1984-1988. Attention is paid to specification testing and the sensitivity of the results for the choice of smoothness parameters.

Consumption, leisure and earnings-related liquidity constraints: A note

Abstract The life-cycle model with liquidity constraints produces a Euler equation with unobservable Kuhn–Tucker multipliers. If borrowing restrictions depend on earnings, preferences are non-separable between goods and leisure, and individuals are employed, we derive a Euler equation involving only observable variables.

Trends in Mortality Decrease and Economic Growth

The vast literature on extrapolative stochastic mortality models focuses mainly on the extrapolation of past mortality trends and summarizes the trends by one or more latent factors. However, the interpretation of these trends is typically not very clear. On the other hand, explanation methods are trying to link mortality dynamics with observable factors. This serves as an intermediate step between the two methods. We perform a comprehensive analysis on the relationship between the latent trend in mortality dynamics and the trend in economic growth represented by gross domestic product (GDP). Subsequently, the Lee-Carter framework is extended through the introduction of GDP as an additional factor next to the latent factor, which provides a better fit and better interpretable forecasts.

Econometric analysis of microscopic simulation models

Microscopic simulation models are often evaluated based on visual inspection of the results. This paper presents formal econometric techniques to compare microscopic simulation (MS) models with real-life data. A related result is a methodology to compare different MS models with each other. For this purpose, possible parameters of interest, such as mean returns, or autocorrelation patterns, are classified and characterized. For each class of characteristics, the appropriate techniques are presented. We illustrate the methodology by comparing the MS model developed by He and Li [J. Econ. Dynam. Control, 2007, 31, 3396–3426, Quant. Finance, 2008, 8, 59–79] with actual data.

Computationally Tractable Counterparts of Distributionally Robust Constraints on Risk Measures

In this paper we study distributionally robust constraints on risk measures (such as standard deviation less the mean, Conditional Value-at-Risk, Entropic Value-at-Risk) of decision-dependent random variables. The uncertainty sets for the discrete probability distributions are defined using statistical goodness-of-fit tests and probability metrics such as Pearson, likelihood ratio, Anderson-Darling tests, or Wasserstein distance. This type of constraints arises in problems in portfolio optimization, economics, machine learning, and engineering. We show that the derivation of a tractable robust counterpart can be split into two parts: one corresponding to the risk measure and the other to the uncertainty set. We also show how the counterpart can be constructed for risk measures that are nonlinear in the probabilities (for example, variance or the Conditional Value-at-Risk). We provide the computational tractability status for each of the uncertainty set-risk measure pairs that we could solve. Numerical examples including portfolio optimization and a multi-item newsvendor problem illustrate the proposed approach.