Testing for asymmetric information in insurance markets has become a very important issue in the empirical literature in the last years. We analyze the (private) accident insurance, which has not been analyzed before in the literature, but covers one of the most important risks faced by...

type="main" xml:lang="en" <title type="main">Abstract</title> <p>Asymmetric information is an important phenomenon in insurance markets, but the empirical evidence on the extent of adverse selection and moral hazard is mixed. Because of its implications for pricing, contract design, and regulation, it is crucial to test for...</p>

Since Chiappori and Salanié, testing for asymmetric information has become tantamount to testing for a positive relation between risk and coverage which is predicted by (equilibrium) models for adverse selection and moral hazard. In this paper we review parametric tests and a recently developed...

Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are...

In this paper we analyze asymmetric information in the (private) disability insurance, which has not been analyzed before in the literature, but covers one of the most important risks faced by individuals in modern society, namely the loss of human capital. We show that there is asymmetric...

Asymmetric information is an important phenomenon in insurance markets, but the empirical evidence on the extent of adverse selection and moral hazard is mixed. Because of its implications for pricing, contract design, and regulation, it is crucial to test for asymmetric information in specic...

In this article, we review quantile models with endogeneity. We focus on models that achieve identification through the use of instrumental variables and discuss conditions under which partial and point identification are obtained. We discuss key conditions, which include monotonicity and...

Quantile regression (QR) methods fit a linear model for conditional quantiles, just as ordinary least squares (OLS) regression estimates a linear model for conditional means. An attractive feature of the OLS estimator is that it gives a minimum mean square error approximation to the conditional...

Suppose that a target function is monotonic and an available original estimate of this target function is not monotonic. Rearrangements, univariate and multivariate, transform the original estimate to a monotonic estimate that always lies closer in common metrics to the target function....

This paper gives identification and estimation results for quantile and average effects in nonseparable panel models, when the distribution of period specific disturbances does not vary over time. Bounds are given for interesting effects with discrete regressors that are strictly exogenous or...