Empirical work on contracts typically regresses contract choice on observed principal and agent characteristics. If (i) some of these characteristics are unobserved or partially observed, and (ii) there are incentives whereby particular types of agents end up contracting with particular types of...

Empirical work on contracts typically regresses contract choice on observed principal and agent characteristics. If (i) some of these characteristics are unobserved or partially observed, and (ii) there are incentives whereby particular types of agents end up contracting with particular types of...

Empirical work on contracts typically regresses contract choice on observed principal and agent characteristics. If (i) some of these characteristics are unobserved or partially observed and (ii) there are incentives whereby particular types of agents end up contracting with particular types of...

All micro studies of demand are based on using time series cross sectional data. Because in such data each household is only observed once, it is only under strong identifying restrictions that one can interpret the coefficients on consumer behavior. For example, if tastes are correlated with...

Completing data sets that are collected in heterogeneous units is a quite frequent problem. Chow and Lin (1971) were the first to develop a united framework for the three problems (interpolation, extrapolation and distribution) of predicting times series by related series (the 'indicators')....

We estimate a New Keynesian DSGE model on French, German, Italian, and Spanish data. The main aim of this paper is to check for the respective sets of parameters that are stable over time, making use of the ESS procedure ('Estimate of Set of Stable parameters') developed by Inoue and Rossi...

This paper is the first attempt to investigate the performance of different learning rules in fitting survey data of household and expert inflation expectations in five core European economies (France, Germany, Italy, Netherlands and Spain). Overall it is found that constant gain learning...