Experimental studies have shown that trust and reciprocity are effective in increasing efficiency when complete contracting is infeasible. One example is the study by Berg et al. (1995) of the investment game. In this game the person who receives the investment is the one who may reward the...

The estimates of the structural parameters of a job separations model derived from the theory of on-the-job search are reported in this papers. Given that each employer pays the same wage to all observably equivalent workers and that wage dispersion across employers exists in the sense that...

In this experimental study of tax evasion and its determinants participants earn their income in a complex stochastic intertemporal environment including the possibility to invest into a risky asset. The earned income has to be declared in four tax returns which are randomly verified. If tax...

A new kind of mixture autoregressive model with GARCH errors is introduced and applied to the U.S. short-term interest rate. According to the diagnostic tests developed in the paper and further informal checks the model is capable of capturing both of the typical characteristics of the...

In empirical applications of structural equation modeling researchers often assume that the sample under investigation is homogenous unless observed characteristics allow for a division of the sample into mutual exclusive homogenous subgroups. If such information is not available, unobserved...

After a temporary period of a fixed exchange rate regime pegging the Polish zloty to the U.S. dollar, Poland established a preannounced crawling peg regime on October 15, 1991. In this system the zloty is tied to a currency basket and devalued with a preannounced monthly rate (rate of crawl). If...

On the basis of a real high stakes insurance experiment with small probabilities of losses, we demonstrate that concern is a more important driver of WTP for insurance than subjective probability estimates when there is ambiguity surrounding the estimate. Concern is still important when...