We construct models which enable a decision-maker to analyze the implications oftypical timeseries patterns of daily exchange rates for currency risk management. Ourapproach is Bayesianwhere extensive use is made of Markov chain Monte Carlo methods. The effects ofseveral modelcharacteristics...

There is increasing empirical and experimental evidence thatproviding financial incentives to agents to performcertain socially desirable actions may permanently reduce other typesof motivations to undertake these actions.We study the impact of financial incentives on the desire for...

We take a dynamic perspective on insurance markets under adverseselection and study a generalized Rothschildand Stiglitz model where agents may differ with respect to theaccidental probability and their expenditure levels incase an accident occurs. We investigate the nature of dynamicinsurance...

This paper puts forward a method to estimate average economic growth, andits associated confidence bounds, which does not require a formal decision onpotential unit root properties. The method is based on the analysis of eitherdifference-stationary or trend-stationary time series models,...

We analyze a market where firms compete in a conventional and an electronicretail channel. Consumers easily compare prices online, but some incur purchaseuncertainties on the online channel. We investigate the market shares of the two retailchannels and the prices that are charged. We find that...

In a Bayesian analysis, different models can be compared on the basis of theexpected or marginal likelihood they attain. Many methods have been devised to compute themarginal likelihood, but simplicity is not the strongest point of most methods. At the sametime, the precision of methods is often...

The linear Gaussian state space model for which the common variance istreated as a stochastic time-varying variable is considered for themodelling of economic time series. The focus of this paper is on thesimultaneous estimation of parameters related to the stochasticprocesses of the mean part...

We study the performance of two analytical methods and one simulation method for computing in-sample confidence bounds for time-varying parameters. These in-sample bounds are designed to reflect parameter uncertainty in the associated filter. They are applicable to the complete class of...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

The multivariate analysis of a panel of economic and financial time series with mixed frequencies is a challenging problem. The standard solution is to analyze the mix of monthly and quarterly time series jointly by means of a multivariate dynamic model with a monthly time index: artificial...