In this paper, I examine the properties of the class of generalized empirical likelihood estimators of moment-condition models. These nonparametric likelihood estimators satisfy exactly the moment conditions and automatically remove any bias due to a lack of centering. Moreover, the bias of the...

This publication consists of ten separate studies on payment and settlement systems employing simulation techniques. Most of these were carried out using the payment and settlement system simulator BoF-PSS2 provided by the Bank of Finland. The preliminary versions were presented at the annual...

This publication consists of nine separate studies on payment and settlement systems conducted using simulation techniques. Most have been carried out using the payment and settlement system simulator BoF-PSS2 provided by the Bank of Finland. The preliminary versions were presented at the annual...

This publication consists of fifteen studies on payment and settlement systems conducted using computational or simulation techniques. The studies have been presented at the simulator seminars arranged by the Bank of Finland during the years 2009–2011. The main focus of the studies is on the...

A dynamic Tobit model with Time-varying parameters is proposed for the daily reaction function of the Open Market Desk of the US Federal Reserve. Such a model offers a more realistic depiction of the Desk's behavior than those of past contributions in the literature as it allows for both...

This paper studies a Monte Carlo algorithm for computing distributions of state variables when the underlying model is a Markov process. It is shown that the $L_1$ error of the estimator always converges to zero with probability one, and often at a parametric rate. A related technique for...

This paper considers two models to deal with an outcome variable that contains a large fraction of zeros, such as individual expenditures on health care: a sample-selection model and a two-part model. The sample-selection model uses two possibly correlated processes to determine the outcome: a...

This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of...

The performance of Monte Carlo integration methods like importance-sampling or Markov-Chain Monte-Carlo procedures depends greatly on the choice of the importance- or candidate-density. Such a density must typically be "close" to the target density to yield numerically accurate results with...