As the millennium draws to an end, the threat posed by the Year 2000 (Y2K) problem is inducing vast private and public spending on its remediation. In this paper, we model the Y2K problem as an anticipated, permanent loss in output whose magnitude can be lessened by investing resources in...

This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of technology shocks in explaining aggregate fluctuations. To this end we estimate the model's posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework we...

Using a two-sector endogenous growth model, this paper explores how productivity shocks in the goods and human capital producing sectors contribute to explaining aggregate cycles in output, consumption, investment and hours. To contextualize our findings, we also assess whether the human capital...

This paper estimates a dynamic stochastic general equilibrium (DSGE) model for the European Monetary Union by using Bayesian techniques. A salient feature of the model is an extension of the typically postulated quadratic cost structure for the monopolistic choice of price variables. As shown in...

In this paper, we derive a small textbook New Keynesian DSGE model to evaluate Polish and Romanian business cycles during the 2003 - 2014 period. Given the similarities between the two economies, we use an identical calibration procedure for certain coefficients and marginal prior distributions...

This paper analyzes the contribution of anticipated capital and labor tax shocks to business cycle volatility in an estimated New Keynesian DSGE model. While fiscal policy accounts for 12 to 20 percent of output variance at business cycle frequencies, the anticipated component hardly matters for...

This paper explores and quantifies the role of endogenous firm entry in amplifying and propagating shocks to the economy. To this end, we estimate two DSGE models on US data with Bayesian methods: one model with endogenous firm entry and translog preferences and one model without. Both models...

Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...