Showing 1 - 10 of 23
In this paper we introduce a technique for perfect simulation from the stationary distribution of a standard model of …
Persistent link: https://www.econbiz.de/10011191164
This paper strengthens the Hopenhayn-Prescott stability theorem for monotone economies by extending it to a significantly larger class of models. We provid general conditions for existence, uniqueness and stability of stationary distributions. The conditions in our main result are both necessary...
Persistent link: https://www.econbiz.de/10010583476
In equilibrium models of firm dynamics, the stationary equilibrium distribution of firms summarizes the predictions of the model for a given set of primitives. Focusing on Hopenhayn's seminal model of firm dynamics with entry and exit (Econometrica, 60:5, 1992, p. 1127–1150), we provide an...
Persistent link: https://www.econbiz.de/10010583477
In equilibrium models of firm dynamics, the stationary equilibrium distribution of firms summarizes the predictions of the model for a given set of primitives. Focusing on Hopenhayn's seminal model of firm dynamics with entry and exit (Econometrica, 60:5, 1992, p. 1127–1150), we provide an...
Persistent link: https://www.econbiz.de/10010822749
In this paper we introduce a technique for perfect sampling from the stationary distribution of possibly non-monotone regenerative processes, such as those that describe industry dynamics (where regeneration corresponds to the process of exit of firms and entry of new ones). The algorithm we...
Persistent link: https://www.econbiz.de/10010822755
In this paper we introduce a technique for perfect simulation from the stationary distribution of a standard model of …
Persistent link: https://www.econbiz.de/10010822758
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 L1 error of the estimator always converges to zero with probability one, and often at a parametric rate. A related technique for computing...
Persistent link: https://www.econbiz.de/10005422905
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...
Persistent link: https://www.econbiz.de/10005342929
In this paper we introduce a technique for perfect simulation from the stationary distribution of a standard model of …
Persistent link: https://www.econbiz.de/10010754830
Persistent link: https://www.econbiz.de/10011403245