Paul M. Beaumont

Learn More
We use a distributed parallel genetic algorithm (DPGA) to nd numerical solutions to a single state deterministic optimal growth model for both the innnite and nite horizon cases. To evaluate the DPGA we consider a version of the Taylor-Uhlig problem for which we know the analytical solutions. The rst-order conditions for the innnite horizon case lead to a(More)
There has been considerable interest recently in long memory models able to capture complex features in both the spectral density and autocorrelation functions of time series data. We present a multiple frequency Gegenbauer autoregressive moving average, or k-factor GARMA, model and we derive the asymptotic distributions for a conditional sum of squares(More)
We study a simple model based upon the Lucas framework where heterogeneous agents behave rationally in a fully intertemporal setting but do not know other investors' personal preferences, wealth or investment portfolios. As a consequence , agents initially do not know the equilibrium asset pricing function and must make guesses which they update via(More)
We examine market dynamics in a discrete-time, Lucas-style asset-pricing model with heterogeneous , utility-optimizing agents. Finitely many agents trade a single asset paying a stochastic dividend. All agents know the probability distribution of the dividend but not the private information such as wealth and asset holdings of other agents. The market(More)
We examine market dynamics in a discrete-time, Lucas-style asset-pricing model with heterogeneous, utility-optimizing agents. Finitely many agents trade a single asset paying a stochastic dividend, and know the probability distribution of the dividend but not the private information of other agents. The market clearing price is determined endogenously in(More)
We examine market dynamics in a Lucas-style, asset-pricing model with heterogeneous traders who know the distribution of dividends but not the private information of other traders. Agents optimize a CRRA utility function while learning about aggregate states in order to better estimate the equilibrium pricing function. Our goal is to determine whether and(More)
  • 1