Analyzing non-stationary and unbalanced growth economic models

Santa Clara University, Santa Clara CA

Investigators

Abstract

This project will develop a new method for the analysis of mathematical models of the economy. The key feature of the new method is that it will give economists new tools to build and analyze models that include not just changes over time (dynamic models) and the possibility that chance plays a role in this changes (stochastic dynamic models) but models in which the probability of a change can itself vary over time (nonstationary stochastic dynamic models). The project has clear potential to improve the methods economists currently use to answer research questions calling for these nonstationary stochastic dynamic models. In addition, the project has transformative potential; the new methods may mean that economists will be able to tackle new research problems that were impossible to answer with previous methods. If this project is fully successful, economic policymakers in the US will benefit from better scientific advice about the effects of their decisions on the US economy. The overall goal is to develop the extended function path (EFP) method for calibrating, solving, simulating and estimating non-stationary and unbalanced growth dynamic stochastic economic models. The key feature of EFT is the notion of semi-Markov processes in which transition density functions are time dependent while also memoryless with respect to the specific history that leads to a current state. The PI team plans to apply EFP to a collection of examples that do not admit conventional stationary Markov solutions. Among these examples are two research projects. The first uses the method to analyze unbalanced growth patterns in the U.S. economy between 1963 and 2012. The second project will attempt to explain the recent economic crisis in the context of a new Keynesian model with a Taylor rule for nominal interest rates and possible non-Markov changes in government policies.