SMU SOE Seminar (Mar 13, 2019, 4-5.30pm): Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods

Please click here if you are unable to view this page.

ABSTRACT We propose to combine smoothing, simulations and sieve approximations to solve for the value function in a general class of dynamic discrete choice (DDC) models. We use Monte Carlo methods to approximate the Bellman operator defining the solution. The random Bellman operator is not smooth which complicates the search for the corresponding solution which is generally non-differentiable. To circumvent this issue, we introduce a smoothed version of the random Bellman operator and solve for the corresponding smoothed value function using projection-based methods where the unknown solution is approximated by a set of basis functions. We provide an asymptotic theory for the approximate solution and show that it converges with root-N-rate, where N is number of Monte Carlo draws, towards a Gaussian process. We examine its performance in practice through a set of numerical experiments and find that the new method is computationally fast and reliable and provides a good approximation to the unknown solution. Click here to view the CV.
		PRESENTER Dennis Kristensen University College London
		RESEARCH FIELDS Econometric Theory Applied Microeconomics Quantitative Finance
		DATE: 13 March 2019 (Wednesday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903