Lecture 12: Nested Fixed Point Algorithm (NFXP)
Plan for lecture 12
We will go though Rust (1987) in detail. This is a path-breaking paper that introduces a methodology to estimate a single-agent dynamic discrete choice models by MLE. The main contribution is that Rust develop and implement the so-called Nested Fixed Point Algorithm (NFXP) to obtain ML estimates of the structural parameters. It's a nested algorithm, since it requires the researcher to repeatedly solving a fixed point problem for each evaluation of the sample criterion used in estimation (in this case the likelihood function). Another key contribution of Rust is the formulation of a set of assumptions, that makes dynamic discrete choice models tractable. Finally, he devise a "bottom-up approach", where aggregate demand goods are based on a micro aggregation of individual decision rules. In this first lecture on discrete choice models, we will focus on the illustrative empirical application: A simple model of engine replacement. Rust model will be the leading example of discrete choice model's throughout the lectures.
Preparation:
Material:
- Slides Download Slides
- Code solving and estimating Rust's model Download Code solving and estimating Rust's model
- Rust (NFXP Manual, 2000) Download Rust (NFXP Manual, 2000)
- Rust (JEL, 2014) Download Rust (JEL, 2014)
Online lectures
Video 1: Plan for lectures in dynamic discrete choice models
https://www.youtube.com/watch?v=SbVIgzWt8So
Links to an external site.
Video 2: Lecture 12 - part 1 - Solving Zurcher
https://www.youtube.com/watch?v=JfFCZhBYgGw
Links to an external site.
Video 3: Lecture 12 - part 1 - Structural Estimation (NFXP) and Empirical Results.
https://www.youtube.com/watch?v=YpCptgY9vzw
Links to an external site.