Working Paper: CEPR ID: DP13245
Authors: Fabio Canova; Christian Matthes
Abstract: We describe how to use the composite likelihood to ameliorateestimation, computational, and inferential problems in dynamic stochasticgeneral equilibrium models.We present a number of situations where the methodology has the potential toresolve well-known problems and formally justifies existing practices. In each case we consider, we provide an exampleto illustrate how the approach works and its properties in practice.
Keywords: Dynamic Structural Models; Composite Likelihood; Identification; Singularity; Large Scale Models; Panel Data
JEL Codes: C10; E27; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| composite likelihood (C10) | ameliorate small sample identification problems (C55) |
| composite likelihood (C10) | resolve population identification problems (R23) |
| composite likelihood (C10) | identify structural parameters (C51) |
| composite likelihood (C10) | address singularity problems (C62) |
| composite likelihood (C10) | facilitate estimation of parameters in large-scale structural models (C51) |