On Parameter Estimation in Unobserved Components Models subject to Linear Inequality Constraints
Abstract
We propose a new \textit{quadratic programming-based} method of approximating a nonstandard density using a multivariate Gaussian density. Such nonstandard densities usually arise while developing posterior samplers for unobserved components models involving inequality constraints on the parameters. For instance, Chan et al. (2016) provided a new model of trend inflation with linear inequality constraints on the stochastic trend. We implemented the proposed quadratic programming-based method for this model and compared it to the existing approximation. We observed that the proposed method works as well as the existing approximation in terms of the final trend estimates while achieving gains in terms of sample efficiency.
Keywords
Cite
@article{arxiv.2110.12149,
title = {On Parameter Estimation in Unobserved Components Models subject to Linear Inequality Constraints},
author = {Abhishek K. Umrawal and Joshua C. C. Chan},
journal= {arXiv preprint arXiv:2110.12149},
year = {2023}
}
Comments
9 pages, 6 figures, Accepted for presentation at MLECON: Machine Learning meets Econometrics workshop. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia