On Parameter Estimation in Unobserved Components Models subject to
Linear Inequality Constraints
- URL: http://arxiv.org/abs/2110.12149v1
- Date: Sat, 23 Oct 2021 05:58:19 GMT
- Title: On Parameter Estimation in Unobserved Components Models subject to
Linear Inequality Constraints
- Authors: Abhishek K. Umrawal, Joshua C.C. Chan
- Abstract summary: We propose a new method of approximating a nonstandard density using a multivariate Gaussian density.
We observe that the proposed new method works as good as the existing approximation in terms of the final trend estimates.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a new 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, Chat et al. (2016) propose a new model of trend inflation with linear
inequality constraints on the stochastic trend. We implement the proposed new
method for this model and compare it to the existing approximation. We observe
that the proposed new method works as good as the existing approximation in
terms of the final trend estimates while achieving greater gains in terms of
sample efficiency.
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