Inference on stochastic time-varying coefficient models

By Liudas Giraitis, George Kapetanios, Tony Yates. 2014

137 citations recorded by the source corpus.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/70f76c60feed13f440c75855835b13a15fb33f1a

How researchers see this paper

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In Economics and Econometrics

1. Ivan Petrella
2. K. Petrova
3. Davide Delle Monache

Citations in the corpus

• Adaptive models and heavy tails with an application to inflation forecasting (2017, background): “Giraitis, Kapetanios, and Yates (2014) propose a non-parametric estimation approach for time-varying coefficient models.”
• A quasi-Bayesian local likelihood approach to time varying parameter VAR models (2019, methodology): “…normal kernel in (5) for the kernel weights with bandwidth of T 0:5 and a Minnesota type prior6 with loose overall shrinkage = 1: Our choice of bandwidth H = T 0:5 is motivated by the optimal bandwidth choice used for inference in time varying random coe¢ cient models, see Giraitis et al. (2014).”