Pruned Skewed Kalman Filter and Smoother: With Application to the Yield Curve

Gaygysyz Guljanov, Willi Mutschler and Mark Trede

No 78, Dynare Working Papers from CEPREMAP

Abstract: The Skewed Kalman Filter is a powerful tool for statistical inference of asymmetrically distributed time series data. However, the need to evaluate Gaussian cumulative distribution functions (cdf) of increasing dimensions, creates a numerical barrier such that the filter is usually applicable for univariate models and under simplifying conditions only. Based on the intuition of how skewness propagates through the state-space system, a computationally efficient algorithm is proposed to prune the overall skewness dimension by discarding elements in the cdfs that do not distort the symmetry up to a pre-specified numerical threshold. Accuracy and efficiency of this Pruned Skewed Kalman Filter for general multivariate state-space models are illustrated through an extensive simulation study. The Skewed Kalman Smoother and its pruned implementation are also derived. Applicability is demonstrated by estimating a multivariate dynamic Nelson-Siegel term structure model of the US yield curve with Maximum Likelihood methods. We find that the data clearly favors a skewed distribution for the innovations to the latent level, slope and curvature factors.

Keywords: state-space models; skewed Kalman filter; skewed Kalman smoother; closed skew-normal; dimension reduction; yield curve; term structure; dynamic Nelson-Siegel (search for similar items in EconPapers)
Pages: 34 pages
Date: 2022-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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Working Paper: Pruned Skewed Kalman Filter and Smoother: With Application to the Yield Curve (2022)
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