Variance decompositions of nonlinear time series using stochastic simulation and sensitivity analysis

Harris, Thomas J.; Yu, Wei

doi:10.1007/s11222-011-9230-7

Cited by 3 publications

(1 citation statement)

References 43 publications

(56 reference statements)

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“…We then introduce our ‘new’ method called the total variance method. This method is ‘new’ because, although it has been used widely in engineering and statistics (Harris and Yu (2012)), it has not been formally introduced and well documented in economics, especially for a nonlinear DSGE framework.…”

Section: Variance Decomposition Methods For Nonlinear Economic Modelsmentioning

confidence: 99%

Variance Decomposition Analysis for Nonlinear Economic Models1

Isakin

Ngo

2020

Oxf Bull Econ Stat

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In this paper, we propose a new method called the total variance method and algorithms to compute and analyse variance decomposition for nonlinear economic models. We provide theoretical and empirical examples to compare our method with the only existing method called generalized forecast error variance decomposition (GFEVD). We find that the results from the two methods are different when shocks are multiplicative or interacted in nonlinear models. We recommend that when working with nonlinear models researchers should use the total variance method in order to see the importance of indirect variance contributions and to quantify correctly the relative variance contribution of each structural shock. JEL Classification numbers: C15, C32, C53, E37. *We would like to thank Francesco Zanetti (Editor) and three anonymous referees for their excellent comments/suggestions. We also thank participants at the 2018 North American Summer Meeting of the Econometric Society and the 2019 SNDE Meeting at the Dallas Fed for their comments. We acknowledge the financial support from the Faculty Scholarship Initiative (FSI) Program of the Cleveland State University. We are also grateful for the support from the Ohio Supercomputer Center. This paper was previously circulated under the title 'Variance Decomposition Analysis for Nonlinear DSGE Models: An Application with ZLB'. 1 An incomplete list of papers using nonlinear models with a ZLB constraint includes Fernandez-Villaverde et al.

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Section: Variance Decomposition Methods For Nonlinear Economic Modelsmentioning

confidence: 99%

Variance Decomposition Analysis for Nonlinear Economic Models1

Isakin

Ngo

2020

Oxf Bull Econ Stat

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A comparison of nonlinear control performance assessment techniques for nonlinear processes

Wilson

Young

2012

Can J Chem Eng

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Assessing the quality of industrial control loops is an important auditing task for the control engineer. However there are complications when considering the ubiquitous nonlinearities present in many industrial control loops. If one simply ignores these nonlinearities, there is the danger of over-estimating the performance of the control loop in rejecting disturbances, and thereby possibly overlooking loops that need attention. To address this problem several techniques have been recently developed to extend the control performance assessment (CPA) of single input/single output linear systems to nonlinear systems. This paper surveys these nonlinear CPA techniques and compares them using three case studies. These results can be used to guide control engineers to select the most suitable CPA techniques for their particular applications.

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A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems

Liu

Tong

et al. 2021

Computer Methods in Applied Mechanics and Engineering

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Variance decompositions of nonlinear time series using stochastic simulation and sensitivity analysis

Cited by 3 publications

References 43 publications

Variance Decomposition Analysis for Nonlinear Economic Models1

Variance Decomposition Analysis for Nonlinear Economic Models1

A comparison of nonlinear control performance assessment techniques for nonlinear processes

A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems

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