Forecasting using a Nonlinear DSGE Model

A medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model was estimated (54 variables, 29 state variables, 7 observed variables). The model includes an observed variable for stock market returns. The root-mean square error (RMSE) of the in-sample and out-of-sample forecasts was ca...

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Bibliographic Details
Main Authors:	Ivashchenko Sergey, Gupta Rangan
Format:	Article
Language:	English
Published:	Sciendo 2018-05-01
Series:	Journal of Central Banking Theory and Practice
Subjects:	nonlinear dsge quadratic kalman filter out-of-sample forecasts e32 e37 e44 e47
Online Access:	https://doi.org/10.2478/jcbtp-2018-0013

id	doaj-c7c45f5a0db445869690724437fbb31c
record_format	Article
spelling	doaj-c7c45f5a0db445869690724437fbb31c2021-09-06T19:41:32ZengSciendoJournal of Central Banking Theory and Practice2336-92052018-05-0172739810.2478/jcbtp-2018-0013jcbtp-2018-0013Forecasting using a Nonlinear DSGE ModelIvashchenko Sergey0Gupta Rangan1Saint Petersburg Institute for Economics and Mathematics (Russian Academy of Sciences); Faculty of Economics of Saint-Petersburg State University; National Research University Higher School of Economics, Saint Petersburg, Russia.Department of Economics, University of Pretoria, Pretoria, South AfricaA medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model was estimated (54 variables, 29 state variables, 7 observed variables). The model includes an observed variable for stock market returns. The root-mean square error (RMSE) of the in-sample and out-of-sample forecasts was calculated. The nonlinear DSGE model with measurement errors outperforms AR (1), VAR (1) and the linearised DSGE in terms of the quality of the out-of-sample forecasts. The nonlinear DSGE model without measurement errors is of a quality equal to that of the linearised DSGE model.https://doi.org/10.2478/jcbtp-2018-0013nonlinear dsgequadratic kalman filterout-of-sample forecastse32e37e44e47
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Ivashchenko Sergey Gupta Rangan
spellingShingle	Ivashchenko Sergey Gupta Rangan Forecasting using a Nonlinear DSGE Model Journal of Central Banking Theory and Practice nonlinear dsge quadratic kalman filter out-of-sample forecasts e32 e37 e44 e47
author_facet	Ivashchenko Sergey Gupta Rangan
author_sort	Ivashchenko Sergey
title	Forecasting using a Nonlinear DSGE Model
title_short	Forecasting using a Nonlinear DSGE Model
title_full	Forecasting using a Nonlinear DSGE Model
title_fullStr	Forecasting using a Nonlinear DSGE Model
title_full_unstemmed	Forecasting using a Nonlinear DSGE Model
title_sort	forecasting using a nonlinear dsge model
publisher	Sciendo
series	Journal of Central Banking Theory and Practice
issn	2336-9205
publishDate	2018-05-01
description	A medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model was estimated (54 variables, 29 state variables, 7 observed variables). The model includes an observed variable for stock market returns. The root-mean square error (RMSE) of the in-sample and out-of-sample forecasts was calculated. The nonlinear DSGE model with measurement errors outperforms AR (1), VAR (1) and the linearised DSGE in terms of the quality of the out-of-sample forecasts. The nonlinear DSGE model without measurement errors is of a quality equal to that of the linearised DSGE model.
topic	nonlinear dsge quadratic kalman filter out-of-sample forecasts e32 e37 e44 e47
url	https://doi.org/10.2478/jcbtp-2018-0013
work_keys_str_mv	AT ivashchenkosergey forecastingusinganonlineardsgemodel AT guptarangan forecastingusinganonlineardsgemodel
_version_	1717766047402033152

Forecasting using a Nonlinear DSGE Model

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