Department of Economics and Business Economics

Timo Teräsvirta

Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009

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Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009. / Kock, Anders Bredahl; Teräsvirta, Timo.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2011.

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Bibtex

@techreport{831870e104a0489a8b5eb9d85b995182,
title = "Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009",
abstract = "In this work we consider forecasting macroeconomic variables during an economic crisis. The focus is on a speci…c class of models, the so-called single hidden-layer feedforward autoregressive neural network models. What makes these models interesting in the present context is that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. These models are often difficult to estimate, and we follow the idea of White (2006) to transform the speci…cation and nonlinear estimation problem into a linear model selection and estimation problem. To this end we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, well known to time series econometricians, and the Marginal Bridge Estimator, better known to statisticians and microeconometricians.The performance of these three model selectors is compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series of the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. Forecast accuracy is measured by the root mean square forecast error. Hypothesis testing is also used to compare the performance of the different techniques with each other.",
keywords = "Autometrics, economic forecasting, Marginal Bridge estimator, neural network, nonlinear time series model, Wilcoxon’s signed-rank test",
author = "Kock, {Anders Bredahl} and Timo Ter{\"a}svirta",
year = "2011",
language = "English",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009

AU - Kock,Anders Bredahl

AU - Teräsvirta,Timo

PY - 2011

Y1 - 2011

N2 - In this work we consider forecasting macroeconomic variables during an economic crisis. The focus is on a speci…c class of models, the so-called single hidden-layer feedforward autoregressive neural network models. What makes these models interesting in the present context is that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. These models are often difficult to estimate, and we follow the idea of White (2006) to transform the speci…cation and nonlinear estimation problem into a linear model selection and estimation problem. To this end we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, well known to time series econometricians, and the Marginal Bridge Estimator, better known to statisticians and microeconometricians.The performance of these three model selectors is compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series of the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. Forecast accuracy is measured by the root mean square forecast error. Hypothesis testing is also used to compare the performance of the different techniques with each other.

AB - In this work we consider forecasting macroeconomic variables during an economic crisis. The focus is on a speci…c class of models, the so-called single hidden-layer feedforward autoregressive neural network models. What makes these models interesting in the present context is that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. These models are often difficult to estimate, and we follow the idea of White (2006) to transform the speci…cation and nonlinear estimation problem into a linear model selection and estimation problem. To this end we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, well known to time series econometricians, and the Marginal Bridge Estimator, better known to statisticians and microeconometricians.The performance of these three model selectors is compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series of the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. Forecast accuracy is measured by the root mean square forecast error. Hypothesis testing is also used to compare the performance of the different techniques with each other.

KW - Autometrics, economic forecasting, Marginal Bridge estimator, neural network, nonlinear time series model, Wilcoxon’s signed-rank test

M3 - Working paper

BT - Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -