Department of Economics and Business Economics

Timo Teräsvirta

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

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

In: International Journal of Forecasting, Vol. 30, 2014, p. 616-631.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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@article{1340063899504e3dbba24272f87ec094,
title = "Forecasting performances of three automated modelling techniques during the economic crisis 2007-2009",
abstract = "In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification 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, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are 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 from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.",
keywords = "Autometrics, Economic forecasting, Marginal Bridge estimator, Neural network, Root mean squared forecast error, RETINA, QuickNet, Nonlinear time series model",
author = "Kock, {Anders Bredahl} and Timo Ter{\"a}svirta",
note = "Campus adgang til artiklen / Campus access to the article",
year = "2014",
doi = "10.1016/j.ijforecast.2013.01.003",
language = "English",
volume = "30",
pages = "616--631",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

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

AU - Kock,Anders Bredahl

AU - Teräsvirta,Timo

N1 - Campus adgang til artiklen / Campus access to the article

PY - 2014

Y1 - 2014

N2 - In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification 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, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are 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 from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.

AB - In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification 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, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are 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 from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.

KW - Autometrics

KW - Economic forecasting

KW - Marginal Bridge estimator

KW - Neural network

KW - Root mean squared forecast error

KW - RETINA

KW - QuickNet

KW - Nonlinear time series model

U2 - 10.1016/j.ijforecast.2013.01.003

DO - 10.1016/j.ijforecast.2013.01.003

M3 - Journal article

VL - 30

SP - 616

EP - 631

JO - International Journal of Forecasting

T2 - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

ER -