dc.contributor.author |
Gogas, Periklis
|
|
dc.contributor.author |
Papadimitriou, Theophilos
|
|
dc.contributor.author |
Plakandaras, Vasilios
|
|
dc.contributor.author |
Gupta, Rangan
|
|
dc.date.accessioned |
2017-05-02T05:39:51Z |
|
dc.date.issued |
2017-03 |
|
dc.description.abstract |
The difficulty in modelling inflation and the significance in discovering the underlying data-generating process of in-flation is expressed in an extensive literature regarding inflation forecasting. In this paper we evaluate nonlinearmachine learning and econometric methodologies in forecasting US inflation based on autoregressive and structuralmodels of the term structure. We employ two nonlinear methodologies: the econometric least absolute shrinkageand selection operator (LASSO) and the machine-learning support vector regression (SVR) method. The SVR hasnever been used before in inflation forecasting considering the term spread as a regressor. In doing so, we use a longmonthly dataset spanning the period 1871:1–2015:3 that covers the entire history of inflation in the US economy. Forcomparison purposes we also use ordinary least squares regression models as a benchmark. In order to evaluate thecontribution of the term spread in inflation forecasting in different time periods, we measure the out-of-sample fore-casting performance of all models using rolling window regressions. Considering various forecasting horizons, theempirical evidence suggests that the structural models do not outperform the autoregressive ones, regardless of themodel’s method. Thus we conclude that the term spread models are not more accurate than autoregressive modelsin inflation forecasting. |
en_ZA |
dc.description.department |
Economics |
en_ZA |
dc.description.embargo |
2018-03-31 |
|
dc.description.librarian |
hb2017 |
en_ZA |
dc.description.sponsorship |
Dr Theophilos Papadimitriou, Dr Periklis Gogas and Dr Vasilios Plakandaras have been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program ‘Education and Lifelong Learning’ of the National Strategic Reference Framework (NSRF)— Research Funding Program : THALES, under grant number MIS 380292. Investing in knowledge society through the European Social Fund. |
en_ZA |
dc.description.uri |
http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-131X |
en_ZA |
dc.identifier.citation |
Plakandaras, V, Gogas, P, Papadimitriou, T & Gupta, R 2017, 'The informational content of the term spread in forecasting the US inflation rate : a nonlinear approach', Journal of Forecasting , vol. 36, no. 2, pp. 109-122. |
en_ZA |
dc.identifier.issn |
0277-6693 (print) |
|
dc.identifier.issn |
1099-131X (online) |
|
dc.identifier.other |
10.1002/for.2417 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/60136 |
|
dc.language.iso |
en |
en_ZA |
dc.publisher |
Wiley |
en_ZA |
dc.rights |
© 2016 John Wiley and Sons, Ltd. This is the pre-peer reviewed version of the following article : The informational content of the term spread in forecasting the US inflation rate : a nonlinear approach, Journal of Forecasting, vol. 36, no. 2, pp. 109-121, 2017. doi : 10.1002/for.2417. The definite version is available at : http://onlinelibrary.wiley.comjournal/10.1002/(ISSN)1099-131X. |
en_ZA |
dc.subject |
US inflation |
en_ZA |
dc.subject |
Forecasting |
en_ZA |
dc.subject |
Least absolute shrinkageand selection operator (LASSO) |
en_ZA |
dc.subject |
Support vector regression (SVR) |
en_ZA |
dc.subject |
United States (US) |
en_ZA |
dc.title |
The informational content of the term spread in forecasting the US inflation rate : a nonlinear approach |
en_ZA |
dc.type |
Postprint Article |
en_ZA |