Please be advised that the site will be down for maintenance on Sunday, September 1, 2024, from 08:00 to 18:00, and again on Monday, September 2, 2024, from 08:00 to 09:00. We apologize for any inconvenience this may cause.
dc.contributor.author | Balcilar, Mehmet![]() |
|
dc.contributor.author | Gupta, Rangan![]() |
|
dc.contributor.author | Van Eyden, Renee![]() |
|
dc.contributor.author | Thompson, Kirsten L.![]() |
|
dc.contributor.author | Majumdar, Anandamayee![]() |
|
dc.date.accessioned | 2018-04-24T12:54:14Z | |
dc.date.issued | 2018-08 | |
dc.description.abstract | In this paper we test the forecasting ability of three estimated financial conditions indices (FCIs) with respect to key macroeconomic variables of output growth, inflation and interest rates. We do this by forecasting the aforementioned macroeconomic variables based on the information contained in the three alternative FCIs using a Bayesian VAR (BVAR), nonlinear logistic vector smooth transition autoregression (VSTAR) and nonparametric (NP) and semi-parametric (SP) regressions, and compare the results with the standard benchmarks of random-walk, univariate autoregressive and classical VAR models. The three FCIs are constructed using rolling-window principal component analysis (PCA), dynamic model averaging (DMA) in the context of a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model, and a time-varying parameter vector autoregressive (TVP-VAR) model with constant factor loadings. Our results suggest that the VSTAR model performs best in the case of forecasting output and inflation, while a SP specification proves to be the best for forecasting the interest rate. More importantly, statistical testing for significant differences in forecast errors across models corroborates the finding of superior predictive ability of the nonlinear models. | en_ZA |
dc.description.department | Economics | en_ZA |
dc.description.embargo | 2020-08-26 | |
dc.description.librarian | hj2018 | en_ZA |
dc.description.uri | http://www.elsevier.com/locate/qre | en_ZA |
dc.identifier.citation | Balcilar, M., Gupta, R., Van Eyden, R. et al. 2018, 'Comparing the forecasting ability of financial conditions indices: the case of South Africa', Quarterly Review of Economics and Finance, vol. 69, pp. 245-259. | en_ZA |
dc.identifier.issn | 1062-9769 | |
dc.identifier.other | 10.1016/j.qref.2018.03.012 | |
dc.identifier.uri | http://hdl.handle.net/2263/64711 | |
dc.language.iso | en | en_ZA |
dc.publisher | Elsevier | en_ZA |
dc.rights | © 2018 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Quarterly Review of Economics and Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published inQuarterly Review of Economics and Finance, vol. 69, pp. 245-259, 2018. doi : 10.1016/j.qref.2018.03.012. | en_ZA |
dc.subject | Financial conditions indices (FCIs) | en_ZA |
dc.subject | Bayesian VAR (BVAR) | en_ZA |
dc.subject | Vector smooth transition autoregression (VSTAR) | en_ZA |
dc.subject | Nonparametric (NP) regression | en_ZA |
dc.subject | Semi-parametric (SP) regression | en_ZA |
dc.subject | Principal component analysis (PCA) | en_ZA |
dc.subject | Dynamic model averaging (DMA) | en_ZA |
dc.subject | Nonlinear logistic smooth transition vector autoregressive model | en_ZA |
dc.title | Comparing the forecasting ability of financial conditions indices : the case of South Africa | en_ZA |
dc.type | Postprint Article | en_ZA |