dc.contributor.author |
Bahramian, Pejman
|
|
dc.contributor.author |
Balcilar, Mehmet
|
|
dc.contributor.author |
Gupta, Rangan
|
|
dc.contributor.author |
Kanda, Patrick T.
|
|
dc.date.accessioned |
2016-03-15T12:39:24Z |
|
dc.date.issued |
2016-01 |
|
dc.description.abstract |
The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear
models have increasingly featured, along with linear counterparts, in the forecasting literature. In
this study, we focus on forecasting South African inflation by means of non-linear models and
using a long historical dataset of seasonally adjusted monthly inflation rates spanning from
1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can
be a salient feature of such long data, hence the relevance of evaluating non-linear models’
forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a
protracted rising trend in South African inflation data, we estimate the models for an in-sample
period of 1921:02–1966:09 and evaluate 1, 4, 12, and 24 step-ahead forecasts over an out-ofsample
period of 1966:10–2013:01. In addition, using a weighted loss function specification, we
evaluate the forecast performance of different non-linear models across various extreme economic
environments and forecast horizons. In general, we find that no competing model
consistently and significantly beats the LoLiMoT’s performance in forecasting South African
inflation. |
en_ZA |
dc.description.embargo |
2017-07-30 |
|
dc.description.librarian |
hb2015 |
en_ZA |
dc.description.uri |
http://www.tandfonline.com/loi/raec20 |
en_ZA |
dc.identifier.citation |
Patrick T. Kanda, Mehmet Balcilar, Pejman Bahramian & Rangan Gupta (2016) Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation, Applied Economics, 48:26, 2412-2427, DOI:10.1080/00036846.2015.1122731. |
en_ZA |
dc.identifier.issn |
0003-6846 (print) |
|
dc.identifier.issn |
1466-4283 (online) |
|
dc.identifier.other |
10.1080/00036846.2015.1122731 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/51890 |
|
dc.language.iso |
en |
en_ZA |
dc.publisher |
Routledge |
en_ZA |
dc.rights |
© 2016 Taylor and Francis. This is an electronic version of an article published in Applied Economics, vol. 48, no. 26, pp. 2412-2427, 2016. doi :10.1080/00036846.2015.1122731. Applied Economics is available online at : http://www.tandfonline.comloi/raec20. |
en_ZA |
dc.subject |
Inflation |
en_ZA |
dc.subject |
Forecasting |
en_ZA |
dc.subject |
Non-linear models |
en_ZA |
dc.subject |
Weighted loss function |
en_ZA |
dc.subject |
South Africa (SA) |
en_ZA |
dc.title |
Forecasting South African inflation using non-linearmodels : a weighted loss-based evaluation |
en_ZA |
dc.type |
Postprint Article |
en_ZA |