Forecasting New Zealand's real GDP
Aaron Schiff and
Peter Phillips
New Zealand Economic Papers, 2000, vol. 34, issue 2, 159-181
Abstract:
Recent time series methods are applied to the problem of forecasting New Zealand's real GDP. Model selection is conducted within autoregressive (AR) and vector autoregressive (VAR) classes, allowing for evolution in the form of the models over time. The selections are performed using the Schwarz (1978) BIC and the Phillips-Ploberger (1996) PIC criteria. The forecasts generated by the data-determined AR models and an international VAR model are found to be competitive with forecasts from fixed format models and forecasts produced by the NZIER. Two illustrations of the methodology in conditional forecasting settings are performed with the VAR models. The first provides conditional predictions of New Zealand's real GDP when there is a future recession in the United States. The second gives conditional predictions of New Zealand's real GDP under a variety of profiles that allow for tightening in monetary conditions by the Reserve Bank.
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/00779950009544321 (text/html)
Access to full text is restricted to subscribers.
Related works:
Working Paper: Forecasting New Zealand's Real GDP (2000) 
Working Paper: Forecasting New Zealand's Real GDP (2000) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:nzecpp:v:34:y:2000:i:2:p:159-181
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RNZP20
DOI: 10.1080/00779950009544321
Access Statistics for this article
New Zealand Economic Papers is currently edited by Dennis Wesselbaum
More articles in New Zealand Economic Papers from Taylor & Francis Journals
Bibliographic data for series maintained by ().