On Forecasting Economic Time Series Data: A Comparative Study

Kuldeep Kumar, Dilbagh S. Gill

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Since the appearance of the book by Box and Jenkins in 1970, the use of autoregressive integrated moving average model has become widespread in analysing and forecasting economic time series data. Sims (1980) proposed a vector auto regressive model (V AR) approach as an alternative to the conventional strategy for constructing macro-economic model. Trevor and Thorp (1988) emphasised that estimating a V ARmodel using non-stationary data may result in unstable econometric relationships and suggested using Bayesian VAR model to accommodate non-stationarity. ill this paper we have compared the forecasting performance of these models using Australian macro-economic time series data.
Original languageEnglish
Pages (from-to)265-272
Number of pages8
JournalJournal of Information and Optimization Sciences
Volume19
Issue number2
DOIs
Publication statusPublished - 1998

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Time series data
Economic forecasting
Comparative study
Integrated
Macroeconomic models
Macroeconomics
Bayesian VAR
Nonstationarity
Forecasting performance
Econometric relationships
Moving average
Vector autoregressive model
VAR model

Cite this

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On Forecasting Economic Time Series Data: A Comparative Study. / Kumar, Kuldeep; Gill, Dilbagh S.

In: Journal of Information and Optimization Sciences, Vol. 19, No. 2, 1998, p. 265-272.

Research output: Contribution to journalArticleResearchpeer-review

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