Firm's R & D Behavior Under Rational Expectations

Dimitrios Thomakos and Prasad S. Bhattacharya
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Prasad S. Bhattacharya: School of Accounting, Economics and Finance, Faculty of Business and Law, Deakin University, Australia

Indian Economic Review, 2005, vol. 40, issue 2, 145-165

Abstract: This paper reports results from a forecasting study for inflation, industrial output and exchange rates for India. We cannot reject the null hypothesis for linearity for all series used except for the growth rate of the foreign exchange series and our analysis is based on linear models, ARIMA and bivariate transfer functions and restricted VAR. Forecasting performance is evaluated using the models’ root mean-squared error differences and Theil’s inequality coefficients from recursive origin static, fixed origin dynamic and rolling origin dynamic forecasts. For models based on weekly data, based on RMSEs, we find that the bivariate models improve upon the forecasts of the ARIMA model while for models based on monthly data the ARIMA model has almost always better performance. In choosing between the two bivariate models on the basis of RMSEs, our overall results tend to support the use of a restricted VAR, as this model had the best forecasting performance more frequently than the transfer function model.

Keywords: ARIMA; Transfer Function; Vector Autoregression; Forecasting; Inflation; Industrial Output (search for similar items in EconPapers)
JEL-codes: C32 C53 C59 (search for similar items in EconPapers)
Date: 2005
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