Recently, Bhatti and Khan considered various functional forms to see their suitability in terms of efficiency and competency in assessing production process based on agricultural data. This paper reviews some models recently used in the literature and selects the most suitable one for measuring the production process of 21 major manufacturing industries in Bangladesh. In particular, the paper estimates and tests the coefficients of the production inputs for each of the selected manufacturing industries using Bangladesh Bureau of Statistics annual data over the period 1982‐1983 through 1991‐1992.
PurposeThe purpose of this paper is to generate three types of forecasts, namely, historical, ex‐post and ex‐ante, using the world famous Box‐Jenkins time series models for motor, mash and mung prices in Bangladesh.Design/methodology/approachThe models on the basis of which these forecasts have been computed were selected by six important information criteria such as Akaike's Information Criterion (AIC), Schwarz's Bayesian Information Criterion (BIC), Theil's R2, Theil's R2, SE(σ) and Mean Absolute Percent Errors (MAPEs). In order to examine the forecasting performance of the selected models, three types of forecast errors were estimated, i.e. root mean square percent errors (RMSPEs), mean percent forecast errors (MPFEs) and Theil's inequality coefficients (TICs).FindingsThe estimates suggest that in most cases the forecasting performances of the models in question are quite satisfactory.Originality/valueThe models developed in this paper can be used for policy purposes as far as price forecasts of the commodities are concerned.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.