ARTICLE
ABSTRACT
Forecasting future values of economic variables are some of the most critical tasks of a country. Especially the values related to foreign trade are to be forecasted efficiently as the need for planning is great in this sector. The main obJective of this research paper is to select an appropriate model for time series forecasting of total import (in taka crore) of Bangladesh. The decision throughout this study is mainly concerned with seasonal autoregressive integrated moving average (SARIMA) model, Holt-Winters' trend and seasonal model with seasonality modeled additively and vector autoregressive model with some other relevant variables. An attempt was made to derive a unique and suitable forecasting model of total import of Bangladesh that will help us to find forecasts with minimum forecasting error.
KEYWORDS
ARIMA model, Holt Winters' trend and seasonality method, VAR model, Forecasting accuracy, Out-of-sample accuracy measurement.
REFERENCES
AMISANO, G. AND GIANNINI, C. (1997). Topics in Structural VAR Econometrics, Springer-Verlag, Berlin, 2nd edition.
GUJRATI, D. N. AND SANGEETHA (2007). Basic Econometrics, McGraw-Hill Book Co, New York.
HAMILTON,J.D. (1994). Time Series Analysis, Princeton University Press, Princeton.
MAKRIDAKIS, S., WHEELWRITGHT, S. C. AND HYNDMAN, R. J. (1998). Forecasting Methods and Applications, John Wiley and Sons, Ink., New York.
PFAFF, B. (2008). VAR, SVAR and SVEC Models: Implementation within R Package vars, New York. URL: http://CRAN.R-proJect.org/package=vars.
