Identifying an Appropriate Forecasting Model for Forecasting Total Import of Bangladesh

• Autorzy: Tanvir Khan
• Języki publikacji: EN

Abstrakty

EN

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. (original abstract)

Słowa kluczowe

PL

Modele ARIMA; Model wektorowej autoregresji; Metody prognozowania; Ekonometria; Zmienne ekonomiczne

EN

Autoregressive integrated moving average (ARIMA) models; Vector Autoregression Model (VAR); Forecasting methods; Econometrics; Economic variables

• Czasopismo: Statistics in Transition
• Rocznik: 2011
• Tom: 12
• Numer: nr 1
• Strony: 179-192

Twórcy

autor

Tanvir Khan

• University of Dhaka, Bangladesh, student

Bibliografia

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• 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.