Identifying an Appropriate Forecasting Model for Forecasting Total Import of Bangladesh

Khan, Tanvir

Author

Khan Tanvir (University of Dhaka, Bangladesh, student)

Title

Identifying an Appropriate Forecasting Model for Forecasting Total Import of Bangladesh

Source

Statistics in Transition, 2011, vol. 12, nr 1, s. 179-192, rys., tab., bibliogr. 5 poz.

Keyword

Zmienne ekonomiczne, Modele ARIMA, Model wektorowej autoregresji, Ekonometria, Metody prognozowania
Economic variables, Autoregressive integrated moving average (ARIMA) models, Vector Autoregression Model (VAR), Econometrics, Forecasting methods

Note

summ.

Country

Bangladesz
Bangladesh

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. (original abstract)

Accessibility

The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business
The Main Library of the Wroclaw University of Economics

Full text

Bibliography

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.

Cited by

ISSN

1234-7655

Language

eng

BazEkon - The Main Library of the Cracow University of Economics

Main menu