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Gross domestic product (GDP) comes under the heading of National Accounts, Which is a subject in macroeconomics. This study aims at to find appropriate VAR (Vector Autoregressive) model for the variables agriculture, industry and services in multivariate time series, to check out the causality among the variables agriculture, industry, and service. In addition to data description and forecasting, here the VAR model is also used for structural inference and policy analysis. But before these we must verify the validation of the model in different time period, because a forecasting model may lose its validity and suitability as time progresses.The basic aim of this study was to check out the causality or inter dependency between the variables agriculture, industry and services on GDP of Bangladesh. Also to forecast the increasing GDP using VAR model. From the above study and estimation, VAR is an appropriate model for the GDP of Bangladesh. The author also found various measures of forecasting accuracy for the model. Author have found strong evidence of inter dependency or causality among the three underlying variable.
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Gross domestic product (GDP) comes under the heading of National Accounts, Which is a subject in macroeconomics. This study aims at to find appropriate VAR (Vector Autoregressive) model for the variables agriculture, industry and services in multivariate time series, to check out the causality among the variables agriculture, industry, and service. In addition to data description and forecasting, here the VAR model is also used for structural inference and policy analysis. But before these we must verify the validation of the model in different time period, because a forecasting model may lose its validity and suitability as time progresses.The basic aim of this study was to check out the causality or inter dependency between the variables agriculture, industry and services on GDP of Bangladesh. Also to forecast the increasing GDP using VAR model. From the above study and estimation, VAR is an appropriate model for the GDP of Bangladesh. The author also found various measures of forecasting accuracy for the model. Author have found strong evidence of inter dependency or causality among the three underlying variable.
Biografía del autor
Ms.Naz Afrin Sultana is currently pursing M.S. at the Institute of Statistical Research and Training (ISRT) of Dhaka University from Bangladesh. She received her B.S. degree from the same institute. Her research interests include Bayesian Statistics, Multivariate Analysis etc.
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GDP OF BANGLADESH: INTER DEPENDENCY WITH RELEVANT VARIABLES
Verlag:
LAP LAMBERT Academic Publishing Jun 2011, 2011
ISBN 10: 3845400862
ISBN 13: 9783845400860
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Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware -Gross domestic product (GDP) comes under the heading of National Accounts, Which is a subject in macroeconomics. This study aims at to find appropriate VAR (Vector Autoregressive) model for the variables agriculture, industry and services in multivariate time series, to check out the causality among the variables agriculture, industry, and service. In addition to data description and forecasting, here the VAR model is also used for structural inference and policy analysis. But before these we must verify the validation of the model in different time period, because a forecasting model may lose its validity and suitability as time progresses.The basic aim of this study was to check out the causality or inter dependency between the variables agriculture, industry and services on GDP of Bangladesh. Also to forecast the increasing GDP using VAR model. From the above study and estimation, VAR is an appropriate model for the GDP of Bangladesh. The author also found various measures of forecasting accuracy for the model. Author have found strong evidence of inter dependency or causality among the three underlying variable.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch. Artikel-Nr. 9783845400860
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