Forecasting the Growth Rate of GDP and its Indicators Remittance and Labor Force Participation of Bangladesh using VECM and ARIMA Models

Akash Saha, Murshida Khanam
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Abstract

The present study used VECM model and ARIMA model for modeling the growth rate of GDP, Remittances and total Labor Force Participation. All of the data of this study are collected from the World Bank Database with indicators as the time series variable from 1999 to 2020. The stationarity of the time series variables has been tested by the Augmented Dickey-Fuller test and investigated that they become stationary at first difference. The lag selection criteria has been applied to find out the no. of lags needed to be included in the model. Lag 2 has been selected for this study. VEC model has been applied in the time series data. Also the ARIMA model imposed on these time series variables. These two models have been used for forecasting. Among the predictions obtained by these two models the better one has been selected by using RMSE, MAPE and MAE. It has been found that for growth rate of GDP and total Labor Force Participation, the ARIMA model is more preferable but for Remittance, VECM is desirable. It has been found that the Remittance and Labor Force have significant positive impact on the growth of GDP. Also, a long run relationship has been found among the growth rate of GDP, Remittances and total Labor Force Participation. Dhaka Univ. J. Sci. 70(2): 42-47, 2022 (July)
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利用VECM和ARIMA模型预测孟加拉国GDP增长率及其指标汇款和劳动力参与率
本研究采用VECM模型和ARIMA模型对GDP增长率、汇款和总劳动力参与率进行建模。本研究的所有数据均来自世界银行数据库,以指标为时间序列变量,时间跨度为1999 - 2020年。用增广Dickey-Fuller检验检验了时间序列变量的平稳性,并研究了它们在一差时变得平稳性。应用滞后选择准则来找出不存在的问题。需要在模型中包含延迟。本研究选择Lag 2。将VEC模型应用于时间序列数据。还有ARIMA模型对这些时间序列变量的作用。这两个模型已被用于预测。利用RMSE、MAPE和MAE等方法对两种模型的预测结果进行了分析,选出了较好的预测结果。研究发现,对于GDP增长率和总劳动力参与率,ARIMA模型更可取,而对于汇款,VECM模型更可取。研究发现,汇款和劳动力对GDP增长有显著的正向影响。此外,国内生产总值增长率、汇款和总劳动力参与率之间存在长期关系。达卡大学学报(自然科学版),70(2):42-47,2022 (7)
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