Generalized Vector Autoregression Controlling Intervention and Volatility for Climatic Variables

Md. Ashek Al Naim (), Md. Abeed Hossain Chowdhury (), Md. Abdul Khalek () and Md. Ayub Ali ()
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Md. Ashek Al Naim: University of Rajshahi, Department of Statistics
Md. Abeed Hossain Chowdhury: BARC
Md. Abdul Khalek: University of Rajshahi, Department of Statistics
Md. Ayub Ali: University of Rajshahi, Department of Statistics

A chapter in Data Science and SDGs, 2021, pp 79-91 from Springer

Abstract: Abstract The purpose of this study is to build a time series model for forecasting the climatic variables of Rajshahi district using the VAR model controlling intervention and volatility. Seven models for seven climatic variables are found, and the stability of every model is checked with proper validation techniques. The fitted models are GVAR with GARCH (2,1) and intervention for Cloud coverage; GVAR with GARCH (3,1) and intervention for Relative Humidity; ARIMA (1,0,1) with GARCH (1,1) for rainfall, GVAR with GARCH (2,1), and intervention for maximum Temperature; GVAR with ARCH (2) and intervention for minimum temperature; GVAR with intervention for sunshine; and ARIMA (2,0,2) for wind speed. The stable models are used to forecast the daily data which may be beneficial to people and policymakers. Finally, it is found by forecasting that Maximum Temperature (T1), Humidity (H), Bright Sunshine (S), and Wind Speed (W) might be shown upward trend while Minimum Temperature (T2), Rainfall (R), and Cloud Coverage (Cl) might be shown decreasing trend from the year 2018 to 2022. Considering the finding of this study, Government and policymakers can make people aware of the adverse effect of climate change.

Keywords: VAR; ARCH; GARCH; ARIMA; GVAR; Stationarity; Stability; Climatic variables; Climate change (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-1919-9_7

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DOI: 10.1007/978-981-16-1919-9_7

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