Do GARCH model improve forecasts of Malaysian log production? A comparative ARIMA and GARCH analysis
DOI:
https://doi.org/10.55573/JISED.118779Keywords:
ARIMA, GARCH, log production, Malaysia, time-series forecasting, volatility, forestryAbstract
Reliable forecasts of log production are important for timber-supply planning, sustainable forest management. This study evaluates the forecasting performance of conventional conditional-mean models against conditional-variance models for Malaysian logs production, a series characterized by non-stationarity and distinct structural changes. Utilizing annual production data from 1947 to 2021, the research methodologically compares the out-of-sample accuracy of various autoregressive integrated moving average (ARIMA) specifications with generalized autoregressive conditional heteroskedasticity (GARCH) type models. The models were estimated using an in-sample period from 1947 to 2002, reserving 2003 to 2021 as a chronological holdout sample for evaluation based on RMSE, MAE, and MAPE metrics. The results demonstrate that the ARIMA(0,2,1) model outperforms GARCH(1,1) specification. These findings provide a substantial forecasting advantage, offering forestry stakeholders a robust analytical tool for sustainable resource planning.
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