Forecasting the Vietnam index of industrial production through the VAR model - Le, Thuyet Huy.
Abstract (The aim of this study was to forecast to IIP of Vietnam for selected industries through VAR modeling, standard modeling procedure used in time series analysis because of its simplicity and usefulness. Four IIPs were forecasted: overall IIP of industries, IIP of the manufacturing or processing industry, industry section C, IIP of the electricity production and distribution industry, industry section D; and IIP of the water and gas industry, industry section E. The VAR models used in this study were limited to the VAR and VAR models. Four leading economic indicators were included in the VAR models but the purpose of the study was not to forecast them but to improve the forecasts of IIP by incorporating their influence on the IIP in the different VAR models considered. These leading economic indicators included oil price arrel, import million , foreign direct investment FDI million , and electricity price vnd/kwh. The data on electricity price seemed to be unusual because of a policy of the Vietnamese government of fixing the price of electricity within a year. Further, aside from the inclusion of these leading indicators, three dummy variables were included as exogeneous variables in the VAR models to indicate the effect on the IIPs of three time points when there were persistent dips and spikes in the IIP data. The time series variables showed exhibited nonstationarity and seasonality, hence, the observations were logarithm-transformed and then differenced before modeling. The initial VAR and VAR models treated all IIP variables and leading indicators as endogenous but both models turned out to be unsatisfactory because the resulting model residuals were autocorrelated at all lags from 1 to 20. These two initial models were perhaps not able to capture the relationships among the variables because of the little variability in the electricity price as an endogenous variable. So, two alternative models were tried: VAR with electricity price as included as exogenous variable and VAR with electricity price included as endogenous variable. When the MAPEs of the VAR model with electricity price included as exogenous variable and the VAR model with electricity price excluded were compared, the VAR model with electricity price included as exogenous variable had uniformly better MAPE values across the sets of variables and may be considered as the "better" of the two. This model had a MAPE of 2.72% for forecasting the oeverall IIP of industries, a MAPE of 3.71% for forecasting the IIP of industry section C, 2.46% for forecasting the IIP of industry section D, and 1.25% for forecasting the IIP of industry section E. For future studies, other approaches to time series modeling that take into account nonstationarity and seasonality may be tried. One approach would be to use models that do no adjust away seasonality prior to using the time series. Models under this approach included the seasonal integration-cointegration model and the periodic integration-cointegration model as discussed by Dua and Kumawat 2005. Another approach is the multivariate singular spectrum anyalsis which is valid for linear or non-linear as well as stationary or non-stationary time series as discussed by Zhigljavsky, Hassini, and Heravi 2009.
May 2018
Thesis (M.S.Statistics)--University of the Philippines, Diliman.
Keywords: VAR model