Forecasting Inflation and GDP Growth: Automatic Leading Indicator (ALI) Method versus Macro Econometric Structural Models (MESMS)

Abstract

This paper compares the forecast performance of the automatic leading indicator (ALI) method with the macro econometric structural model (MESM) and seeks ways of improving the ALI method. Inflation and gross domestic product growth form the forecast objects for comparison, using data from People’s Republic of China, Indonesia, and Philippines. The ALI method is found to produce better forecasts than MESMs in general, but the method is found to involve greater uncertainty in choosing indicators, mixing data frequencies, and utilizing unrestricted vector auto-regressions. Two possible improvements are found helpful to reduce the uncertainty: (i) give theory priority in choosing indicators and include theory-based disequilibrium shocks in the indicator sets; and (ii) reduce the vector auto-regressions by means of the general → specific model reduction procedure.