Evaluating the forecasting power of foreign Country\u27s income growth: a global analysis

Purpose: The objective of the paper is to explore the out-of-sample forecasting connections in income growth across the globe. Design/methodology/approach: An autoregressive distributed lag (ARDL) framework is employed and the forecasting performance is analyzed across several horizons using different forecast combination techniques. Findings: Results show that the foreign country\u27s income provides superior forecasts beyond what is provided by the country\u27s own past income movements. Superior forecasting power is particularly held by Belgium, Korea, New Zealand, the UK and the US, while these countries\u27 income is rather difficult to predict by global counterparts. Contrary to conventional wisdom, improved forecasts of income can be obtained even for longer horizons using our approach. Results also show that the forecast combination techniques yield higher forecasting gains relative to individual model forecasts, both in magnitude and the number of countries. Research limitations/implications: The forecasting paths of income movement across the globe reveal that predictive power greatly differs across countries, regions and forecast horizons. The countries that are difficult to predict in the short run are often seen to be predictable by global income movements in the long run. Practical implications: Even while it is difficult to predict the income movements at an individual country level, combining information from the income growth of several countries is likely to provide superior forecasting gains. And these gains are higher for long-horizon forecasts as compared to the short-horizon forecast. Social implications: In evaluating the forward-looking social implications of economic policy changes, the policymakers should also consider the possible global forecasting connections revealed in the study. Originality/value: Employing an ARDL model to explore global income forecasting connections across several forecast horizons using different forecast combination techniques