Forecasting national activity using lots of international predictors - an application to New Zealand
We apply “data-rich” factor and shrinkage methods to understand how large international datasets can be used to improve forecasts of New Zealand GDP. We find that exploiting a large number of international predictors can improve forecasts compared to more traditional models based on small datasets. This is in spite of New Zealand survey data capturing a substantial proportion of the predictive information in the international data. The largest forecasting accuracy gains from including international predictors are at longer forecast horizons. The forecasting performance achievable with the data-rich methods differs widely, with shrinkage methods and partial least squares performing best. We also assess the type of international data that contains the most predictive information for New Zealand growth over our sample.