Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities

Abstract

Forecasting multiple macroeconomic variables with accounting identity restrictions, also known as macroframework, is useful for presenting an internally consistent economic narrative and is widely used in policy institutions. Macroframework forecasting, however, is challenging. Forecasters often have information about only a subset of (known) variables, and in the absence of a systematic way to forecast the rest of the (unknown) variables, the task is resource-intensive and involves ad-hoc adjustments. We propose a novel 2-step method to forecast unknown variables conditional on known variables, which reflects historical correlations and satisfies accounting identities. The method offers (1) the flexibility to incorporate available information in known variables and (2) the convenience to automate the forecasting of unknown variables. Applying our method to forecast GDP subcomponents in an advanced and emerging market country, we show that it improves upon alternative forecasting techniques.