What does Google say about credit developments in Brazil?

Abstract

Abstract In this paper multivariate State Space (SS) models are used to evaluate and forecast household loans in Brazil, taking into account two Google search terms in order to identify credit demand: financiamento (type of loan used to finance goods) and empréstimo (a more general type of loan). Our framework is coupled with nonlinear features, such as Markov-switching and threshold point. We explore these nonlinearities to build identification strategies to disentangle the supply and demand forces which drive the credit market to equilibrium over time. We also show that the underlying nonlinearities significantly improves the performance of SS models on forecasting the household loans in Brazil, particularly in short-term horizons.