The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach

TL;DR

This paper introduces a high-dimensional Granger Causality approach using bootstrap tests and Adaptive Lasso to identify industry-specific sentiment indicators that enhance macroeconomic forecasting accuracy.

Contribution

It develops a novel bootstrap Granger Causality test with Adaptive Lasso for high-dimensional sentiment data, improving prediction by selecting key industries.

Findings

Most predictive industries vary across sectors.

Using selected industries improves forecast accuracy.

The proposed method outperforms standard Wald tests.

Abstract

We study the predictive power of industry-specific economic sentiment indicators for future macro-economic developments. In addition to the sentiment of firms towards their own business situation, we study their sentiment with respect to the banking sector - their main credit providers. The use of industry-specific sentiment indicators results in a high-dimensional forecasting problem. To identify the most predictive industries, we present a bootstrap Granger Causality test based on the Adaptive Lasso. This test is more powerful than the standard Wald test in such high-dimensional settings. Forecast accuracy is improved by using only the most predictive industries rather than all industries.