Three dimensions of uncertainty: economic, inflation and employment

What is it about?

The main aim of the research is to point at potential differences in the perception of uncertainty across agents. With this aim we evaluate the dynamic response of different macroeconomic variables to shocks in agents’ perception of three dimensions of uncertainty (economic, inflation and employment). First, we apply a geometric indicator to compute the proportion of disagreement in business and consumer expectations of eight European countries and the Euro Area. Next, we use a bivariate vector autoregressive framework to estimate the impulse response functions to innovations in disagreement. We also run Granger causality tests to evaluate whether including past values of uncertainty measures improves predictions of their respective reference series based only on their own past values. Finally, we perform a forecasting exercise to assess the predictive performance of the disagreement indicators for different time horizons.

Featured Image

Photo by Michael Dziedzic on Unsplash

Photo by Michael Dziedzic on Unsplash

Why is it important?

While we find an adverse reaction in unemployment rates to shocks in discrepancy, results differ markedly between disagreement in business and in consumer surveys with regard to economic growth and inflation: shocks to manufacturing production discrepancy lead to a decrease in economic activity, as opposed to shocks to consumer economic discrepancy; and the opposite in the case of a shock in the perception of price uncertainty. The Granger causality tests show evidence that disagreement in business surveys Granger-causes macroeconomic aggregates in most cases, especially for economic growth and inflation, while the opposite happens for disagreement in consumer surveys. We additionally perform a forecasting exercise to assess the predictive performance of the disagreement indicators for different time horizons, obtaining more accurate out-of-sample recursive forecasts of economic growth with the indicators of discrepancy of manufacturing firms and, of unemployment with the indicators of consumer discrepancy. When compared to recursive autoregressive predictions used as a benchmark, we find that vector autoregressions with industry discrepancy tend to outperform the benchmark in more cases that models with indicators of consumer discrepancy.

Perspectives