Unemployment rate forecasting using consumer expectations

What is it about?

This study aims to refine unemployment forecasts by incorporating the degree of consensus in consumers’ unemployment expectations. With this objective, we first model the unemployment rate in eight European countries using the step-wise algorithm proposed by Hyndman and Khandakar (2008). The selected optimal autoregressive integrated moving average (ARIMA) models are then used to generate out-of-sample recursive forecasts of the unemployment rates, which are used as benchmark. We then replicate the forecasting experiment using ARIMAX models that include as predictors: a) An indicator of unemployment based on the degree of agreement in consumer unemployment expectations (Claveria, 2019) b) And a measure of disagreement based on the dispersion of expectations (Bachmann et al., 2013). Finally, we compute the Diebold–Mariano statistic of predictive accuracytest to test whether the reduction in forecast accuracy of the two augmented models with regards to the benchmark is statistically significant.

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Photo by Markus Spiske on Unsplash

Photo by Markus Spiske on Unsplash

Why is it important?

The study analyses whether the inclusion of the information coming from the degree of agreement among consumers’ expectations helps to refine forecasts of the unemployment rate. We find that both the consensus-based unemployment indicator and the measure of disagreement lead to an improvement in forecast accuracy in most countries. These results allow us to conclude that the level of agreement/disagreement in consumer unemployment expectations contain useful information to forecast unemployment rates, especially for the detection of turning points detected by agents in advance.

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