Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations

Abstract

This study aims to refine unemployment forecasts by incorporating the degree of consensus in consumers' expectations. With this objective, we first model the unemployment rate in eight European countries using the step-wise algorithm proposed by Hyndman and Khandakar (J Stat Softw 27(3):1-22, 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. Finally, we replicate the forecasting experiment including as predictors both an indicator of unemployment, based on the degree of agreement in consumer unemployment expectations, and a measure of disagreement based on the dispersion of expectations. In both cases, we obtain an improvement in forecast accuracy in most countries. These results reveal that the degree of agreement in consumers' expectations contains useful information to predict unemployment rates, especially for the detection of turning points. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

The Impact of the COVID-19 Pandemic on Gaseous and Solid Air Pollutants Concentrations and Emissions in the EU, with Particular Emphasis on Poland

“…They consist of AR processes, i.e., autoregressive and MA (moving average) moving average processes. In the case of the AR (autoregression) process, the current series value is the sum of the linear combination of the previous series observations as well as the random disturbance [16,17].…”

Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023

“…The coronavirus crisis in 2020 has a similar impact, affecting the unemployment rate worldwide. The pandemic has severely disrupted the economic activity through various supply and demand channels, leading to large and protracted increases in unemployment and declines in inflation [13].…”

Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam

“…The ARIMA model was used by Claveria [30] to examine unemployment rates in eight European Union countries between 2007 and 2017. Moreover, Ahmad et al [31] used a hybrid ARIMA-SVM-ARNN prediction methodology to forecast unemployment rates for six selected European nations, notably France, Spain, Belgium, Turkey, Italy, and Germany.…”