Economic determinants of employment sentiment: A socio- demographic analysis for the euro area (original) (raw)

Unemployment expectations: A socio-demographic analysis of the effect of news

Labour Economics, 2019

In this study, we evaluate the effect of news on consumer unemployment expectations for sixteen socio-demographic groups. To this end, we construct an unemployment sentiment indicator and extract news about several economic variables. By means of genetic programming we estimate symbolic regressions that link unemployment rates in the Euro Area to qualitative expectations about a wide range of economic variables. We then use the evolved expressions to compute unemployment expectations for each consumer group. We first assess the out-of-sample forecast accuracy of the evolved indicators, obtaining better forecasts for the leading unemployment sentiment indicator than for the coincident one. Results are similar across the different socio-demographic groups. The best forecast results are obtained for respondents between 30 and 49 years. The group where we observe the bigger differences among categories is the occupation, where the lowest forecast errors are obtained for the unemployed respondents. Next, we link news about inflation, industrial production, and stock markets to unemployment expectations. With this aim we match positive and negative news with consumers' unemployment sentiment using a distributed lag regression model for each news item. We find asymmetries in the responses of consumers' unemployment expectations to economic news: they tend to be stronger in the case of negative news, especially in the case of inflation.

Forecasting the unemployment rate using the degree of agreement in consumers' expectations

13th ifo Dresden Workshop on Macroeconomics and Business Cycle Research, 2019

This study aims to refine unemployment forecasts by incorporating the degree of agreement 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 (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 a consensus-based indicator as a predictor. We obtain an improvement in forecast accuracy in all countries except Austria. 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.

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

Journal for Labour Market Research, 2019

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.

Economic forecasting with evolved confidence indicators

Economic Modelling, 2020

We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms' production expectations and consumers' expectations to spend on home improvements are the most frequently selected variables-both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides. JEL Classification: C51; C55; C63; C83; C93

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

Applied Sciences, 2022

We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a nowcasting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes.

Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming

IREA Working Paper 2017/11, 2017

In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive the optimal combination of expectations that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ “perception about the overall economy compared to last year” is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.

Empirical modelling of survey-based expectations for the design of economic indicators in five European regions

Empirica - Journal of European Economics, 2019

In this study we use agents' expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents' " perception about the overall economy compared to last year " is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.

Empirical modelling of survey-based expectations for the design of economic indicators

CIRET/KOF/WIFO Workshop on Economic Tendency Surveys and Financing Conditions, 2017

In this study we use agents' expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive the optimal combination of expectations that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents' " perception about the overall economy compared to last year " is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth.

Confidence and unemployment in the European Union: a lesson from the 2004 enlargement

Notas Económicas

We use data for the monthly unemployment rate and consumer confidence indicator in the European Union to study whether there is a relationship between unemployment and confidence. Our estimation method is based on a fuzzy logic methodology with a Gaussian membership function. We find a stronger relationship between unemployment and confidence than it is apparent. We show in an ex-post prediction study that this relationship did not change, in a significant way, after the 2004 enlargement of the European Union. This fact constitutes a lesson for the post-2007 enlargement period. The link between unemployment and confidence has important policy implications given its relevance, in particular for the (latest call for a new start of the) Lisbon Strategy.