Experts, firms, consumers or even hard data? Forecasting employment in Germany

Lehmann, Robert; Wohlrabe, Klaus

doi:10.1080/13504851.2016.1184219

Cited by 9 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results are in line with those of Abberger (2007) and Lehmann and Wohlrabe (2017), who find that consumers' employment and unemployment expectations respectively exhibit a high forecasting accuracy in Germany. Martinsen et al (2014) and Österholm (2010) also find evidence that unemployment expectations help to improve unemployment forecasting in Norway, Sweden and Finland.…”

Section: Resultssupporting

confidence: 92%

“…This study aims to fill this gap by incorporating information coming from qualitative surveys. In recent years, evidence has been found that consumer expectations contain valuable information in order to improve predictions of employment (Abberger 2007;Claveria et al 2007;Hutter and Weber 2015;Lehmann and Wohlrabe 2017). These studies focus on a single country, more specifically in Germany, and compare the role of different employment indicators.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Clavería

2019

J Labour Market Res

View full text Add to dashboard Cite

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.

show abstract

Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

Clavería

2019

J Labour Market Res

View full text Add to dashboard Cite

show abstract

“…Similarly, Lehmann and Wohlrabe (2017) found that consumers' unemployment expectations were a good indicator of employment growth in Germany.…”

Section: Introductionmentioning

confidence: 94%

Evolutionary Computation for Macroeconomic Forecasting

2017

View full text Add to dashboard Cite

The main objective of this study is twofold. First, we propose an empirical modelling approach based on genetic programming to forecast economic growth by means of survey data on expectations. We use evolutionary algorithms to estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. The set of empirically-generated proxies of economic growth are used as building blocks to forecast the evolution of GDP. Second, we use these estimates of GDP to assess the impact of the 2008 financial crisis on the accuracy of agents' expectations about the evolution of the economic activity in four Scandinavian economies. While we find an improvement in the capacity of agents' to anticipate economic growth after the crisis, predictive accuracy worsens in relation to the period prior to the crisis. The most accurate GDP forecasts are obtained for Sweden.

show abstract

“…Accordingly, survey results provide important information about agents' economic expectations, allowing comparisons among different countries' business cycles. On the one hand, sectoral results of the surveys have been often used as partial indicators for the construction of more general aggregate economic indicators and for the estimation of macro magnitudes through their introduction in econometric models (Abberger, 2007;Bruestle and Crain, 2015;Graff, 2010;Hanson et al, 2005;Lehmann and Wohlrabe, 2017;Wilms et al, 2016). On the other hand, survey-based expectations have also been introduced into behavioural equations postulated by economic theory such as the Phillips curve and to evaluate the formation of expectations, as they provide a direct measure of expectations to test the rationality of agents (Altug and Çakmakli, 2016;Bovi, 2013;Jean-Baptiste, 2012;Lee, 1994;Miah et al, 2016;Paloviita, 2006).…”

Section: Introductionmentioning

confidence: 99%

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

2018

View full text Add to dashboard Cite

This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”.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,\ud 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\ud 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.This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”.Peer ReviewedPostprint (author's final draft

show abstract

Experts, firms, consumers or even hard data? Forecasting employment in Germany

Cited by 9 publications

References 7 publications

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

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

Evolutionary Computation for Macroeconomic Forecasting

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

Contact Info

Product

Resources

About