petar sorić, ivana lolić: economic uncertainty and its impact on the croatian economy public sector economics 41 (4) 443-477 (2017)

In the last five decades the European Economic Sentiment Indicator (ESI) has positioned itself as a high-quality leading indicator of overall economic activity. Relying on data from five distinct business and consumer survey sectors (industry, retail trade, services, construction and the consumer sector), ESI is conceptualized as a weighted average of the chosen 15 response balances. However, the official methodology of calculating ESI is quite flawed because of the arbitrarily chosen balance response weights. This paper proposes two alternative methods for obtaining novel weights aimed at enhancing ESI's forecasting power. Specifically, the weights are determined by minimizing the root mean square error in simple GDP forecasting regression equations; and by maximizing the correlation coefficient between ESI and GDP growth for various lead lengths (up to 12 months). Both employed methods seem to considerably increase ESI's forecasting accuracy in 26 individual European Union countries. The obtained results are quite robust across specifications. Key wordsBusiness and Consumer Surveys, Economic Sentiment Indicator, Nonlinear Optimization with Constraints, Leading Indicator JEL classification C53, C61, E32, E37 AbstractIn the last five decades the European Economic Sentiment Indicator (ESI) has positioned itself as a high-quality leading indicator of overall economic activity. Relying on data from five distinct business and consumer survey sectors (industry, retail trade, services, construction and the consumer sector), ESI is conceptualized as a weighted average of the chosen 15 response balances. However, the official methodology of calculating ESI is quite flawed because of the arbitrarily chosen balance response weights. This paper proposes two alternative methods for obtaining novel weights aimed at enhancing ESI's forecasting power. Specifically, the weights are determined by minimizing the root mean square error in simple GDP forecasting regression equations; and by maximizing the correlation coefficient between ESI and GDP growth for various lead lengths (up to 12 months). Both employed methods seem to considerably increase ESI's forecasting accuracy in 26 individual European Union countries. The obtained results are quite robust across specifications.

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.

This paper assesses the euro area inflation expectations by examining five different survey‐based expectations indicators. The Survey of Professional Forecasters outperforms all other expectations indicators in terms of forecasting accuracy. We test the unbiasedness and efficiency of these indicators by viewing the Rational Expectations Hypothesis (REH) from a time‐varying perspective in a state space framework. Our model shows that the deviations from expectations' unbiasedness and efficiency are the most pronounced in the global financial crisis. Additionally, we offer evidence that the adaptive expectations and regressive expectations models are considerably more in line with actual data than REH.

This paper is a follow-up on the Economic Policy Uncertainty (EPU) index, developed in 2011 by Baker, Bloom, and Davis. The principal idea of the EPU index is to quantify the level of uncertainty in an economic system, based on three separate pillars: news media, number of federal tax code provisions expiring in the following years, and disagreement amongst professional forecasters on future tendencies of relevant macroeconomic variables. Although the original EPU index was designed and published for the US economy, it had instantly caught the attention of numerous academics and was rapidly introduced in 15 countries worldwide. Extensive academic debate has been triggered on the importance of economic uncertainty relating to the intensity and persistence of the recent crisis. Despite the intensive (mostly politically-motivated) debate, formal scientific confirmation of causality running from the EPU index to economic activity has not followed. Moreover, empirical literature has completely failed to conduct formal econometric testing of the Granger causality between the two mentioned phenomena. This paper provides an estimation of the Toda-Yamamoto causality test between the EPU index and economic activity in the USA and several European countries. The results do not provide a general conclusion: causality seems to run in both directions only for the USA, while only in one direction for France and Germany. Having taken into account the Great Recession of 2008, the main result does not change, therefore casting doubt on the index methodology and overall media bias.