How is perceived inflation related to actual price changes in the European Union?

Antonides, Gerrit

doi:10.1016/j.joep.2008.04.002

Cited by 33 publications

(25 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…3 The empirical strategy followed in this paper allows different weights for each of the 15 analyzed BCS questions. Therefore the obtained results consist of a large-dimension dataset, summarized in Tables 1, 2 and 3 by summing up the calculated 15 weights in a sectoral fashion (e.g.…”

Section: Estimation Resultsmentioning

confidence: 99%

“…Their first role is to serve as a data source for quantifying the prevailing business climate in particular branches of the national economy (Gayer, 2005) or to get estimates for non-measurable factors such as expectations or perceptions (see e.g. Antonides, 2008). On the other hand, BCS are also utilized to construct composite leading indicators.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

European economic sentiment indicator: an empirical reappraisal

2015

View full text Add to dashboard Cite

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.

show abstract

Section: Estimation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

European economic sentiment indicator: an empirical reappraisal

2015

View full text Add to dashboard Cite

show abstract

“…These findings suggest that an "out-of-pocket" subsample of CPI basket could provide an index better suited to catch perceived inflation rate of growth (Lyziak, 2009;ECB, 2003; contrasting findings are reported in Antonides, 2008).…”

mentioning

confidence: 85%

Consumer Price Indexes: An Analysis of Heterogeneity Across Sub-Populations

Santioni

Carbonaro

Carlucci

2009

Contributions to Statistics

View full text Add to dashboard Cite

The availability of separate indexes for target population groups could play a major role in assessing inflation impacts on people at risk of poverty, thus providing an useful tool for active policies aimed to the fight against poverty and social exclusion. We developed sub-indexes according to both economic and socio-demographic structural characteristics. Our results show that on average poorer households have experienced higher inflation than richer ones

show abstract

“…1 The necessity of exploring consumers' inflation sentiment stems from both theoretical and empirical grounds. Microeconomic theory postulates that individual demand is determined by the consumer's budget and market prices, taking as given that consumers indeed perceive price changes correctly (Antonides, 2008). Nevertheless, empirical studies often contradict this assumption, revealing a significant gap between real and perceived inflation (as "seen" by the consumers.…”

Section: Introductionmentioning

confidence: 99%

“…2 Perhaps the most striking example is the disproportionally high perceived inflation in the Euro area (EA) due to the Euro cash changeover in January 2002 (Brachinger, 2008). Such discrepancy has raised the question of HICP non-credibility, regarding the fact that it is evidently not accepted by the public as a realistically founded measure of aggregate price changes (Antonides, 2008).…”

Section: Introductionmentioning

confidence: 99%