Niet beschikbaar in het Nederlands
Heinz Christian Dieden
- 19 April 2017
- OCCASIONAL PAPER SERIES - No. 186Details
- Abstract
- This report updates and extends earlier assessments of quantitative inflation perceptions and expectations of consumers in the euro area and the EU using an anonymised micro data set collected by the European Commission in the context of the Harmonised EU Programme of Business and Consumer Surveys. Confirming earlier findings, consumers' quantitative estimates of inflation are found to be higher than actual HICP (Harmonised Index of Consumer Prices) inflation over the entire sample period (2004-2015). The analysis shows that European consumers hold different opinions of inflation depending on their income, age, education and gender. Although many of the features highlighted for the EU and the euro area aggregates are valid across individual Member States, differences exist also at the country level. Despite the higher inflation estimates, there is a high level of co-movement between measured and estimated (perceived/expected) inflation. Even respondents providing estimates largely above actual HICP inflation, demonstrate understanding of the relative level of inflation during both high and low inflation periods. Based on these economically plausible results, the report concludes that further work should be devoted to defining concrete aggregate indicators of consumers' quantitative inflation perceptions and expectations on the basis of the dataset used in this study. Moreover, it outlines a number of future research topics that can be addressed by exploiting the enormous potential of the data set.
- JEL Code
- D8 : Microeconomics→Information, Knowledge, and Uncertainty
D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
- 9 December 2014
- STATISTICS PAPER SERIES - No. 6Details
- Abstract
- This paper models industrial new orders across European Union (EU) Member States for various breakdowns. A common modelling framework exploits both soft data (business opinion surveys) and hard data (industrial turnover). The estimates show for about 200 cases that the model determinants significantly help in explaining monthly growth rates for new orders. An alternative estimation method, different model specifications and out-of-sample and real-time forecasting all show that the model results are robust. We present real-time outcomes of a European Central Bank (ECB) indicator on industrial new orders at an aggregated euro area level. This indicator is largely based on national new orders data and on estimates yielded by the model for those countries that no longer report new orders at the national level. Finally, we demonstrate the leading nature of the ECB indicator on euro area new orders in relation to industrial production.
- JEL Code
- C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
Annexes- 9 December 2014
- ANNEX
- 26 June 2013
- OCCASIONAL PAPER SERIES - No. 149Details
- Abstract
- Following the discontinuation of the official statistics on industrial new orders by Eurostat in mid-2012, this paper introduces the ECB indicator on euro area industrial new orders, which aims to fill the new statistical gaps for euro area total new orders as well as for various breakdowns. Despite the discontinuation of the data collection at European level, a large number of euro area countries are expected to continue with the data collection nationally. For those countries which have discontinued the collection of national data, model-estimates are used in calculating the ECB indicator on euro area industrial new orders. New orders are modelled across EU countries using "soft" data (business opinion surveys) as well as "hard" data (industrial turnover) and applying a common modelling framework. The model determinants significantly explain the monthly growth rates in new orders across approximately 200 estimated equations. Various tests show that the estimates are robust. This paper demonstrates that, besides the leading information content of industrial new orders for euro area industrial production, the monitoring of the ECB indicator on new orders is useful for cross-checking developments in industrial production in real time.
- JEL Code
- C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
- 30 October 2007
- OCCASIONAL PAPER SERIES - No. 74Details
- Abstract
- The preparations for the introduction of the euro in 1999 involved the need for a new set of statistics for the euro area. Since then, significant progress has been made with regard to the coverage, timeliness and accuracy of these statistics. The reliability of the first releases - i.e. their stability in the process of later revisions - is an important quality-related feature. New data releases for the euro area have generally shown a very small or no bias, i.e. data revisions have been very modest and comparable with those of, for example, the United States or Japan. Despite the relatively small size of revisions, however, their combination with the low growth of the euro area economy may have drawn attention to such revisions of economic data for the euro area. This paper quantifies the revisions to selected key indicators in the period from the start of Monetary Union in 1999 to July 2007 and compares them with the corresponding mediumterm averages (1999-2006). The analysis covers the euro area, its six largest member countries, the United Kingdom, the United States and Japan. For this purpose, available time series for the various periods involved are used, series that record all revisions to published statistical data releases. The analysis is carried out separately for GDP growth and its expenditure components, for employment, unemployment rates, compensation per employee, labour cost indicators, industrial production, retail trade turnover and consumer prices. Overall, the evidence presented in this paper suggests that euro area data releases have generally shown a very small or no bias and have been more stable than those for individual euro area countries. Furthermore, recent euro area data how levels of revisions similar to those of the past, or levels of revisions that stabilised after the implementation of harmonised statistical concepts had largely been completed.
- JEL Code
- E01 : Macroeconomics and Monetary Economics→General→Measurement and Data on National Income and Product Accounts and Wealth, Environmental Accounts
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
E24 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Employment, Unemployment, Wages, Intergenerational Income Distribution, Aggregate Human Capital
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit