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Per Nymand-Andersen

9 February 2018
STATISTICS PAPER SERIES - No. 27
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Abstract
The European Central Bank (ECB), as part of its forward-looking strategy, needs high-quality financial market statistical indicators as a means to facilitate evidence-based and sound decision-making. Such indicators include timely market intelligence and information to gauge investors’ expectations and reaction functions with regard to policy decisions. The main use of yield curve estimations from an ECB monetary policy perspective is to obtain a proper empirical representation of the term structure of interest rates for the euro area which can be interpreted in terms of market expectations of monetary policy, economic activity and inflation expectations over short-, medium- and long-term horizons. Yield curves therefore play a pivotal role in the monitoring of the term structure of interest rates in the euro area. In this context, the purpose of this paper is twofold: firstly, to pave the way for a conceptual framework with recommendations for selecting a high-quality government bond sample for yield curve estimations, where changes mainly reflect changes in the yields-to-maturity rather than in other attributes of the underlying debt securities and models; and secondly, to supplement the comprehensive – mainly theoretical – literature with the more empirical side of term structure estimations by applying statistical tests to select and produce representative yield curves for policymakers and market-makers.
JEL Code
G1 : Financial Economics→General Financial Markets
E4 : Macroeconomics and Monetary Economics→Money and Interest Rates
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
Annexes
29 November 2018
STATISTICS PAPER SERIES - No. 30
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Abstract
Big data” is becoming an increasingly important aspect of our daily lives as the digital sources of information and intelligence that it encompasses become more structured and more publicly available. These sources may enable the generation of new datasets providing high-frequency and timely insights into unconscious digital behaviour and the consequent actions of economic agents, which may, in turn, assist in the generation of early indicators of economic and financial trends and activities. This paper examines the usefulness of Google search data in nowcasting euro area car sales, as a leading macroeconomic indicator, and considers the quality requirements for using these new data sources as a toolkit for sound decision and policy making. The paper finds that, while Google data may have predictive capabilities for nowcasting euro area car sales, further quality improvements in the data source are needed in order to move beyond experimental statistics. If these quality requirements can be met, the resulting advances in theory and knowledge around interpreting big data can be expected to significantly re-shape how we think about and explain both behaviour and complex socio-economic phenomena.
JEL Code
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
E71 : Macroeconomics and Monetary Economics