Statistics papers

Snapshot of statistics

The Statistics Paper Series (SPS) is a channel for statisticians, economists and other professionals to publish innovative work undertaken in the area of statistics and related methodologies that is of interest to central banks.

The SPS covers both methodological and conceptual issues related to central banking statistics, new statistical topics and techniques. The Statistics Paper Series is published to stimulate discussion and to contribute to informing the international statistics community about work on improving methodological standards, with the goal of ultimately attaining more comparable and higher-quality statistics from all economic areas and countries.

Availability: ECB Statistics Paper Series are available online only.

No. 25
9 November 2017
Supervisory and statistical granular data modelling at the Croatian National Bank

Abstract

JEL Classification

E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies

C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access

G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation

Abstract

As the European Reporting Framework (ERF): Key facts and information 1 report has recognised, some countries have already implemented integrated “statistical” and supervisory reporting requirements at a granular level. Croatia is one of these countries. Moreover, Croatia has been able to produce a local “AnaCredit” system on a loanby-loan basis for legal entities and non-residents (see the ECB MFI list 2 or Annex 4 of the Banks’ Integrated Reporting Dictionary of the Croatian National Bank 3), and at an aggregate level for households, other non-residents and small businesses, using the same underlying data as for statistical and prudential reporting. A Croatian granular data system at a counterparty level for legal entities/nonresidents on the list and at an aggregate level for households, other non-residents and small businesses was developed in 2007/2008 following a series of workshops held among colleagues from Supervision, Statistics and IT at the Croatian National Bank (CNB) and credit institutions. One of the most important deliverables of the project was the CNB Banks’ Integrated Reporting Dictionary, a document in which all attributes collected by the system are listed, organised into categories, described and explained, and where examples and the methodologies used are provided. In Croatia, the CNB Banks’ Integrated Reporting Dictionary is mandatory for all credit institutions, and it has been enforced on the financial market following a decision of the Croatian National Bank Governor. This article discusses granular data modelling for the purpose of statistical, supervisory and European Central Bank reporting and analysis.

No. 24
7 August 2017
Spontaneous recognition: an unnecessary control on data access?

Abstract

JEL Classification

C19 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Other

C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access

Abstract

Social scientists increasingly expect to have access to detailed data for research purposes. As the level of detail increases, data providers worry about “spontaneous recognition”, the likelihood that a microdata user believes that he or she has accidentally identified one of the data subjects in the dataset, and may share that information. This concern, particularly in respect of microdata on businesses, leads to excessive restrictions on data use. We argue that spontaneous recognition presents no meaningful risk to confidentiality. The standard models of deliberate attack on the data cover re-identification risk to an acceptable standard under most current legislation. If spontaneous recognition did occur, the user is very unlikely to be in breach of any law or condition of access. Any breach would only occur as a result of further actions by the user to confirm or assert identity, and these should be seen as a managerial problem. Nevertheless, a consideration of spontaneous recognition does highlight some of the implicit assumptions made in data access decisions. It also shows the importance of the data provider’s culture and attitude. For data providers focused on users, spontaneous recognition is a useful check on whether all relevant risks have been addressed. For data providers primarily concerned with the risks of release, it provides a way to place insurmountable barriers in front of those wanting to increase data access. We present a case study on a business dataset to show how rejecting the concept of spontaneous recognition led to a substantial change in research outcomes.

No. 23
23 May 2017
Estimating non-financial assets by institutional sector for the euro area

Abstract

JEL Classification

C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models

C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access

E02 : Macroeconomics and Monetary Economics→General→Institutions and the Macroeconomy

E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity

Abstract

Official euro area-wide statistics on the capital stock and its breakdowns by asset type and sector are not yet available, but would be very useful for economic and financial stability analysis. This paper proposes a constrained optimisation model with the help of which a full cross-sector classification of the capital stock by non-financial asset type can be estimated. The model is applied for the estimation of the capital stock by institutional sector, including households’ non-financial asset types and housing wealth, both for the euro area as a whole and for euro area countries currently not estimating and/or publishing such data.

No. 22
23 May 2017
Estimating consumption in the HFCS: Experimental results on the first wave of the HFCS

Abstract

JEL Classification

D120 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis

D140 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance

D310 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions

Abstract

In this paper, we estimate consumption in the first wave of the Eurosystem Household Finance and Consumption Survey for a subset of countries that account for around 85% of the aggregate final consumption expenditure of households in the euro area. For this purpose we use the methodology described by Browning et al. (2003), taking advantage of the few questions on consumption asked to households participating in the survey and information on consumption collected in the Household Budget Surveys. Using also the framework developed for statistical matching, we give assessments of the uncertainty related to this kind of estimation. We find that the quality of estimation varies greatly across countries and in general is sensitive to the Conditional Independence Assumption implicitly made through this exercise. At any rate, estimations of consumption (provided throughout this paper) should be used with caution, bearing in mind that they rely on strong assumptions.

No. 21
22 May 2017
Decomposition techniques for financial ratios of European non-financial listed groups

Abstract

JEL Classification

C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation

L22 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Organization and Market Structure

L25 : Industrial Organization→Firm Objectives, Organization, and Behavior→Firm Performance: Size, Diversification, and Scope

M4 : Business Administration and Business Economics, Marketing, Accounting→Accounting and Auditing

Abstract

Analysis of consolidated accounting data of European listed groups shows significant differences in some key ratios between countries However, the figures do not reveal whether these differences result from a distinct composition of the countries’ populations in terms of branches of activity (structural effect) or from intrinsic disparities in the behaviour of groups from various countries. This paper will address this issue using ratio decomposition techniques. A comparative overview of decomposition methodologies available in the literature will be provided, as well as an in-depth description of the methodology used. This will be applied to decompose the difference in the financial debt ratio, the equity ratio and the EBIT margin across countries for one specific year and to consider any dissimilarities in financial debt ratios over a limited period of time. The study will be based on the data available in the ERICA dataset from the European Committee of Central Balance Sheet Data Offices (ECCBSO), which includes accounting data of listed groups from Austria, Belgium, France, Germany, Greece, Italy, Portugal and Spain. The aggregate ratios of each country will be compared against a benchmark composed of the aggregate ratios for the eight countries together.

No. 20
3 May 2017
The journey from micro supervisory data to aggregate macroprudential statistics

Abstract

JEL Classification

C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access

G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages

Abstract

The Consolidated Banking Data CBD) are a key component of the ECB/ESCB statistical toolbox for financial stability analysis. This dataset, which contains all the relevant dimensions of systemic risk stemming from and affecting national banking systems, is compiled from firm-level supervisory returns. With the entry into force of the new set of European Banking Authority (EBA) Implementing Technical Standards on Supervisory Reporting in 2014, the whole CBD statistical framework had to be reshaped. In August 2015 the first data for the revised CBD were released. This paper deals with the main issues in the challenging endeavour of transposing firmlevel supervisory returns, often based on different accounting systems, into comprehensive aggregate statistics, while ensuring as far as possible continuity in the time series for indicators and aggregates calculated from different successive data models. At the same time, the new CBD has substantially enlarged the quantity and increased the quality of data, available to the users. This paper provides a description of the database, together with some examples drawn from it.

No. 19
23 January 2017
Leverage interactions: a national accounts approach

Abstract

JEL Classification

E01 : Macroeconomics and Monetary Economics→General→Measurement and Data on National Income and Product Accounts and Wealth, Environmental Accounts

E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy

H3 : Public Economics→Fiscal Policies and Behavior of Economic Agents

H6 : Public Economics→National Budget, Deficit, and Debt

Abstract

The policy focus on excessive leverage in the euro area has raised interest in developing comprehensive analytical approaches to better understand the interrelationship between leverage and deleveraging processes across economic agents. In particular, the interplay between government debt and private leverage is attracting increasing attention in the current context of simultaneous deleveraging adjustments. However, analyses of the subject are generally partial in that they fail to take into account feedback effects on balance sheet positions across economic agents. This paper attempts to clarify these cross-agent interlinkages by examining concepts, relationships and restrictions taken from the national accounts framework. Hence, the paper presents a mechanism that captures how increased leverage in certain agents contributes, ceteris paribus, to a reduction in leverage in the rest of the economy. The novelty of the underlying framework for leverage behaviour is that it takes the financial assets held by agents into consideration.

No. 18
23 December 2016
The Household Finance and Consumption Survey: results from the second wave

Abstract

JEL Classification

D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance

D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions

E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth

No. 17
23 December 2016
The Household Finance and Consumption Survey: methodological report for the second wave

Abstract

JEL Classification

C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods

D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions

No. 16
27 July 2016
The statistical classification of cash pooling activities

Abstract

JEL Classification

G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages

G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill

E51 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Money Supply, Credit, Money Multipliers

E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects

M41 : Business Administration and Business Economics, Marketing, Accounting→Accounting and Auditing→Accounting

Abstract

Cash pooling is a bank service that allows corporates to externalise the intra-group cash management, and thus manage their global liquidity effectively with lower costs. Although there is little quantitative information on the significance of the phenomenon, cash pooling appears to have become increasingly popular after the onset of the financial crisis when, in an environment characterised by limited access to capital markets, reduced bank lending, low returns and higher risks on banks' deposits, corporate groups started to maximise their use of internal sources of financing. In particular, cash pooling is currently very relevant in Western and Northern European countries, and is mainly offered in the United Kingdom, France and the Netherlands. This paper first analyses cash pooling agreements with a focus on the aspects that are relevant from a statistical viewpoint. It then addresses their statistical recording in compliance with ESA 2010 and, specifically, the methodological framework of Monetary Financial Institutions (MFI) balance sheet item statistics. It is proposed that positions related to cash pooling shall be recorded on a gross basis vis-

No. 15
22 July 2016
Unit non-response in household wealth surveys

Abstract

JEL Classification

C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods

Abstract

The Household Finance and Consumption Survey (HFCS) is a recent initiative from the Eurosystem to collect comparable micro-data on household wealth and indebtedness in the euro area countries. The Household Finance and Consumption Network (HFCN), which comprises the European Central Bank (ECB), national central banks (NCBs), and national statistical institutes (NSIs), is in charge of the development and implementation of the HFCS. The first round of the survey was successfully conducted between 2008 and 2011, and the results were published in April 2013. The second round is now under way and will cover all the euro area countries. This paper is a joint effort by several members of the HFCN to further investigate the issue of unit non-response in the HFCS, better describe and understand its patterns, measure its effects on the overall quality of the survey and, ultimately, propose strategies to mitigate them. The paper is divided into sections, the first section being the introduction. The second section draws up a list of the main possible sources of auxiliary information that can be relied on in order to analyse non-response patterns in the HFCS. It also presents summary indicators that can be used to quantify unit non-response. In the third section, based on the experience from the first wave of the HFCS, the report elaborates on good survey practices (e.g. interviewer training and compensation, use of incentives, persuasive contact strategies, etc.) to prevent unit non-response from occurring. The fourth section compares several reweighting strategies for coping with unit non-response a posteriori, in particular simple and generalised calibration methods. These methods are assessed with respect to their impact on the main HFCS-based estimates. Finally, based on the outcome of this empirical analysis, recommendations are made with regard to post-survey weighting adjustment in the HFCS.

No. 14
11 July 2016
Estimating gross value added volumes and prices by institutional sector

Abstract

JEL Classification

C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models

C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access

E01 : Macroeconomics and Monetary Economics→General→Measurement and Data on National Income and Product Accounts and Wealth, Environmental Accounts

E30 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→General

Abstract

Integrated quarterly sector accounts (QSA) provide an analytical tool to understand the generation, allocation and use of income for all institutional sectors in the economy. They also provide a tool to analyse production from a sectoral point of view instead of an industry point of view. However, since QSA are published in current prices only, sectoral volume and price measures are lacking as an important toolkit for economic analysis and forecasting, notably in the case of gross value added. This paper introduces a methodology to estimate sectoral price and volume measures for euro area value added at a quarterly frequency and provides a comparison of alternative estimation methods. It presents a benchmark method which yields robust estimates of sectoral volumes and prices in the euro area.

No. 13
8 April 2016
Modelling metadata in central banks

Abstract

JEL Classification

E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies

C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access

G18 : Financial Economics→General Financial Markets→Government Policy and Regulation

Abstract

This article discusses a small scale pilot to harmonise three Bank of England statistical and regulatory data forms. The primary purpose of the pilot was to assess opportunities for improved operational efficiency in regulatory reporting. The broader purpose was to demonstrate how common data standards can be created from heterogeneous data sets. In the course of discussing the pilot, the article explains the history of how data has been collected at the Bank of England; how that process is changing in light of the Bank

No. 12
18 December 2015
Measuring non-response bias in a cross-country enterprise survey

Abstract

JEL Classification

C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access

C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods

D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis

Abstract

Non-response is a common issue affecting the vast majority of surveys, and low non-response is usually associated with higher quality. However, efforts to convince unwilling respondents to participate in a survey might not necessarily result in a better picture of the target population. It can lead to higher, rather than lower, non-response bias, for example if incentives are effective only for particular groups, e.g. in a business survey, if the incentives tend to attract mainly larger companies or enterprises encountering financial difficulties. We investigate the impact of non-response in the European Commission and European Central Bank Survey on the Access to Finance of Enterprises (SAFE), which collects evidence on the financing conditions faced by European small and medium-sized enterprises compared with those of large firms. This survey, which has been conducted by telephone biannually since 2009 by the European Central Bank and the European Commission, provides a valuable means of searching for this kind of bias, given the high heterogeneity of response propensities across countries. The study relies on so-called

No. 11
10 September 2015
The Bank for the Accounts of Companies Harmonized (BACH) database

Abstract

JEL Classification

C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access

M41 : Business Administration and Business Economics, Marketing, Accounting→Accounting and Auditing→Accounting

Abstract

The Bank for the Accounts of Companies Harmonized (BACH) is a free-of-charge database containing the aggregated accounting data of non-financial incorporated enterprises for, so far, 11 European countries. While the individual accounts feeding the database were originally prepared in line with national accounting standards consistent with European Accounting Directives, they have been harmonised with a view to preserving, to the greatest extent possible, the cross-country comparability of the resulting data.

This article presents the methodology underpinning BACH, including the content of the database. It describes the characteristics of national samples and outlines the harmonisation process. BACH is a unique tool for analysing and comparing the financial structure and performance of firms across European countries. A simple case study is also presented in support.

No. 10
19 August 2015
Nowcasting GDP with electronic payments data

Abstract

JEL Classification

E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles

E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications

C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods

Abstract

We assess the usefulness of a large set of electronic payments data comprising debit and credit card transactions, as well as cheques that clear through the banking system, as potential indicators of current GDP growth. These variables capture a broad range of spending activity and are available on a very timely basis, making them suitable current indicators. While every transaction made with these payment mechanisms is in principle observable, the data are aggregated for macroeconomic forecasting. Controlling for the release dates of each of a set of indicators, we generated nowcasts of GDP growth for a given quarter over a span of five months, which is the period over which interest in nowcasts would exist. We find that nowcast errors fall by about 65 per cent between the first and final nowcast. Evidence on the value of the additional payments variables suggests that there may be modest reductions in forecast loss, tending to appear in nowcasts produced at the beginning of a quarter. Among the payments variables considered, debit card transactions appear to produce the greatest improvements in forecast accuracy.

No. 9
14 July 2015
Quantifying the effects of online bullishness on international financial markets

Abstract

JEL Classification

C1 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General

C12 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Hypothesis Testing: General

Abstract

Computational methods to gauge investor sentiment from commonly used online data sources that rely on machine learning classifiers and lexicons have shown considerable promise, but suffer from measurement and classification errors. In our work, we develop a simple, direct and unambiguous indicator of online investor sentiment, which is based on Twitter updates and Google search queries. We examine the predictive power of this new investor bullishness indicator for international stock markets. Our results indicate several striking regularities. First, changes in Twitter bullishness predict changes in Google bullishness, indicating that Twitter information precedes Google queries. Second, Twitter and Google bullishness are positively correlated to investor sentiment and lead established investor sentiment surveys. The former, in particular, is a more powerful predictor of changes in sentiment in the stock market than the latter. Third, we observe that high Twitter bullishness predicts increases in stock returns, with these then returning to their fundamental values. We believe that our results may support the investor sentiment hypothesis in behavioural finance.

No. 8
30 June 2015
New and timely statistical indicators on government debt securities

Abstract

JEL Classification

E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy

H63 : Public Economics→National Budget, Deficit, and Debt→Debt, Debt Management, Sovereign Debt

H68 : Public Economics→National Budget, Deficit, and Debt→Forecasts of Budgets, Deficits, and Debt

Abstract

New monthly statistical indicators on government debt securities for euro area countries have now been developed on the basis of the information contained in the Centralised Securities Database (CSDB), an internal database available to the European System of Central Banks (ESCB). The CSDB is jointly operated by the ESCB and contains timely and high-quality security-by-security reference data on debt securities, equities and investment funds. The new indicators on government debt securities provide an indication of the expected disbursements made for the servicing of issued debt securities together with the associated interest rate (nominal yield), broken down by country, original and remaining maturity, currency and type of coupon rate.

This paper describes in detail the newly compiled statistical information and thus contributes to further describing the euro area government bond markets. The new indicators on euro area government debt securities are also highly relevant for policy-making and monetary and fiscal analyses. They indicate that, as at December 2014, the debt service scheduled for such securities in 2015 stood at approximately 15.9% of GDP (

No. 7
9 April 2015
Financial assistance measures in the euro area from 2008 to 2013: statistical framework and fiscal impact

Abstract

JEL Classification

H81 : Public Economics→Miscellaneous Issues→Governmental Loans, Loan Guarantees, Credits, Grants, Bailouts

Abstract

This paper summarises the accounting principles and methodology used by statisticians within the European System of Central Banks (ESCB) to assess the impact on the government

No. 6
9 December 2014
Modelling industrial new orders for the euro area

Abstract

JEL Classification

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

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.

No. 5
8 September 2014
Social media sentiment and consumer confidence

Abstract

JEL Classification

C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?

Abstract

Changes in the sentiment of Dutch public social media messages were compared with changes in monthly consumer confidence over a period of three-and-a-half years, revealing that both were highly correlated (up to r = 0.9) and that both series cointegrated. This phenomenon is predominantly affected by changes in the sentiment of all Dutch public Facebook messages. The inclusion of various selections of public Twitter messages improved this association and the response to changes in sentiment. Granger causality studies revealed that it is more likely that changes in consumer confidence precede those in social media sentiment than vice-versa. A comparison of the development of various seven-day sentiment aggregates with the monthly consumer confidence series confirmed this finding and revealed that the social media sentiment lag is most likely in the order of seven days. This indicates that, because of the ease at which social media sentiment-based data are available and can be processed, they can be published before the official consumer confidence publication and certainly at a higher frequency. All research findings are consistent with the notion that changes in consumer confidence and social media sentiment are affected by an identical underlying phenomenon. An explanation for this phenomenon can be found in the Appraisal-Tendency Framework (Han et al. 2007), which is concerned with consumer decision-making. In this framework, it is claimed that a consumer decision is influenced by two kinds of emotions, namely the incidental and the integral. In this framework, the integral emotion is relevant for the decision at stake, whereas the incidental emotion is not. Based on this theory, consumer confidence is likely to be influenced mainly by the incidental emotion, as consumer confidence is also not measured in relation to an actual decision to buy something. This suggests that the sentiment in social media messages might reflect the incidental emotion in that part of the population that is active on social media. Because of the general nature of the latter, one could denote this the

No. 4
2 December 2013
Valuation of foreign direct investment positions - final report

Abstract

JEL Classification

A39 : General Economics and Teaching→Collective Works→Other

C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access

F21 : International Economics→International Factor Movements and International Business→International Investment, Long-Term Capital Movements

Abstract

The mandate of the Task Force on Valuation of Foreign Direct Investment (FDI) Positions was to review the methods to value direct investment positions stated in Annex III of the Guideline ECB/2011/23 on External Statistics. In particular, the work of the Task Force was focused on reviewing recent developments that may justify amendments to the current method and assessing whether new methods to be applied in the national contributions to the euro area aggregate would lead to a more reliable international investment position (i.i.p.), in particular by increasing the consistency in the valuation of FDI assets and liabilities. The report concluded that in some cases where the use of own funds at book value (OFBV) data would lead to significant biases in the national net i.i.p., other valuation methods than OFBV could be used, adding that national compilers shall exchange information on those positions with the other relevant Member State(s). National compilers should then consider, on a case-bycase basis, adjusting the valuation of those positions in order to strive for a consistent recording of these investments by counterpart EU countries. The Task Force

No. 3
30 September 2013
Quality measures in non-random sampling: MFI interest rate statistics

Abstract

JEL Classification

C42 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Survey Methods

E43 : Macroeconomics and Monetary Economics→Money and Interest Rates→Interest Rates: Determination, Term Structure, and Effects

Abstract

Traditional literature on sampling techniques focuses mainly on statistical samples and covers non-random (non-statistical) samples only marginally. Nevertheless, there has been a recent revival of interest in non-statistical samples, given their widespread use in certain fields like government surveys and marketing research, or for audit purposes. This paper attempts to set up common rules for non-statistical samples in which only data on the largest institutions within each stratum are collected. This is done by focusing on the statistics compiled by the European System of Central Banks (ESCB) on the interest rates of monetary financial institutions (MFIs) in countries of the European Union. The paper concludes by proposing a way of establishing common rules for non-statistical samples based on a synthetic measurement of a mean of absolute errors.

No. 2
8 April 2013
The Eurosystem Household Finance and Consumption Survey - Results from the first wave

Abstract

JEL Classification

D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis

D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance

D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions

Abstract

This report summarises key stylised facts from the first wave of the Eurosystem Household Finance and Consumption Survey, which provides household-level data collected in a harmonised way in 15 euro area countries for a sample of more than 62,000 households. The report presents results on household assets and liabilities, income, and indicators of consumption and credit constraints.

No. 1
8 April 2013
The Eurosystem Household Finance and Consumption Survey - Methodological report

Abstract

JEL Classification

D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis

D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance

D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions

Abstract

This report summarises the methodologies used in the first wave of the Eurosystem Household Finance and Consumption Survey, which provides household-level data collected in a harmonised way in 15 euro area countries for a sample of more than 62,000 households. The report presents the methodologies applied in areas such as data collection, sample design, weighting, imputation, and variance estimation. It also analyses issues like differential unit and item non-response and other issues that may have an effect on the comparability of the survey data across countries

Disclaimer: Please keep in mind that the papers are published in the name of the author(s). Their views do not necessarily reflect those of the ECB.