A word on metadata sheets and observed heterogeneity in ad hoc quality indicators of BCS data Presentation by Christian Gayer DG ECFIN A.4.2, Business.

Slides:



Advertisements
Similar presentations
Paul Smith Office for National Statistics
Advertisements

On the pulse of the property world Transaction based indices for the UK commercial property market Steven Devaney (University of Aberdeen) Roberto Martinez.
4/16/ Ardavan Asef-Vaziri Variable of interest Time Series Analysis.
Random Assignment Experiments
EVAL 6970: Meta-Analysis Vote Counting, The Sign Test, Power, Publication Bias, and Outliers Dr. Chris L. S. Coryn Spring 2011.
1 European Conference on Quality in Official Statistics Rome, 8-11 July 2008 Improving the quality and the quality assessment of the Labour Force Survey.
Methodological and Analytical Issues Gaia Dallera 6 June,
Correlation and regression
Building Up a Real Sector Confidence Index for Turkey Ece Oral Dilara Ece Türknur Hamsici CBRT.
Making use of the financial services survey in Commission policy analysis Presentation by Staffan LINDÉN DG ECFIN, Directorate of Financial Markets and.
United Nations Statistics Division Scope and Role of Quarterly National Accounts Training Workshop on the Compilation of Quarterly National Accounts for.
Moving Averages Ft(1) is average of last m observations
1 Chapter 4 Sources of Macroeconomic Fluctuations © Pierre-Richard Agénor and Peter J. Montiel.
Chapter 5 Time Series Analysis
Part II – TIME SERIES ANALYSIS C2 Simple Time Series Methods & Moving Averages © Angel A. Juan & Carles Serrat - UPC 2007/2008.
Sébastien FAIVRE INSEE Workshop on scanner data, Stockholm, /06/2012 Would scanner data improve the French CPI?
OECD Short-Term Economic Statistics Working PartyJune Impact and timing of revisions for seasonally adjusted series relative to those for the.
OECD Short-Term Economic Statistics Working PartyJune Analysis of revisions for short-term economic statistics Richard McKenzie OECD OECD Short.
Slides 13b: Time-Series Models; Measuring Forecast Error
CHAPTER 18 Models for Time Series and Forecasting
The ECB Survey of Professional Forecasters Luca Onorante European Central Bank* (updated from A. Meyler and I.Rubene) October 2009 *The views and opinions.
Lecture 4 Time-Series Forecasting
Tendency Surveys terminology Rosa Ruggeri Cannata – Eurostat Third International Seminar on Early Warning and Business Cycle Indicators 17 – 19 November.
Business Forecasting Used to try to predict the future Uses two main methods: Qualitative – seeking opinions on which to base decision making – Consumer.
European Commission Directorate General Economic and Financial Affairs Using BCS data for tracking q-o-q GDP growth Andreas Reuter Business and consumer.
Chap 20-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 20 Sampling: Additional Topics in Sampling Statistics for Business.
Planning Demand and Supply in a Supply Chain
The next step in performance monitoring – Stochastic monitoring (and reserving!) NZ Actuarial Conference November 2010.
Recent Developments of the OECD Business Tendency and Consumer Opinion Surveys Portal coi/coordination
Slide 1 Estimating Performance Below the National Level Applying Simulation Methods to TIMSS Fourth Annual IES Research Conference Dan Sherman, Ph.D. American.
Constant Price Estimates Expert Group Meeting on National Accounts Cairo May 12-14, 2009 Presentation points.
Q20101 National accounts revisions: Italian manufacturing productivity analysis Alessandro Faramondi Istat – National Statistical Institute.
1 Things That May Affect Estimates from the American Community Survey.
DSc 3120 Generalized Modeling Techniques with Applications Part II. Forecasting.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Time Series Forecasting Chapter 13.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 4 Gathering Data Section 4.3 Good and Poor Ways to Experiment.
Time Series Analysis and Forecasting
Joint OECD / European Commission workshop on international development in business and consumer tendency surveys Nov Task force on improvement.
Things that May Affect the Estimates from the American Community Survey Updated February 2013.
5-4-1 Unit 4: Sampling approaches After completing this unit you should be able to: Outline the purpose of sampling Understand key theoretical.
Impact of updating weights on tracking performance and volatility: Industry survey G. Bruno, L. Crosilla, P. Margani, A. Righi EU Workshop on Recent Developments.
10B11PD311 Economics. Process of predicting a future event on the basis of past as well as present knowledge and experience Underlying basis of all business.
Seðlabanki Íslands Inflation control around the world: Why are some countries more successful than others? Thórarinn G. Pétursson Central Bank of Iceland.
© Federal Statistical Office Germany, Division IB, Institute for Research and Development in Federal Statistics Sheet 1 Surveys, administrative data or.
1 DATA PRESENTATION AND SEASONAL ADJUSTMENT - DATA AND METADATA PRESENTATION TERMINOLOGY - DATA PRESENTATION AND SEASONAL ADJUSTMENT - DATA AND METADATA.
1 European Commission 2007 European Commission Directorate General for Economic and Financial Affairs Report: The Economic Climate Tracer – A tool to visualise.
European Commission Directorate General Economic and Financial Affairs The EU Programme of Business and Consumer surveys: scope and relevance for economic.
9 th Euroindicators Working Group Luxembourg, 4 th & 5 th December 2006 Eurostat - Unit D1 Key Indicators for European Policies.
1 Revisions analysis of OECD composite leading indicators (CLI) Emmanuelle Guidetti Third Joint European Commission OECD Workshop on Business and Consumer.
Demand Forecasting Prof. Ravikesh Srivastava Lecture-11.
Main results of the Break-out session on Tendency Surveys Gian Paolo Oneto Istat.
United Nations Economic Commission for Europe Statistical Division Production and dissemination of short-term economic statistics: the need for long timeseries,
1 ICP PPP Methods Regional Course on Price Statistics and ICP Male, Maldives September 2005 TIMOTHY LO Statistician, International Comparison Program.
International portfolio diversification benefits: Cross-country evidence from a local perspective By J. Driessen and L. Laeven Presented by Michal Kolář,
Demand Management and Forecasting Chapter 11 Portions Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
Forecast 2 Linear trend Forecast error Seasonal demand.
Demand Forecasting Production and Operations Management Judit Uzonyi-Kecskés Ph.D. Student Department of Management and Corporate Economics Budapest University.
Chapter 11 – With Woodruff Modications Demand Management and Forecasting Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.
Yandell – Econ 216 Chap 16-1 Chapter 16 Time-Series Forecasting.
European Commission Directorate General for Economic and Financial Affairs The harmonised EU investment survey: What can it tell us about investment growth.
Carsten Boldsen Hansen Economic Statistics Section, UNECE
Competition, financial innovation and commercial
FORCASTING AND DEMAND PLANNING
Macroeconomic heatmap taking the temperature of the Estonian economy
Global Assessment on Tendency Surveys
New Composite Indicators Based on KOF Business Surveys
Possible complements to improve EU/EA estimation techniques
Ronny Nilsson Statistics Directorate OECD
Performance of Fiscal Rules
Structural Approach Potential output - part I
Presentation transcript:

A word on metadata sheets and observed heterogeneity in ad hoc quality indicators of BCS data Presentation by Christian Gayer DG ECFIN A.4.2, Business and consumer surveys and short-term forecast BCS Workshop November 2012, Brussels

Background (1) Transparency calls for regular updating of metadata sheets Apart from contact data etc, metadata sheets contain valuable info on sampling (universe, frame, sampling method, sample size, sampling error, response rates, treatment of non- response, weighting etc) Ideally, sheets should enable users to gauge the "quality" of survey data

Background (2) Quality is multi-facetted (relevance, accuracy, timeliness, accessibility, comparability, …) Focus here on accuracy Components are: sampling errors and non-sampling errors (frame, measurement, processing, non-response, assumptions) Sampling error depends on 1) inherent variability of figures to be measured, 2) sampling design, esp. sample size ("sample 4 times larger  sampling error 2 times smaller")

Background (3) High sampling error reduces accuracy of point estimates Also leads to higher volatility of estimates in time Month-on-month changes therefore subject to noise which is increasing in the sampling error Currently: particular interest in signals of turning point The more noise, the more difficult to detect TPs We look at ad-hoc quality indicators of BCS data

Outline Focus here on 1.Sample sizes 2.Volatility 3.Tracking performance

1. Gross sample sizes Top (green) and lowest (red) 10 Samples vary broadly as function of country size

Gross sample sizes (total) Outliers particularly visible for large countries Response rates have to be taken into account

Effective sample sizes Top (green) and lowest (red) 10 Effective samples can be significantly reduced, reflecting low response rates (  bias and higher sampling error) Largest effective samples in indu & serv, smallest in reta & buil

Effective sample sizes (total) Outliers persist for at least two countries

2. Measure of the volatility/noise in the series: Months for cyclical dominance (MCD) MCD = Time span (in months) that one has to wait before one can attribute a change of direction to cyclical rather than irregular (noise) factors Based on time series decomposition into trend/cycle and irregular component Computation of m-on-m, 2-month, 3-month, etc. changes Computation of absolute averages of these n-period changes Comparison for the two components (ratio irreg/trend-cycle)

mean|IR-IR(-1)|= 2.19 mean|IR-IR(-2)|= 2.18 mean|IR-IR(-3)|= 2.16 mean|TC-TC(-1)|= 0.88 mean|TC-TC(-1)|= 1.74 mean|TC-TC(-1)|= 2.57  MCD=3 >>< >><

MCDs for confidence indicators 1 or 2: green 4 or more: red EU/EA aggregates are smoother ESIs are smoother than CIs Some CIs indicate change in cyclicaal conditions immediately (MCD=1) Oder of MCDs across surveys in line with sample sizes (indu & serv<cons<reta&buil) Strong variation across countries Irregular component can dominate cycle even after 4,5,6 months Caveat: Not always same sample

Some examples (1) n=2660 MCD=1 n=700 MCD=5 n=1625 MCD=1 n=601 MCD=5

Some examples (2) n=651 MCD=2 n=180 MCD=4 n=2400 MCD=1 n=190 MCD=5

But…. n=743 MCD=2 n=2438 MCD=5

Plotting sample sizes (effective) vs. MCDs

Continuous measure: Ratio of average absolute 2-month changes in irreg to trend/cycle Overall: no strong evidence, but very large samples correspond with low volatility (exceptions: Reta FR, PL; Serv RO)

Summary on volatility MCDs as materialisation of sampling error Wide differences in the usefulness of results for detecting trends and TPs In some cases volatility has to be reduced, otherwise short-term noise buries cyclical info we are interested in Ways to reduce volatility: larger samples, higher response rates, better stratification/weighting, stabilisation of (panel) responses, …

3. Tracking performance Behaviour with respect to hard reference series the indicators are supposed to track Reference series: growth in GDP, IP, value added in services, private consumption, building production index Biased estimates (due to e.g. frame errors or systematic non-response) should have worse tracking performance than unbiased estimates

Correlation with reference series >75% green, <50% red Correlation of EU/EA aggregates higher except for retail ESI more strongly correlated with GDP than CIs with sector reference series CIs in indu, serv and buil on average better than cons, reta (can also point to worse CI composition) Some countries fare much better than pothers There should be some positive correlation between broad sector CIs and respective output data Link to sample sizes?

Correlations vs. sample sizes Some visual correspondence, but not significant Non-sampling errors likely more important (frames, systematic non-response,…)

Conclusions (1) Few institutes provide info about the sampling error of their estimates Need some measure of volatility / the margin of error around the balance results Here we looked at MCDs instead Strong differences across countries w.r.t. sample sizes, smoothness/volatility and tracking performance (these are already aggregated indicators for total sectors and combining several questions!)

Conclusions (2) Which factors are driving these differences? Volatility: sample size and other characteristics of sampling method, … Tracking performance/bias: inappropriate frame, treatment of non-response, … Need to develop a common framework for the assessment… … with a view to a necessary improvement of the results in some cases We propose to set up a Taskforce "BCS quality assessment framework"

Thanks for your attention