Enhancing MIP data coverage: back-calculation and estimates of missing values ESTP Course Luxembourg 9-11 December 2014 Ferdinando Biscosi, MIP TF

Outline Background Methodology Approval workflow An application: HPI Other applications

Policy makers need a complete picture of the economy Background Policy makers need a complete picture of the economy Back-calculation & Missing values estimation Revisions New production standards New classifications Missing values

When do we need to back-calculate? Changes in methodology Change in classification systems Introduction of new regulations Major revisions

Back-calculation (1) Based on transparent and robust estimation techniques Targeted horizon back in time Available primary data at national level, historical context

Back-calculation (2) Choice of the model Set of validation criteria Simple, parsimonious, limited need for subjective judgment Set of validation criteria Measurable criteria, in agreement with MSs

Back-calculation (3) First step: Planning Variable to be back-calculated and possible related variables to be used as proxy Optimal horizon

Back-calculation (4) Second step: Data availability Search for: alternative sources alternative frequencies (quarterly, etc.) proxies

Back-calculation (5) Third step: Model selection Two scenarios: Only the target series is available There are related series with greater coverage

When only target series is available… series transformation (inverse) ARIMA model selection test parameters significance back series calculation

Issues: When only target series is available… Only limited numbers of steps in the past Feasibility depends on length of target series

When additional data is available… data transformation test correlation between additional and target series selection of explanatory variable OLS regression on overlap period obtained parameters used to estimate the back series

Issues: When additional data is available… Additional data not correlated to target series Feasibility depends on length of common period

Model selection Second scenario: additional data

Approval workflow: internal, at Eurostat level MIP back-calculations technical note feedback 1st Responsible Unit in Eurostat

Approval workflow: external, at MS level MIP Task Force feedback Responsible Unit in Eurostat technical note feedback 2nd Country contact NSI/NCB

The technical note Introduction about the MIP exercise Note explaining the additional data used and the model selected Schedule by which data should be validated

An application: HPI (1) Background New legislation entered into force in 2013 not covering past periods Some series rather short, starting in 2009 Data available for some past periods from other sources (NCB, ECB, BIS, OECD) Aim: ESTAT (2010=100) series for the period 2000q1 onward Quarterly series used to derive annual ones

An application: HPI (2) Coverage: 10th October 2014

An application: HPI (3) Coverage: 10th October 2014 13 Member States had at least one missing value Corresponding to 17.5% of the total number of required values

An application: HPI (4) Current coverage na Enlarged

An application: HPI (5) Work done: CZ NSI has provided back data for the period 2000-2007 BG NSI has provided estimates for the period 2001-2008 ES, LV, LT* for the period 2000-2005 and CY for 2002- 2004: NSIs have approved Eurostat estimates 2000-2005 (published flagged as estimates) EL: NCB data used by Eurostat as estimates AT: NCB data used by Eurostat as estimates until NSI data become available * LT MIP headline indicator not available for years previous 2006 due to derogation on the deflator

HPI – metadata file see: http://epp. eurostat. ec. europa

Example: Latvian HPI (1) 2000Q1 2006Q1 2013Q4   overlapping series NSI series ECB series Back-calculated Available

Example: Latvian HPI (2) High correlation index on the common period Indexes log transformed and first order differentiated to achieve stationarity OLS regression ran on common period Obtained parameters used to back-calculate ESTAT quarterly series Annual figures derived by quarterly ones

Example: Latvian HPI (3)

Other applications (1) When do we make estimates? Why/what for? Missing annual figures of the previous year Why/what for? Used only for Commission internal purposes Values are not officially disseminated/neither communicated to the MS DG ECFIN uses these figures in their forecast models

Other applications (2) Only target series available Test for parameters Estimation of missing figures using ARIMA models

Other applications (3) Additional related series available Test correlation between additional and target series Proxy selection Estimation of missing figures using proxy

Other applications: issues As for the back-calculation: Length of the target series Correlation between proxy and target series Parameters not significant

Future applications Unit Labour Cost The auxiliary indicator expressed as percentage change over 10 years ESA 2010 entering into force in 2014 NACE Rev. 2 entering into force in 2008

Open discussion Methods used in NSIs Exchange of experiences