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BANK OF GREECE STATISTICS DIVISION

BANK OF GREECE STATISTICS DIVISION. JOINT NATIONAL BANK OF THE REPUBLIC OF MACEDONIA/ECB SEMINAR From ITRS to Direct Reporting 3 OCTOBER 2013 Alexandros Milionis. External Sector Statistics. Balance of payments and International investment position FDI and related statistics

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BANK OF GREECE STATISTICS DIVISION

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  1. BANK OF GREECESTATISTICS DIVISION JOINT NATIONAL BANK OF THE REPUBLIC OF MACEDONIA/ECB SEMINAR From ITRS to Direct Reporting 3 OCTOBER 2013 Alexandros Milionis

  2. External Sector Statistics • Balance of payments and International investment position • FDI and related statistics • International reserves and foreign currency liquidity templates

  3. DATA COLLECTION SYSTEMS (1) PREHISTORY: • Up to the1990’s data collection was based on an exchange control framework and banking statistics (aggregated data) HISTORY • Since 1999 a new collection system in effect following the methodology of BPM5 of IMF resident/non-resident transaction by transaction. • For a detailed presentation of the methodology, sources and output for BoP data for all EU countries ,see ECB’s  publication ‘European Union balance of payments/international investment position statistical methods’ May 2007

  4. DATA COLLECTION SYSTEMS (2) TODAY Present data collection system at the Bank of Greece - Mixed: • Main data source: International transactions reporting system (ITRS) – • Direct Reporting: For the following BOP items only:

  5. DATA COLLECTION SYSTEMS (3) Direct Reporting sources of information • Frontier travel survey (travel item) • Oil refineries (oil account) • Mutual funds • Investment companies • Stock exchange firms • Custodians and end-investors stock data (s-b-s) • Central Securities Depository • Annual FDI survey • Annual survey on external assets and liabilities • Residents non-residents transactions without the intermediation of resident banks

  6. DATA COLLECTION SYSTEMS (4) Other sources of Direct Reporting data: • Data from BoG’s departments for: • current transfers • general government • reserve assets • electronic secondary securities market (HDAT) – bonds/MMI • Consolidated Balance Sheet of MFI’s • General Accounting Office (Ministry of Finance) • ELSTAT, National Accounts and Foreign Trade • BIS (household’s data)

  7. REASONS FOR ITRS ABANDONMENT • PRAGMATIC • Insufficiency (transactions without a resident-MFI intermediation) • Inaccuracy (including miscoding, funnel effect, etc.) • LEGAL Exemption threshold of declaration at European level (Reg EC 2560/2001) • 2002: 12 500 € • 2010: 50 000 € (Reg EC 924/2009) • 2016: full exemption (Reg EC 260/2012)

  8. TWO STRIKING EXAMPLES OFINEFFICIENCY/INACCURACY OF ITRS • LOANS GRANTED BY NON RESIDENT MFIs TO RESIDENT ENTERPRISES (further details later) • TRAVEL RECEITS

  9. DIRECT REPORTING POPULATION OF POTENTIAL DIRECT RESPONDENTS (1) • FINANCIAL SECTOR • MFIs • Unit trusts • Insurance Companies • Pension funds i.e. small and well defined population sweeping

  10. POPULATION OF POTENTIAL DIRECT RESPONDENTS (2) • NON FINANCIAL SECTOR much larger and widely dispersed populationsampling • OTHERS • Public Sector • Individuals (households) • Embasies • Notaries

  11. METHODOLOGICAL ISSUES • Two characteristics of practical importance: • Amount of BOP transactions “concentrated” in a relatively small number of transactors. This is more evident in financial companies. • “Concentration” quite stable over time. Therefore complete sweeping may not be imperative even for financial companies.

  12. Non-financial companies -sampling • Take advantage of some stylized facts and form selection criteria. • For non financial companies the sector of activity (e.g. NACE classification) and size should be taken into account for the selection of the sample. • For companies of congenial activities the probability of performing BOP transactions as well as the amount of transactions are related to companies’ financial data.

  13. METHODOLOGICAL ISSUES • Define a dichotomous dummy variable, valued 1= BOP transactions, 0=no BOP transactions, as the dependent variable and financial data of companies as causal factors (independent variables). • Use two-group discriminant analysis for (possible): • Discrimination • Forecasting

  14. Discriminant analysis Geometric representation

  15. AN EXAMPLE ANALYSISLOANS GRANTED TO RESIDENT COMPANIES BY NON-RESIDENT MFIs • Sample size: 1050 • Response rate: 88%

  16. AN EXAMPLE ANALYSIS (2) • Some Results Hotelling’s T-squate statistic =0.993 (statistically significant at 1%) That means discrimination is possible

  17. AN EXAMPLE ANALYSIS (3) • So total assets may be used as the primary selection criterion

  18. AN EXAMPLE ANALYSIS (4) • A simple model

  19. AN EXAMPLE ANALYSIS (5) • Basic Reference: Karapappas A. P. and Milionis A. E. (1999): Estimation and Analysis of external debt in the private sector, Economic Bulletin, 14, pp43-53, Bank of Greece,

  20. Existing ITRS data with transactors very useful! • The initial “core” of the direct reporters registry. • Assessment of the quality of statistical models. • Use the statistical models to update the registry.

  21. POTENTIAL DIFFICULTIES WITH DIRECT REPORTING • TRANSACTIONS BY PHYSICAL ENTITIES IN GENERAL • GOODS NOT CROSSING THE FRONTIERS • TRANSPORTATION SERVICES – SEA TRANSPORT • INCOME-COMPENSATION OF EMPLOYEES • CURRENT TRANSFERS- WORKERS’ REMITTANCES • INVESTMENTS IN REAL ESTATE • DEPOSITS OF GREEK RESIDENTS ABROAD • DEPOSITS OF NON-RESIDENTS IN GREECE

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