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Back Matter

Editor(s):
Adriaan Bloem, Robert Dippelsman, and Nils Mæhle
Published Date:
May 2001
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    Bibliography

    This bibliography lists material bearing on quarterly national accounts that came to the authors’ notice as well as country sources and method publications found on the IMF Dissemination Standards Bulletin Board, http://dsbb.imf.org.

    I. Introduction

    II. Strategic Issues in Quarterly National Accounts

      Cainelli, G., and C. Lupi, 1999, “The Choice of the Aggregation Level in the Estimation of Quarterly National Accounts,”Review of Income and Wealth, Series 45 (December), pp. 48392.

      Caplan, D., and S. Lambert, 1995, “Quarterly GDP – Process and Issues,”Economic Trends, No. 504 (October), pp. 4043.

      Cope, I., 1995, “Quarterly National Accounts in the United Kingdom: Overview of UK Approach,” Economic Trends, No. 498 (April), pp. 2225.

      Janssen, R., and S. Algera, 1988, Methodology of the Dutch System of Quarterly Accounts, Occasional Paper No. NA-025 (Voorburg: Netherlands Central Bureau of Statistics).

      Janssen, R., P. Oomens, and N. van Stokrom, 1994, “Data Flows in the Dutch Quarterly National Accounts,”paper presented at the INSEE-Eurostat Workshop on Quarterly National Accounts, Paris, December.

    III. Sources for GDP and Its Components

    General and Multicountry:

      Daniel, D., 1996, “The Use of Quarterly Current Price Output Data in National Accounts,” Economic Trends, No. 516 (October), pp. 1623.

      Eurostat, 1998a, Methodology of Industrial Short Term Indicators—Rules and Recommendations (Luxembourg; Office for Official Publications of the European Communities).

      Eurostat, 1998b, Handbook on the Design and Implementation of Business Surveys (Luxembourg: Office for Official Publications of the European Communities).

      Pike, R., and G. Reed, 2000, “Introducing the Experimental Monthly Index of Services,” Economic Trends, No. 565 (December), pp. 5163.

      United Nations, 1986, “Handbook of National Accounting. Accounting for Production: Sources and Methods,” Studies in Methods, Series F, No. 39 (New York).

    Country publications:

    The following list of country publications is derived from information on the SDDS website http://dsbb.imf.org; the SDDS website also contains summary methodologies:

    • Argentina: Sistema de Cuentas Nacionales Argentina Año Base 1993, Estimaciones trimestrales y anuales: años 1993–1997, Ministerio de Económia y Obras y Servicios Públicos, Spanish.

    • Australia: Australian National Accounts: Concepts, Sources and Methods, ABS Catalogue Number 5216.0, and Statistical Concepts Reference Library on CD-ROM, Australian Bureau of Statistics.

    • Austria: Annex B of the regulation (EG) Nr. 2223/96 of the European Council.

    • Canada: Guide to the Income and Expenditure Accounts. Catalogue No. 13–603.E-F; A Guide to the Financial Flow and National Balance Sheet Accounts, Catalogue No. 13–585.E-F;A User Guide to the Canadian System of National Accounts, Catalogue No. 13–589.E-F; and The Input-Output Structure of the Canadian Economy, Catalogue No. 15–511., Statistics Canada.

    • Chile: Cuentas Nacionales de Chile 1985–1992., Central Bank of Chile.

    • Colombia: Metodología de Cuentas Nacionales, Departamento Administrativo Nacional de Estadísticas.

    • Croatia: Quarterly Gross Domestic Product, Monthly Statistical Report, and Statistical Yearbook, Central Bureau of Statistics.

    • Czech Republic: National Accounts for the Czech Republic and Annual National Accounts of the Czech Republic 1997, Czech Statistical Office.

    • Denmark: Konjunkturstatistik: Supplement, Statistics Danmark.

    • Ecuador: Cuentas Nacionales Trimestrales del Ecuador 1980.l1–1991..1 and Cuentas Nacionales Trimestrales del Ecuador J965.1–1992..11, Banco Central del Ecuador.

    • El Salvador: El Salvador: Metodologia del Producto Interno Bruto Trimestral, Central Reserve Bank of El Salvador.

    • Estonia: National Accounts of Estonia, Statistical Office of Estonia.

    • Finland: Statistics Finland Statistical Studies, No. 62 (1980), Uotila, Leppä, Katajala, Statistics Finland.

    • France: INSEE. Méthodes n°l3: Comptes nationaux trimestriels, Institut National de la Statistique et des Etudes Economiques.

    • Germany: Selected Working Documents on Federal Statistics in number 7 Survey of National Product Calculations of the Federal Statistical Office, number 19 Housing Rentals, number 21 Input-Output Tables as the Basis of National Product Calculation, number 22 Construction Investments, number 23 Production Approach, number 24 Equipment Investments, and number 25 Subsidies, and the working paper Private Consumption, State Consumption, Net Exports, Federal Statistical Office.

    • Hong Kong SAR, China: Gross Domestic Product 1961 to 1999, Census and Statistics Department.

    • Hungary: 1999 issue of National Accounts Hungary, Hungarian Central Statistical Office.

    • Iceland: Compiling Icelandic National Accounts, Documentation of Methods Applied, Output and Expenditure Approaches, National Economic Institute.

    • India: 1999 edition of the annual National Accounts Statistics, Central Statistical Organisation.

    • Indonesia: Pendapala Nasional Indonesia, Triwulanan. 1991–93., Badan Pusat Statistik.

    • Ireland: National Income and Expenditure, Central Statistics Office.

    • Israel: Current Briefings in Statistics ( March of each year), Central Bureau of Statistics.

    • Italy: Statistica in Breve (May 26, 1999 and August 4, 1999), Comunicato Stampa (June 30, 1999), Note Rapide (April 30, 1999), Istituto Nazionale di Statistica.

    • Japan, The System of National Accounts in Japan, Economic Planning Agency.

    • Korea: Estimation Methods of National Income Accounts in Korea, Bank of Korea.

    • Latvia: National Accounts of Latvia, Central Statistical Bureau of Latvia.

    • Lithuania: Lithuanian National Accounts, Statistics Lithuania.

    • Malaysia: Quarterly National Product and Expenditure Account, xxx Quarter xxxx, Departments of Statistics, Malaysia.

    • Mexico: Producto Interno Bruto Trimestral, Oferta y Demanda Global Trimestral a Precios Corrientes, and Oferta y Utilización Trimestral a Precios Constantes de 1993, Instituto Nacional de Estadística, Geografía e Informática.

    • Netherlands: Fast GDP-growth Estimates, Data Flows in QNA, The Methodology of the Dutch System of Quarterly National Accounts, and A Provisional Time Series of 1977 – 1994 Quarterly National Accounts data linking up with the 1995 – 1999 ESA 1995 figures: method and results, Statistics Netherlands.

    • Norway: Quarterly National Accounts 1978–1998. Production, Uses and Employment, Statistics Norway.

    • Peru: Cómo Leer la Nota Semanal, Central Reserve Bank of Peru.

    • Philippines: Sources and Methods, National Statistical Coordination Board.

    • Poland: Gross Domestic Product by Quarters for the Year 1995–1998, Central Statistical Office.

    • Singapore: Singapore National Accounts 1987 and Singapore System of National Accounts, 1995, Department of Statistics.

    • Slovak Republic: Macroeconomic Indicators of Quarterly National Accounts and Value Added and CESTAT Statistical Bulletin, Statistical Office of the Slovak Republic.

    • Slovenia: National Accounts of the Republic of Slovenia. Sources, Methods and Estimates, Statistical Office of the Republic of Slovenia.

    • South Africa: Statistical Release P0441 of June 1999, Statistics South Africa.

    • Spain: Contabilidad Nacional Trimestral de España. Metodología y serie Trimestral 1970–1992., Instituto Nacional de Estadística.

    • Switzerland: Die Quartalsschӓtzungen des Bruttoinlandproduktes, Milteilungsblatt für Konjunkturfragen, Heft 1, State Secretariat for Economic Affairs.

    • Turkey: Gross National Product; Concepts, Methods and Sources, State Institute of Statistics.

    • United Kingdom: Concepts, Sources and Methods and The UK National Accounts, Office for National Statistics.

    • United States: “A Guide to the NIPA’s,” Survey of Current Business, March 1998, Bureau of Economic Analysis.

    IV. Sources for Other Components of the 1993 SNA

      Jenkinson, G., 1997, “Quarterly Integrated Economic Accounts – the United Kingdom Approach,” Economic Trends, No. 520 (March), pp. 6065.

    V. Editing and Reconciliation

      Arkhipoff, O., 1990, “Importance et diversité des problémes d’agrégation en comptabilité national: esquise d’une théorie générale de I’agrégation,” in La Comptabilité National Face au Defi International, ed. by E. Archambault and O. Arkhipoff (ed) (Paris: Economica).

      Aspden, C, 1990, “Which Is the Best Short-Term Measure of Gross Domestic Product?” in Australian National Accounts: National Income, Expenditure and Product, Catalogue 5206.0 (Canberra: Australian Bureau of Statistics).

      BloemA., F. Maitland-Smith, R. Dippelsman, and P. Armknecht, 1997, “Discrepancies between Quarterly GDP Estimates,”IMF Working Paper 97/123 (Washington: International Monetary Fund).

      Kim, C., G. Salou, and P. Rossiter, 1994, “Balanced Australian National Accounts,” Australian Bureau of Statistics Working Papers in Econometrics No. 94/2 (Canberra: Australian Bureau of Statistics).

      Snowdon, T, 1997, “Quarterly Alignment Adjustments in the UK National Accounts,” Economic Trends, No. 528 (November), pp. 2327.

      Stone, R., D.G. Champernowne, and J.E. Meade, 1942, “The Precision of National Income Estimates,” Review of Economic Studies, Vol. 9, No. 2, pp. 11125.

      Stone, J.R.N., 1975, “Direct and Indirect Constraints in the Adjustment of Observations,” in National Accounts Models and Analysis. To Odd Aukrust in Honor of His Sixtieth Birthday, Samfunnsøkonomiske Studierno. 26 (Social Economic Studies No. 26) (Oslo: Statistics Norway).

    VI. Benchmarking

      Alba, E. de, 1979, “Temporal Dissaggregration of Time Series: A Unified Approach,” in Proceedings of the Business and Economic Statistics Section, American Statistical Association (Washington: American Statistical Association), pp. 35970.

      Barcellan, R., 1994, “ECOTRIM: A Program for Temporal Disaggregation of Time Series,”paper presented at INSEE-Eurostat Quarterly National Accounts Workshop, Paris, December.

      Bassi, V.L., 1939, “Interpolation Formula for the Adjustment of Index Numbers,” in Proceedings of the Annual Meeting of the American Statistical Association (Washington: American Statistical Association).

      Bassi, V.L., 1958, “Appendix A,” in Economic Forecasting, ed. by V.L. Bassi (ed) (New York: McGraw-Hill).

      Bournay, J., and G. Laroque, 1979, “Reflexions sur la methode d’élaboration des comptes trimestriels,” Annales de l’lnsee, Vol. 36 (October-December), pp. 330.

      Chen, Z.-G., P.A., Cholette, and E.B. Dagum, 1997, “A Nonparametric Method for Benchmarking Survey Data via Signal Extraction,” Journal of the American Statistical Association, Vol. 92 (December), pp. 156371.

      Cholette, P.A., 1978, “A Comparison and Assessment of Various Adjustment Methods of Sub-Annual Series to Yearly Benchmarks,” Research Paper No. 78–03-00IB (Ottawa: Statistics Canada).

      Cholette, P.A., 1984, “Adjusting Sub-Annual Series to Yearly Benchmarks,” Survey Methodology, Vol. 10 (December), pp. 3549.

      Cholette, P.A., 1988a, “Concepts, Definitions and Principles of Benchmarking and Interpolation of Time Series,”Working Paper No. TSRA-87–014e (Ottawa: Statistics Canada).

      Cholette, P.A., 1988b, “Benchmarking System of Socio-Economic Time Series,”Working Paper No. TSRA-88–017e (Ottawa: Statistics Canada).

      Cholette, P.A., 1994, “Users’ Manual of Programme BENCH to Benchmark, Interpolate, and Calendarize Time Series Data,”Working Paper No. TSRA-90–008 (Ottawa: Statistics Canada).

      Cholette, P.A., and A. Baldwin, 1988, “Converting Fiscal Year Data into Calendar Values,”Working Paper No. TSRA-88–012e (Ottawa: Statistics Canada).

      Cholette, P.A., and N. Chhab, 1991, “Converting Aggregates of Weekly Data into Monthly Values,” Applied Statistics, Vol. 40, No. 3, pp. 41122.

      Cholette, P.A., and E.B. Dagum, 1994, “Benchmarking Time Series with Autocorrelated Survey Errors,” International Statistical Review, Vol. 62 (December), pp. 36577.

      Chow, G. C, and An-lohLin, 1971, “Best Linear Unbiased Interpolation, Distribution and Extrapolation of Time Series by Related Series,” Review of Economic and Statistics, Vol. 53 (November), pp. 37275.

      Dagum, E.B., Cholette, P.A., and Z.G. Chen, 1998, “A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series,” International Statistical Review, Vol. 66, No. 3, pp. 24569.

      Denton, F.T., 1971, “Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization,” Journal of the American Statistical Association, Vol. 66 (March), pp. 92102.

      Di Fonzo, T., 1994, “Temporal Disaggregation of System of Time Series When Aggregate Is Known. Optimal Versus Adjustment Methods,”paper presented at INSEE-Eurostat Quarterly National Accounts Workshop, Paris, December.

      Durbin, J., and B. Quenneville,1997,“Benchmarking by State Space Models,” International Statistical Review, Vol. 65, No. 1, pp. 2348.

      Dureau, G., 1995, “Methodology of French Quarterly National Accounts,” INSEE MethodsNo. 13 (Paris: INSEE).

      Fernandez, R.B., 1981, “A Methodology Note on the Estimation of Time Series,” Review of Economic and Statistics, Vol. 63 (August), pp. 47176.

      Friedman, M., 1962, “The Interpolation of Time Series by Related Series,” Journal of the American Statistical Association, Vol. 57 (December), pp. 72957.

      Ginsburgh, V.A., 1973, “A Further Note on the Derivation of Quarterly Figures Consistent with Annual Data,” Applied Statistics, Vol. 22, No. 3, pp. 36874.

      Helfand, S.D., N.J. Monsour, and M.L. Trager, 1977, “Historical Revision of Current Business Survey Estimates,” in Proceedings of the Business and Economic Statistics Section, American Statistical Association (Washington: American Statistical Association), pp. 24650.

      Hillmer, S.C., and A. Trabelsi, 1987, “Benchmarking of Economic Time Series,” Journal of the American Statistical Association, Vol. 82 (December), pp. 106471.

      Laniel, N., and K. Fyfe, 1990, “Benchmarking of Economic Time Series,” Survey Methodology, Vol. 16 (December), pp. 27177.

      Lanning, S.G., 1986, “Missing Observations: A Simultaneous Approach versus Interpolation by Related Series,” Journal of Economic and Social Measurement, Vol. 14 (July), pp. 15563.

      Mian, I.U.H., and N. Laniel, 1993, “Maximum Likelihood Estimation of Constant Multiplicative Bias Benchmark Model with Application,” Survey Methodology, Vol. 19 (December), pp. 16572.

      Monsour, N.J., and M.L. Trager, 1979, “Revision and Benchmarking of Business Time Series,” in Proceedings of the Business and Economic Statistics Section, American Statistical Association (Washington: American Statistical Association), pp. 33337.

      Nasse, P., 1973, “Le Systéme des Comptes Nationaux Trimestriels,” Annales de l’lnsee, No. 14 (September-December), pp. 11961.

      Pinheiro, M., and C. Coimbra, 1993, “Distribution and Extrapolation of Time Series by Related Series Using Logarithms and Smoothing Penalties,” Economia, Vol. 17 (October), pp. 35974.

      Sanz, R., 1981, “Metodos de Desagregacion Temporal de Series Economicas,” Banco de Espana, Servicio de Estudios, Seri de estudios economicosno. 22 (Madrid: Banco de Espana). (Also available in English under the title Temporal Disaggregation Methods of Economic Time Series.)

      Schmidt, J. R., 1986, “A General Framework for Interpolation, Distribution, and Extrapolation of Time Series by Related Series,” in Regional Econometric Modeling, ed. by R.Perryman and J.R.Schmidt (ed) (Boston: Kluwer/Nijhoff), pp. 18194.

      Sjöberg, L., 1982, Jämförelse av Uppräkningsmetoder för Nationalräkenskapsdata (Comparison of Adjustment Methods for National Accounts Data), Memorandum (Stockholm: Statistics Sweden).

      Skjӕveland, A.,1985, Avstemming av Kvartalsvise Nasjonalregnskapsdata mot Årlige Nasjonalregnskap (Reconciliation of Quarterly National Accounts Data Against Annual National Accounts), Interne notater 85/22 (Oslo: Statistics Norway).

      Somermeyer, W.H., R. Jansen, and A.S. Louter, 1976, “Estimating Quarterly Values of Annually Known Variables in Quarterly Relationships,” Journal of the American Statistical Association, Vol. 71 (September), pp. 58895.

      Trabelsi, A., and S.C. Hillmer, 1990, “Benchmarking Time Series with Reliable Benchmarks,” Applied Statistics, Vol. 39, No. 3, pp. 36779.

    VII. Mechanical Projections

      Al-Osh, M., 1989, “A Dynamic Linear Model Approach for Disaggregating Time Series Data,” Journal of Forecasting, Vol. 8 (June), pp. 8596.

      Boot, J.C.G., W. Feibes, and J.H.C. Lisman, 1967, “Further Methods of Derivation of Quarterly Figures from Annual Data,” Applied Statistics, Vol. 16, No. 1, pp. 6575.

      Lisman, J.H.C, and J. Sandee, 1964, “Derivation of Quarterly Figures from Annual Data,” Applied Statistics, Vol. 13, No. 2, pp. 8790.

      Stram, D.O., and W.W.S. Wei, 1986, “A Methodological Note on the Disaggregation of Time Series Totals,” Journal of Time Series Analysis, Vol. 7, No. 4, pp. 293302.

      Wei, W.W.S., and D.O. Stram, 1990, “Disaggregation of Time Series Models,” Journal of Royal Statistical Society, Series B, Vol. 52, No. 3, pp. 45367.

    VIII. Seasonal Adjustment and Estimation of Trend-Cycles

      Alterman. W.F., E.Diewert, and R.Feenestra, 1999, “Time Series Approaches to the Problem of Seasonal Commodities,” in International Trade Price Indexes and Seasonal Commodities, ed. by W.F.Alterman, E.Diewert, and R.Feenestra (ed) (Washington: U.S. Bureau of Labor Statistics).

      Australian Bureau of Statistics, 1987, A Guide to Smoothing Time Series—Estimation of “Trend,” Information Paper 1316.0 (Canberra: Australian Bureau of Statistics).

      Australian Bureau of Statistics, 1993, A Guide to Interpreting Time Series-Monitoring “Trends,” Information Paper 1348.0 (Canberra: Australian Bureau of Statistics.).

      Baxter, M., 1999, “Seasonal Adjustment of RPIY,”Economic Trends, No. 546 (May), pp. 3538.

      Bell, W.R., and S.C.Hillmer, 1984, “Issues Involved With the Seasonal Adjustment of Time Series,”Journal of Business and Economic Statistics, Vol. 2 (October), pp. 291349. With comments byH.Akaike, C.Ansley and W.E.Wecker, P.Burman, E.B.Dagum and N.Laniel, M.M.G.Fase, C.Granger, A.Maravall, and D.A.Pierce. (ed)

      Butter, F.A.G. den, and M.M.G.Fase, 1991, Seasonal Adjustment as a Practical Problem (Amsterdam; New York: North-Holland).

      Cleveland, W.S., and S.J.Devlin, 1980, “Calendar Effects in Monthly Time Series: Detection by Spectrum Analysis and Graphical Methods,”Journal of the American Statistical Association, Vol. 75 (September), pp. 48796.

      Compton, S., 1998, “Estimating and Presenting Short-Term Trend,” Economic Trends, No. 538 (September), pp. 3344.

      Compton, S., 2000, “Presentation of Trend Estimates in Official UK and International Practice,”paper presented at the Second International Conference on Establishment Surveys, Buffalo, New York, June.

      Cristadoro, R., and R.Sabbatini, 2000, “The Seasonal Adjustment of the Harmonised Index of Consumer Prices for the Euro Area: A Comparison of Direct and Indirect Methods,” Banca d’Italia temi di discussioneNo. 371 (Rome: Banca d’Italia). Available via the Internet: http://www.bancaditalia.it/pubblicazioni/temidi;internal&action=contenuti.action

      Dagum, E.B., 1982, “Revisions of Time Varying Seasonal Filters,”Journal of Forecasting, Vol. 1 (April-June), pp. 17387.

      Dagum, E.B., 1987, “Monthly Versus Annual Revisions of Concurrent Seasonally Adjusted Series,” in Time Series and Economic Modeling, ed. by I.B.MacNeill and G. J.Umphrey (ed) (Dordrecht: D. Reidel), pp. 13146.

      Dagum, E.B., 1988, The X-11-ARIMA/88 Seasonal Adjustment Method – Foundations and User’s Manual (Ottawa: Statistics Canada).

      Dagum, E.B., and M.Morry, 1984, “Basic Issues on the Seasonal Adjustment of the Canadian Consumer Price Index,”Journal of Business & Economic Statistics, Vol. 2 (July), pp. 25059.

      Dagum, E.B., and N.Laniel, 1987, “Revisions of Trend-Cycle Estimators of Moving Average Seasonal Adjustment Methods,”Journal of Business & Economic Statistics, Vol. 5 (April), pp. 17789.

      Deutsche Bundesbank, 1987, “Seasonal Adjustment as a Tool for Analysing Economic Activity,”Deutsche Bundesbank Monthly Report, Vol. 39 (October), pp. 3039.

      Deutsche Bundesbank, 1991, “Data Adjusted for Seasonal and Working-Day Variations, on the Expenditure Component of GNP,”Monthly Report, Vol. 43 (April), pp. 3540.

      Deutsche Bundesbank, 1999, “The Changeover from Seasonal Adjustment Method Census X-11 to Census X-12-ARIMA,”Monthly Report, Vol. 51 (September), pp. 3951.

      European Central Bank, 2000, Task Force on Seasonal Adjustment; Final Report (Frankfurt).

      Eurostat, 1998, Seasonal Adjustment Methods – A Comparison for Industry Statistics (Luxembourg: Office for Official Publications of the European Communities).

      Findley, D.F., B.C.Monsell, H.B.Shulman, and M.G.Pugh, 1990, “Sliding-Spans Diagnostics for Seasonal and Related Adjustments,”Journal of the American Statistical Association, Vol. 85 (June), pp. 34555.

      Findley, D. F., B.C.Monsell, W.R.Bell, M.C.Otto, and B.-C.Chen, 1996, “New Capabilities and Methods of the X-12-ARIMA Seasonal Adjustment Program,”Journal of Business and Economic Statistics, Vol. 16 (April), pp. 12777. With comments byW.Cleveland, S.Hyllenberg, A.Maravall, M.Morry and N.Chhab, K.Wallis, and E.Ghysels (ed).

      Findley, D.F., and C. C.Hood, undated, X-12-ARIMA and Its Application to Some Italian Indicator Series. Available via the Internet: http://www.cen-sus.gov/srd/vww/x 12istat_abs.html

      Ghysels, E., 1997, “Seasonal Adjustment and Other Data Transformations,”Journal of Business & Economic Statistics, Vol. 15 (October), pp. 41018.

      Hecq, A., 1998, “Does Seasonal Adjustment Induce Common Cycles?”Economic Letters, Vol. 59 (June), pp. 28997.

      Hylleberg, S., ed., 1992, Modelling Seasonality (Oxford: Oxford University Press).

      Jain, R.K., 1989, “The Seasonal Procedure for the Consumer Price Indexes: Some Empirical Results,”Journal of Business & Economic Statistics, Vol. 7 (October), pp. 46174.

      Kenny, P.B., and J.Durbin, 1982, “Local Trend Estimation and Seasonal Adjustment of Economic and Social Time Series,”Journal of the Royal Statistical Society, Series A, Vol. 145, No. 1, pp. 141.

      Knowles, J., 1997, Trend Estimation Practices of National Statistical Institutes, United Kingdom Office for National Statistics Methods and Quality Paper Number 44 (London: Office for National Statistics).

      Knowles, J., and P.Kenny, 1997, An Investigation of Trend Estimation Methods, United Kingdom Office for National Statistics Methods and Quality, Paper Number 43 (London: Office for National Statistics).

      Ladiray, D., and B.Quenneville, 2001, Seasonal Adjustment with the XII Method (New York: Springer-Verlag).

      Lothian, J., and M.Morry, 1977, The Problem of Aggregation: Direct and Indirect Seasonal Adjustment, Time Series Research and Analysis Division Research Paper No. 77-08-001 (Ottawa: Statistics Canada).

      McKenzie, S., 1984, “Concurrent Seasonal Adjustment with Census X-11,”Journal of Business & Economic Statistics, Vol. 2 (July), pp. 23549.

      Organization for Economic Cooperation and Development, 1997, Seasonal Adjustment of Industrial Production Series in Transition Countries in Central and Eastern Europe and the Russian Federation (Paris).

      Pierce, D.A., 1980, “Data Revision With Moving Average Seasonal Adjustment Procedures,”Journal of Econometrics, Vol. 14 (September), pp. 95114.

      Pierce, D.A., and S.McKenzie, 1987, “On Concurrent Seasonal Adjustment,”Journal of the American Statistical Association, Vol. 82 (September), pp. 72032.

      Shiskin, J., A.H.Young, and J.C.Musgrave, 1967, The X-11 Variant of the Census Method II Seasonal Adjustment Program; Technical Paper 15 (Washington: Bureau of the Census, U.S. Department of Commerce).

      Soukup, R., and D.F.Findley, undated, On the Spectrum Diagnostics Used by X-12-ARIMA to Indicate the Presence of Trading Day Effects after Modeling or Adjustment. Available via the Internet: http://www.census.gov/srd/www/rr9903_abs.html

      U.S. Bureau of the Census, undated, X-12-ARIMA Reference Manual. Available via the Internet: http://www.census.gov/srd/www/x12a/x12down_pc.html#xl2doc

      U.S. Bureau of the Census, undated, Manufacturing and Construction Division Frequently Asked Questions on Seasonal Adjustment. Available via the Internet: http://www.census.gov/const/www/faq2.html

      Wallis, K.F., 1982, “Seasonal Adjustment and Revision of Current Data: Linear Filters for the X-11 Method,”Journal of the Royal Statistical Society, Series A, Vol. 145, No. 1, pp. 7485.

    IX. Price and Volume Measures: Specific QNA-ANA Issues

      Al, P.G., Balk B. S. de Boer G.P. den Bakker. 1985, “The Use of Chain Indices for Deflating the National Accounts,”National Accounts Occasional Papers No. 5, (Voorburg: Netherlands Central Bureau of Statistics). Also in Statistical Journal of the United Nations Economic Commission for Europe, Vol. 4 (July1987), pp. 34768.

      Allan, R.G.D., 1975, Index Numbers in Theory and Practice (Chicago: Aldine Publishing Co.)

      Australian Bureau of Statistics, 1998, “Introduction of Chain Volume Measures in the Australian National Accounts,” Information Paper 5248.0 (Canberra: Australian Bureau of Statistics).

      Brueton, A., 1999, “The Development of Chain-Linked and Harmonised Estimates of GDP at Constant Prices,” Economic Trends, No. 552 (November), pp. 3945.

      Dalgaard, E., 1997, “Implementing the Revised SNA: Recommendations on Price and Volume Measures,”Review of Income and Wealth, Series 43 (December), pp. 487503.

      de Boer, S., J.van Dalen, and P.Verbiest, 1997, “The Use of Chain Indices in the Netherlands,”paper presented at the Conference on Measurement Problems in Econometric Modeling, Istituto Nazionale di Statistica, Rome, January. Also presented at the joint UNECE/Eurostat/OECD meeting on national accounts, Paris, June.

      Diewert, W.E., 1976, “Exact and Superlative Index Numbers,”Journal of Econometrics, Vol. 4 (May), pp. 11445.

      Diewert, W.E., 1978, “Superlative Index Numbers and Consistency in Aggregation,”Econometrica, Vol. 46 (July), pp. 883900.

      Diewert, W.E., 1996a, “Price and Volume Measures in the System of National Accounts,” in The New System of National Economic Accounts, ed. by J.Kendrick (ed) (Boston: Kluwer Academic Publisher), pp. 23785.

      Diewert, W.E., 1996b, “Seasonal Commodities, High Inflation and Index Number Theory,”Discussion Paper No. 96–06 (Vancouver: Department of Economics, University of British Columbia, Canada). Available via the Internet: http://web.arts.ubc.ca/econ/diewert/Disc.htm

      Diewert, W.E., 1998, “High Inflation, Seasonal Commodities, and Annual Index Numbers,”Macroeconomic Dynamics, Vol. 43 (December), pp. 45671.

      Diewert, W.E., 2000, “Index Numbers,” in draft Manual on Consumer Price Indices, ed. by the ECE, EEC, ILO, IMF, OECD, UNSD, and World Bank (forthcoming). Available via the Internet: http://www.ilo.org/pubIic/engIish/bureau/stat/guides/cpi/index.htm

      Ehemann, C, 1997, Analyzing the Chain-Dollar Measures of Output: Contribution of Components to Level and Change (unpublished; Washington: U.S. Bureau of Economic Analysis).

      Ehemann, C, A.J.Katz, and B.Moulton, 2000, “How the Chain-Additivity Issue Is Treated in the U.S. Economic Accounts,”paper presented at the 2000 Annual OECD Meeting of National Accounts Experts, Paris, September.

      Forsyth, F.G., and R.F.Fowler, 1981, “The Theory and Practice of Chain Price Index Numbers,”Journal of the Royal Statistical Society, Series A, Vol. 144, No. 1, pp. 22446.

      Fuà, G., and M.Gallegati, 1996, “An Annual Chain Index of Italy’s ‘Real’ Product, 1861–1989,”Review of Income and Wealth, Series 42 (June), pp. 20724.

      Hill, T.P., 1971, The Measurement of Real Product: A Theoretical and Empirical Analysis of the Growth Rates for Different Industries and Countries (Paris: OECD).

      Hill, T.P, 1988, “Recent Developments in Index Number Theory and Practice,” OECD Economic Studies, No. 10 (Spring), pp. 12348.

      Hill, T.P, 1996, “Price and Quantity Measures,” in Inflation Accounting: A Manual on National Accounting Under Conditions of High Inflation, ed. by T.P.Hill (ed) (Paris: OECD), pp. 4356.

      Jackson, C, 1996, “The Effect of Rebasing GDP,” in National Economic and Financial Accounts, Second Quarter1996, Statistics Canada Cat. No. 13-001-XPB (Ottawa: Statistics Canada).

      Janssen, R., and P.Oomens, 1998, “Quarterly Chain Series,”paper presented at the Annual OECD Meeting of National Accounts Experts, Paris, December.

      Landefeld, S., and R.Parker, 1995, “Preview of the Comprehensive Revision of the National Income and Product Accounts: BEA’s New Featured Measures of Output and Prices,”Survey of Current Business, Vol. 75 (July), pp. 3138.

      Landefeld, S., and R.Parker, 1997, “BEA’s Chain Indexes, Time Series, and Measures of Long-Term Economic Growth,”Survey of Current Business, Vol. 77 (May), pp. 5868.

      Lasky, M.J., 1998, “Chain-Type Data and Macro Modeling Properties: The DRI/McGraw-Hill Experience,”Journal of Economic and Social Measurement, Vol. 24 (Summer), pp. 83108.

      Lynch, R., 1996, “Measuring Real Growth – Index Numbers and Chain-Linking,” Economic Trends, No. 512 (June), pp. 2223.

      Moulton, B.R., and E.PSeskin, 1999, “A Preview of the 1999 Comprehensive Revision of the National Income and Product Accounts,”Survey of Current Business, Vol. 79 (October), 617.

      Parker, R.P., and J.E.Triplett, 1996, “Chain-Type Measures of Real Output and Prices in the U.S. National Income and Product Accounts: An Update,”Business Economics, Vol. 31 (October), pp. 3743.

      Reinsdorf, M., E.Diewert, and C.Ehemann, 2000, “Additivity Decompositions of the Change of Fisher, Törnquist and Geometric Mean Indexes,”Discussion Paper No. 01–01 (Vancouver: Department of Economics, University of British Columbia, Canada). Available via the Internet: http://web.arts.ubc.ca/econ/diewert/Disc.htm

      Ribe, M., 1999, “Effect of Subcomponents on Chained Price Indices Like the HICP and the MUICP,”paper presented at the Eurostat meeting of the working party of consumer price indices, Luxembourg, September.

      Szultc, B., 1983, “Linking Price Index Numbers,”in Price Level Measurement: Proceedings of a Conference Sponsored by Statistics Canada, ed. by W.E.Diewert and C.Montmarquette (ed) (Ottawa: Statistics Canada), pp. 53766.

      Triplett, E., 1992, “Economic Theory and BEA’s Alternative Quantity and Price Indexes,”Survey of Current Business, Vol. 72 (April), pp. 4952.

      United Nations, Department of International Economic and Social Affairs, 1979, Manual on National Accounts at Constant Prices, Statistical Papers, Series M, No. 64 (New York).

      Varvares, C., J.Prakken, and L.Guirl, 1998, “Macro Modeling with Chain-Type GDP,”Journal of Economic and Social Measurement, Vol. 24, (Summer), pp. 12342.

      Young, A., 1992, “Alternative Measures of Change in Real Output and Prices,”Survey of Current Business, Vol. 72 (April), pp. 3243.

      Young, A., 1993, “Alternative Measures of Change in Real Output and Prices, Quarterly Estimates for 1959–92,”Survey of Current Business, Vol. 73 (March), pp. 3137.

    X. Work-in-Progress

    XI. Revision Policy and the Compilation and Release Schedule

      Barklem, A.J., 2000, “Revision Analysis of Initial Estimates of Key Economic Indicators and GDP Components,” Economic Trends, No. 556 (March), pp. 3152.

      Di Fonzo, T., S.Pisani, and G.Savio, 1994, “Revisions to Italian Quarterly National Accounts Aggregates: Some Empirical Results,”paper presented at INSEE-Eurostat Quarterly National Accounts Workshop, Paris, December.

      Grimm, B.T., and R.P.Parker, 1998, “Reliability of the Quarterly and Annual Estimates of GDP and Gross Domestic Income,”Survey of Current Business, Vol. 78 (December), pp. 1221.

      Johnson, A.G., 1982, “The Accuracy and Reliability of the Quarterly Australian National Accounts,” Australian Bureau of Statistics Occasional Paper No. 1982/2 (Canberra: Australian Bureau of Statistics).

      Kenny, P.B., and U.M.Rizki, 1992, “Testing for Bias in Initial Estimates of Key Economic Indicators,” Economic Trends, No. 463 (May), pp. 7786.

      Lal, K., 1998, “National Accounts Revision Practice: Canada,”paper presented at the Annual OECD Meeting of National Accounts Experts, Paris, December.

      Mork, K.A., 1987, “Ain’t behavin’: Forecast Errors and Measurement Errors in Early GNP Estimates,”Journal of Business & Economic Statistics, Vol. 5 (April), pp. 16575.

      Penneck, S., 1998, “National Accounts Revision Policy,”paper presented at the Annual OECD Meeting of National Accounts Experts, Paris, December.

      Penneck, S., 1998, “The UK Approach to Educating Users,”paper presented at the Annual OECD Meeting of National Accounts Experts, Paris, December.

      Rizki, U.M., 1996a, “Testing for Bias in Initial Estimates of Key Economic Indicators,” Economic Trends, No. 510 (April), pp. 2835.

      Rizki, U.M., 1996b, “Testing for Bias in Initial Estimates of the Components of GDP,” Economic Trends, No. 514 (August), pp. 7282.

      Seskin, E., and D.Sullivan, 2000, “Annual Revision of the National Income and Product Accounts,”Survey of Current Business, Vol. 80 (August), pp. 633.

      Smith, P., 1993, “The Timeliness of Quarterly Income and Expenditure Accounts: An International Comparison,” Australian Economic Indicators (September), pp. xixvi.

      Statistics Norway, 1998, “National Accounts Revision Policy in Norway,”paper presented at the Annual OECD Meeting of National Accounts Experts, Paris, December.

      U.S. Bureau of Economic Analysis, 1998, “U.S. National Income and Product Accounts: Release Schedule and Revision Practice,”paper presented at the Annual OECD Meeting of National Accounts Experts, Paris, December.

      Wroe, D., 1993, “Handling Revisions in the National Accounts,” Economic Trends, No. 480 (October), pp. 12123.

      York, R., and P.Atkinson, 1997, “The Reliability of Quarterly National Accounts in Seven Major Countries: A User’s Perspective,”OECD Economics Department Working PaperNo. 171 (Paris: OECD).

      Young, A.H., 1993, “Reliability and Accuracy of the Quarterly Estimates of GDP,”Survey of Current Business, Vol. 73 (October), pp. 2943.

    Index

    • Accounts for the total economy. See Unconsolidated accounts

    • Accrual accounting. See Time of recording

    • Adjustment process, 2.61

    • Administrative byproduct data, 3.15–3.16, 3.81

    • Aggregate tracking exercise, 2.47–2.50

    • Aggregation, defined, 9.6

    • Agriculture, work-in-progress issues, 10.3, 10.5, 10.38–10.50

    • Alternative extrapolation base. See Extrapolation bases

    • ANA. See Annual national accounts

    • Analytical testing

      • about, 5.14

      • edits of plausibility, 5.17–5.24

      • logical edits, 5.15–5.16

    • Annual national accounts (ANA)

    • conceptual links between QNA, 1.24–1.28

    • consistency with QNA, 1.24, 1.28

    • editing and reconciliation, 5.1–5.7

    • QNA and, 1.5–1.12

    • Annual reporting, fiscal- versus calendar-year basis, 2.38

    • Architectural and approval costs, 3.107

    • ARIMA-model-based method, 6.A1.39–6.A1.41

    • Balance of Payments Manual, 4.18

    • Balance sheets, 4.25–4.29

    • Balancing items, 4.6

    • Bassie method. 6.A1.17–6.A1.26

    • Benchmarking. See also Denton family of benchmarking methods

      • about, 1.24–1.27, 1.43, 2.7, 2.56–2.59, 6.1–6.11

      • additivity in, 9.43–9.45

      • alternative methods, 6.A1.1–6.A1.5

      • ARIMA-model-based method, 6.A1.39–6.A1.41

      • back series, 6.2

      • balancing items and accounting identities, 6.46–6.47

      • basic extrapolation with an indicator, 6.17–6.21

      • basic technique for distribution and extrapolation with an indicator, 6.12–6.21

      • Bassie method, 6. A1.17–6. A1.26

      • benchmark-lo-indicator ratio framework, 1.40, 6.2

      • BI ratio-based procedure, 6.39

      • BI ratio forecasts, 6.51

      • Chow-Lin method, 6.A1.48

      • and compilation procedures, 6.42–6.45

      • editing and reconciliation and, 5.36, 5.37

      • extrapolation, 6.2

      • extrapolation base and forward step problem, 6.A2.1–6.A2.18

      • fiscal- versus calendar-year reporting and, 2.38

      • fixed coefficient assumptions, 6.37–6.41

      • forward series, 6.2, 6.28–6.29. See also Proportional Denton technique with enhancements

      • general-least-squares regression models, 6. A 1.42–6. A 1.47

      • Ginsburgh-Nasse method, 6.A1.27–6.A1.38

      • IO ratios, 6.37, 6.38

      • more options, 6.48

      • other comments, 6.49–6.50

      • particular issues, 6.37–6.51

      • pro rata distribution and the step problem, 6.13–6.16

      • quarterizalion, 6.2

      • and revisions, 6.49–6.50

      • seasonal adjustment-based procedure, 6.39

      • sources and methods and, 1.18

    • Biases, 2.37, 2.39

      • extrapolation base and, 6.A2.2, 6.A2.10–6.A2.11

    • Boot-Feibes-Lisman distribution method, 7.16–7.18

    • Business accounting treatment of work-in-progress, 10.16–10.22

    • Calen dar-related effects, seasonal adjustment and trend-cycle estimates, 8.7, 8.26–8.30

    • Calendar year, annual reporting based on, 2.38

    • Capital accounts, 4.18, 4.35

    • Catastrophic events affecting agricultural production, 10.42–10.43

    • Chain-linking in the QNA

      • about, 9.21–9.31

      • additivity in, 9.42, 9.43–9.45

      • base period, 9.22, 9.25

      • chain-linked measures and nonadditivity, 9.42

      • choice of index number formulas for annual data, 9.36–9.38

      • Fisher index and, 9.22, 9.33, 9.37, 9.38

      • frequency of, 9.32–9.35

      • Laspeyres index and, 9.23–9.44

      • Paasche index and, 9.22, 9.32, 9.36, 9.37, 9.A1.1, 9.AL2–9.A1.4, 9.A2.2–9.A2.6, 9.A2.11

      • presentation of chain-linked measures, 9.46–9.53

      • reference period, 9.22, 9.26

      • techniques for annual chain-linking of quarterly data, 9.39–9.41

      • weight period, 9.22, 9.25

    • Changes in inventories

      • about inventories, 3.134–3.137

      • estimation of, 3.A1.1–3.A1.12

      • perpetual inventory method, 3.138

      • price indicators, 3.144

      • valuation problems, 3.69

      • value indicators, 3.138–3.142

      • volume indicators, 3.143

    • Characteristics of series, calculations and, 2.56

    • Chow-Lin method, 6.A 1.48

    • Commodity flow method, 2.23

    • Compensation of employees, value indicators for, 3.161–3.163

    • Compilation and release schedule, in revision policy, 11.11–11.21

    • Compilation system, 2, 5

      • assessing, 2.31–2.34, 2.47–2.50

      • choice between, 2.6

    • Compiling QNA

      • additivity in, 9.43–9.45

      • compilation cycle, 1.47

      • editing as part of, 5.39–5.47

      • from unadjusted source data, 1.17–1.22

    • Components of the 1993 SNA other than GDP

      • accounts for the total economy (unconsolidated accounts), 4.7—4.29

      • general issues, 4.1–4.5

      • institutional sector accounts, 4.30–4.49

      • main aggregates for the total economy, 4.6

    • Comprehensive quarterly statistical publications, 2.66

    • Computer software

      • database software, 2.92–2.97

      • in fixed capital formation, 3.131

      • for seasonal adjustment and trend-cycle estimates, 8.13

    • COMWIL valuation (cost or market, whichever is less), 3.A 1.4

    • Confrontation of data. See Editing and reconciliation

    • Constant price estimates. See Price and volume measurement

    • Construction industry

      • price indicators, 3.116–3.122

      • value indicators, 3.32, 3.101–3.112

      • volume indicators, 3.113–3.115

      • work-in-progress activities, 10.3

    • Consumer price index (CPI), 3.54, 3.83–3.86

    • Consumption of dwelling services data, 3.79

    • Consumption of fixed capital, 4.5, 4.26, 10.25, 10.A1.3

    • Cost or market, whichever is less (COMWIL valuation), 3.A1.4

    • Coverage of QNA

      • about, 2.8–2.14

    • GDP measurement, 2.15–2.23

      • supply and use approach to GDP, 2.24–2.29

    • CPI. See Consumer price index

    • Customs data, 3.148

    • Data, reviewing. See Tracking exercises, and Editing and reconciliation

    • Data problems

      • causes of, 5.8–5.9

      • identifying, 5.10–5.24

    • Data sources, 1.41, 3.4–3.10. See also Source data and specific sources

      • filling gaps in, 3.17–3.19

    • Data suppliers, contact with, 5.3

    • Data validation. See Editing and reconciliation

    • Denton family of benchmarking methods, 6.22–6.36, 6.A1.6–6.A1.16. See also Proportional Denton method;

      • Proportional Denton technique with enhancements

    • Differences between QNA and source data statistics, causes for, 2.60–2.61

    • Disposable income account, 4.17

    • Dissemination

      • revised data, 11.23–11.26

    • Dissemination issues, 2.62–2.67

    • Dividends, 4.46

    • Documentation, source data, 1.29, 1.33

    • Editing and reconciliation

      • about, 5.1–5.7

      • adjusted data, 5.23

      • benchmarking and, 5.36, 5.37

      • at both detailed and aggregate levels, 5.22

      • causes of data problems, 5.8–5.9

      • changes in estimates, 5.7

      • concerns in showing explicit discrepancies, 5.35

      • deadlines and, 5.4

      • dealing with inconsistencies, 5.25–5.38

      • discrepancies and residual items, 5.24

      • documentation and, 5.7

      • editing as part of compilation, 5.39–5.47

      • errors and mistakes, 5.2, 5.4–5.9

      • explicit discrepancies, 5.34–5.35

      • identifying data problems, 5.10–5.24

      • independent estimates of GDP and, 5.27

      • making adjustments, 5.30

      • other alternatives for treating discrepancies, 5.31–5.34

      • procedural and practical differences in reconciliation, 5.36

      • reconciliation process, 5.25–5.38

      • relationships within data and, 5.6

      • statistical noise and, 5.37

      • supply and use balancing, 5.26. 5.28, 5.30

      • timing errors and, 5.37

    • Edits of plausibility, 5.17–5.24

    • Educating and informing users, 1.34–1.36

    • Equipment

      • price indicators, 3.128–3.130

      • value indicators, 3.123–3.126

      • volume indicators, 3.127

    • Equipment capacity, 2.13

    • Errors and mistakes. See Editing and reconciliation

    • Establishing phase, 2.2, 2.52

    • Expenditure approach to GDP measurement, 2.15, 2.16, 2.18–2.20

    • Exports and imports of goods and services

      • merchandise price indicators, 3.148–3.155

      • price indicators, 3.148–3.155

      • service price indicators, 3.156

      • value indicators, 3.145

      • volume indicators, 3.146–3.147

    • Extended accounting framework, 2.11

    • Extrapolation, 1.26, 6.2

      • with an indicator, 6.17–6.21

      • basic technique for distribution and extrapolation with an indicator, 6.12–6.21

    • Extrapolation bases

      • about, 6.A2.1–6.A2.5

      • alternative extrapolation bases, 6.A2.3–6.A2.5

      • annual rate of change in derived forward series, 6.A2.8–6.A2.15

      • biases, 6.A2.2, 6.A2.10–6.A2.11

      • forward step problem and. 6.A2.6—6.A2.7

      • and robustness toward errors in indicator, 6.A2.16

      • and seasonality, 6.A2.17–6.A2.18

    • Eyeball testing, 5.11–5.13

    • FIFO (first in, first out), 3.A1.2, 3.A1.3

    • Final consumption expenditure by nonprofit institutions serving households

      • price indicators, 3.97

      • value indicators, 3.95

      • volume indicators, 3.96

    • Financial accounts, 4.19—4.24, 4.36

    • Financial intermediation services indirectly measured (FISIM), 3.59, 3.65, 3.68

    • Fiscal year, annual reporting based on, 2.38

    • Fisher-type volume indices, 9.18–9.20

    • FISIM. See Financial intermediation services indirectly measured

    • Fixed coefficients, 3.24

    • Flash estimates, 1.37–1.38

    • GDP by income category

      • employee compensation, 3.161–3.163

      • general issues, 3.157–3.160

      • operating surplus/mixed income, 3.164–3.167

      • taxes and subsidies on products, production, and imports, 3.168

      • value indicators, 3.161–3.168

      • volume and price indicators, 3.169–3.170

    • GDP by industry

      • adjustment items, 3.65–3.68

      • current price data on outputs and/or inputs, 3.29–3.36

      • data on quantities of output and/or inputs, 3.37–3.42

      • general issues, 3.20–3.27

      • indirect indicators, 3.48–3.52

      • industrial production indices, 3.62–3.64

      • labor input measures, 3.43–3.47

      • price indicators, 3.53–3.61

      • types of source data, 3.28

    • GDP by type of expenditure

      • changes in inventories, 3.69, 3.134–3.144, 3.A1.1–3.A1.12

      • exports and imports of goods and services, 3.145–3.156

      • final consumption expenditure by nonprofit institutions serving households, 3.95–3.97

      • general issues, 3.69–3.70

      • government final consumption expenditure, 3.87–3.94

      • gross fixed capital formation, 3.98–3.113

      • household final consumption expenditure, 3.71–3.86

    • GDP measurement, 2.15–2.23

    • GDP sources

      • in absence of surveys or administrative data, 3.17–3.19

      • administrative byproduct data issues, 3.15–3.16, 3.81

      • data sources, 3.4–3.10

      • general issues, 3.1–3.3

      • survey issues, 3.11–3.14

    • General-least-squares regression models, 6. A1.42–6.A1.47

    • GFS. See Government finance statistics system

    • Ginsburgh-Nasse method, 6.A1.27–6.A1.38

    • GNI (gross national income), 4.14

    • Goods and services

      • current price data on outputs/inputs, 3.31

      • exports and imports of, 3.145–3.156

      • volume indicators, 3.79

    • Goods and services tax, 3.16. See also Value added tax (VAT) systems

    • Government accounting systems, 4.38–4.40

    • Government final consumption expenditure

      • price indicators, 3.93–3.94

      • value indicators, 3.87–3.90

      • volume indicators, 3.91–3.92

    • Government finance statistics (GFS) system, 3.168

    • Government Finance Statistics Manual, 4.38

    • Gross domestic product (GDP), See under GDP

    • Gross fixed capital formation

      • construction industry, 3.101–3.122

      • equipment, 3.123–3.130

      • general value indicators, 3.98–3.100

      • other fixed capital formation and acquisition less disposables of valuables, 3.131–3.133

      • specific value indicators. 3.101–3.133

    • Gross national income (GNI), 4.14

    • Gross operating surplus, indicators for 3.164—3.167

    • Holding gains and losses, work-in-progress considerations, 10.7, 10.17, 10.24

    • Household final consumption expenditure price indicators, 3.83–3.86

      • value indicators, 3.71–3.78

      • volume indicators, 3.79–3.82

    • Human resources capacity, 2.13

    • Improvements to QNA source data, 2.44—2.46

    • Income accounts

      • about, 4.9–4.12

      • allocation of primary income account, 4.14—4.15

      • generation of, 4.13

      • secondary distribution of income account, 4.16

      • timing issues, 4.10–4.11

      • use of disposable income account, 4.17

    • Income approach to GDP measurement, 2.15, 2.21–2.22

    • Inconsistencies among data. See Editing and reconciliation

    • Indirect indicators, 3.48–3.52

    • Industrial production indices (IPI), GDP by industry, 3.62–3.64

    • Industry approach. See Production approach to GDP measurement

    • Inputs/outputs. See also IO coefficients

      • current price data on outputs and/or inputs, 3.29–3.36

      • data on quantities of output and/or inputs. 3.37–3.42

      • labor input measures, 3.43–3.47

    • Institutional sector accounts

      • about, 4.30–4.37

      • financial corporations, 4.41

      • general government, 4.38—4.40

      • households, 4.42–4.44

      • nonfinancial corporations, 4.47–448

      • rest of the world, 4.49

    • Insurance premiums, 4.44

    • Intangible assets, in fixed capital formation, 3.131

    • Integrated compilation systems, 2.5

    • Interest, 4.43, 4.44

    • Intermediate consumption, deriving and presenting, 3.25, 3.26

    • International investment position, 4.49

    • Inventories, 3.134–3.135. See also Changes in inventories

    • Inventory valuation adjustment (IVA), 3.A1.1 1

    • IO coefficients, relationship between, 3.24

    • IO ratios, benchmarking and. 6.37. 6.38

    • IPI (industrial production indices), 3.62–3.64

    • Irregular changes (statistical noise), 2.39

      • editing and reconciliation and, 5.37

    • IVA (inventory valuation adjustment), 3.A1.1

    • Labor input measures, 3.43–3.47, 3.96

    • Laspeyres-type volume measures

      • about, 9.15–9.17, 9.A1.1

      • aggregation over time and consistency between annual and quarterly estimates, 9.A1.1–9.A1.10

      • annual average prices as price base, 9.A1.6–9.A1.10

      • relationship between quarterly and annual deflators, 9.A1.2–9.A1.5

    • Least-squares-distribution technique, 7.16–7.8

    • LIFO (last in, first out), 3.A1.2, 3.A1.3

    • Lisman and Sandee quarterly distribution formula, 7.14–7.15

    • Loans and deposits, price indicator, 3.59

    • Logical edits, 5.15–5.16

    • Long production cycles, 1.28

    • Low-frequency payments, 1.28, 4.10, 4.11

    • Maintaining QNA. See Operational phase

    • Managerial issues

      • about, 2.68–2.73

      • database software, 2.92–2.97

      • managing data compilation systems, 2.92–2.97

      • methods of speeding compilation, 2.78–2.81

      • organizing data supply, 2.89–2.91

      • organizing staff, 2.68, 2.83–2.88

      • planning workloads, 2.75–2.77

      • spread sheet-based systems, 2.92–2.97

      • structuring the compilation process, 2.74

      • timing compilation process, 2.74–2.82

    • Manual on Government Finance Statistics, 3.168

    • Mechanical trend projections

      • about, 7.1–7.6

      • based on annual data, 7.7–7.18

      • based on monthly or quarterly data, 7.19–7.22

    • Boot-Feibes-Lisman distribution method, 7.16–7.18

      • leasl-squarcs-distribution technique, 7.16–7.18

    • Lisman and Sandee quarterly distribution formula, 7.14–7.15

    • Merchandise, price indicators, 3.148–3.155

    • Mixed income, indicators for, 3.164–3.167

    • Monthly GDP, Ch. I footnote 1

    • Net taxes, 3.66–3.67

    • 1993 SNA. See System of National Accounts 1993

    • Noise, See Irregular changes (statistical noise)

    • Nonprofit institutions serving households (NPISHs), 3.95–3.97.4.47–4.48

    • Operating surplus/mixed income, value indicator, 3.164–3.167

    • Operational phase, 2.2, 2.53–2.55

    • Output. See also IO coefficients

      • allocating work-in-progress output to periods, 10.26

      • current price data on outputs and/or inputs, 3.29–3.36

      • data on quantities of output and/or inputs, 3.37–3.42

      • deriving and presenting, 3.25, 3.27

      • estimating work-in-progress output based on cost plus estimate of markup from other source, 10.33

      • work-in-progress treated as output, 10.4, 10.8–10.12

    • Pensions and annuities, 4.43

    • Perpetual inventory method, 3.138

    • Population, as indicator, 3.49

    • PPI (producer price index), 3.54

    • Presentation of data, 2.62. 2.66

      • revisions, 11.24

    • Press releases, 2.65

    • Price and volume measurement

      • about, 9.1–9.5

      • aggregating over time, 9.6–9.14, 9.A1.1–9.A1.10

      • annual overlap technique, 9.A2.1–9.A2.9.A2.6

      • chain-linking, 9.21–9.53, 9.A2.1–9.A2.11

      • choice of price weights for volume measures, 9.15–9.20, 9. Al. 1–9.A 1.10

      • consistency between annual and quarterly estimates, 9.A1.1–9.A1.10

      • Fisher-type volume indices, 9.18–9.20

      • four requirements to constitute time series, 9.3

      • Laspeyres-type volume measures, 9.15–9.17

      • one-quarter overlap technique, 9.A2.7–9.A2.9.A2.11

    • Price indicators

      • changes in inventories, 3.144

      • construction industry, 3.116–3.122

      • equipment, 3.128–3.130

      • exports and imports of goods and services, 3.148–3.155, 3.156

      • final consumption expenditure by nonprofit institutions serving households, 3.97

      • GDP by income category, 3.169–3.170

      • GDP by industry, 3.53–3.61

      • government final consumption expenditure, 3.93–3.94

      • household final consumption expenditure, 3.83–3.86

      • merchandise, 3.148–3.155

      • services, 3.156

      • specific-purpose price indices, 3.55

    • Producer price index (PPl), 3.54

    • Production account, 4.8. See also Work-in-progress

    • Production approach to GDP measurement, 2.15, 2.16, 2.17, 3.20–3.23, 3.28

    • Proportional Denton method (D4 formula), 6.7–6.8, 6.22–6.36, 6.48, 6.50, 6.A1.7, 6.A1.11, 6.A1.13–6.A 1.16, 6.A3.1–6.A3.3

    • Proportional Denton technique with enhancements, 1.27, 2.57, 2.59, 6.8, 6.31–6.36, 6.A1.2–6.A1.5, 6.A1.18, 6.A1.40, 6.A1.43, 6.A1.49

    • Publication policy, 1.33

    • QNA. See Quarterly national accounts

    • Quantity measures, 3.38–3.41

    • Quarterization, 1.26

    • Quarterly National Accounts Manual, 1.2–1.3, 1.39–1.47

    • Quarterly national accounts (QNA)

      • about, 1.1–1.4

      • ANA and, 1.5–1.12

      • availability, 1.7

      • business cycle analysis and, 1.7–1.9, 1.12, 1.14

      • conceptual links between ANA, 1.24—1.28

      • consistency with ANA, 1.24, 1.28

      • critique of use for business cycle analysis, 1.12

      • high inflation and, 1.10

      • purposes of, 1.5–1.12

      • short-term indicators and, 1.11

      • as time series, 1.13–1.15

      • transparency in, 1.29–1.36

    • Real estate transfer costs, 3.108

    • Reconciliation. See Editing and reconciliation

    • Regulation, information gathered in process of, 3.15–3.16, 3.80, 3.81

    • Release cycle, 2.73

    • Release of data, 2.62–2.67

      • revisions, 11.23–11.26

    • Rest of the world, institutional sector accounts, 4.50

    • Retail trade, sales data, 3.33, 3.34

    • Revision policy

      • about, 1.33, 1.47, 11.1–11.4

      • communication with users and, 11.23–11.25

      • frequency of data incorporation in, 11.16, 11.18–11.20

      • other important elements of, 11.22

      • providing revised time series, 11.26

      • timeliness in, 11.14, 11.15, 11.17

      • user requirements and resource constraints, 11.1, 11.5–11.6

      • waves of source data and related revision cycles, 11.7–11.10

    • Revisions of preliminary data, 1.30–1.33

    • Road freight transport, indicators, 3.49

    • Seasonal adjustment and trend-cycle estimates

      • about, 1.16, 1.44, 8.1–8.6

      • additive model, 8.8, 8.9

      • additivity in, 9.43–9.45

      • business cycle changes and, 1.19

      • BV4 program, 8.13

      • calendar-related systematic effects, 8.7, 8.26–8.30

      • differing opinions on, 1.17

      • impact of irregular events and, 1.23

      • irregular component, 8.7

      • irregular effects narrowly defined, 8.7

      • moving holidays, 8.2, 8.7, 8.26, 8.28, 8.29

      • multiplicative model, 8.8, 8.9

      • other calendar effects, 8.7

      • other irregular effects, 8.7

      • outlier effects, 8.7

      • seasonal adjustment principles, 8.7–8.16

      • seasonal component, 8.7

      • software, 8.13

      • status and presentation of estimates, 8.62–8.69

      • trading-day effect, 8.2, 8.7, 8.26–8.30

      • TRAMO-SEATS software, 8.13

      • trend-cycle estimates, 8.3

      • lrend-cycle component, 8.7

      • unadjusted data and, 1.18–1.22, 5.23

      • X-ll family of programs, 8.13, 8.17–8.33

    • Seasonality issues

      • changes in seasonal patterns, 8.34–8.43

      • compilation levels and adjustment of aggregates, 8.49–8.52, 8.54, 8.55, 8.56

      • compilation levels and adjustment of balancing items, 8.49, 8.53

      • consistency with annual accounts, 8.59–8.61

      • direct and indirect approach to estimates, 8.49–8.56

      • extrapolation base and, 6.A2.17–6.A2.18

      • four critical issues in, 8.48–8.61

      • minimum length of time series for adjustment, 8.44–8.47

      • relationship among price, volume, and value, 8.57

      • revisions, 8.35–8.43

      • seasonal filters, 8.36, 8.37

      • status and presentation of estimates, 8.62–8.69

      • supply and use and other accounting identities, 8.58

      • wagging tail problem, 8.35–8.43

    • Separate compilation systems, 2.5

    • Sequence of accounts, 4.4–4.5

    • Services. See also Goods and services

      • price indicators, 3.156

      • work-in-progress activities, 10.3

    • Setting up new QNA systems, 1.40

    • Social contributions, 4.43, 4.44

    • Source data. See also Data sources and specific sources

      • assessing, 2.31–2.47

      • future extension of QNA and, 2.10

      • inventory of, 2.9

    • Source statistics, discussions with compilers about differences, 2.61

    • Statistical issues

      • assessing compilation system, 2.31–2.34, 2.47–2.50

      • assessing source data, 2.31–2.47

      • compilation level, 2.30

      • coverage of QNA, 2.8–2.29

      • link between QNA and ANA, 2.4–2.7

      • relationship between QNA and source data statistics, 2.60–2.61

      • statistical processing, 2.51–2.59

    • Statistical noise. See Irregular changes

    • Step problem, 1.27, 1.43.2.56.6.9, 6.13–6.16

      • Bassie method and, 6.A1.17–6.A1.26

      • forward and back step problems, 6.A2.1–6.A2.2, 6.A2.6–6.A2.7

    • Strategies for QNA systems, 1.40, 2.1–2.3. See also Dissemination issues; Managerial issues; Statistical issues

    • Subsidies on products, production, and imports, value indicator, 3.168

    • Subsistence production of food, volume indicator, 3.82

    • Supply and use approach to GDP, 2.24–2.29

    • Survey issues, 3.11–3.14

    • Taxation, information gathered in process of, 3.15–3.16

    • Taxes

      • households and, 4.43, 4.44

      • nonfinancial corporations, 4.46

      • on products, production, and imports as value indicator, 3.168

    • Time of recording, 1.28, 4.10, 4.11

    • Time series, 1.13–1.15, 8.1

    • Timeliness of source data. 2.40

    • Timing errors, editing and reconciliation and, 5.37

    • Tracking exercises, 2.34

      • aggregate tracking exercise, 2.47–2.50

    • Trade data, 3.148

    • Transparency in quarterly national accounting, 1.29–1.36, 1.47

    • Trend cycle estimates. See Seasonal adjustment and trend-cycle estimates

    • Trustworthiness of data, revisions and, 1.31

    • Turning points, identification of, 1.A1.1–1.A1.9

    • Unadjusted source data, 1.18, 1.20, 1.21

    • Unconsolidated accounts (accounts for the total economy)

      • about, 4.7

      • balance sheets, 4.25—1.29

      • capital account, 4.18

      • financial accounts, 4.19–4.24

      • income accounts, 4.9–4.17

      • production account, 4.8

    • Users

      • and revisions to data, 1.30–1.31, 11.1–11.3, 11.5

      • consulting with, 2.2, 2.8, 2.13

      • educating and informing, 1.34–1.36, 2.31, 2.33, 2.35, 2.48

      • guiding future extension of QNA, 2.10–2.11

      • opinions on, and need for, seasonally adjusted and trend-cycle estimates, 1.17—1.21 revision policy communication, 11.22–11.24

      • transparency requirement of, 1.29–1.36

    • Valuables, in fixed capital formation, 3.131

    • Value added, deriving and presenting, 3.25, 3.27

    • Value added tax (VAT) systems, 3.16, 3.36, 3.71, 3.73, 3.99

    • Value data, 3.32, 3.35, 3.36

    • Value indicators

      • changes in inventories, 3.138–3.142

      • construction industry, 3.32, 3.101–3.112

      • equipment, 3.123–3.126

      • exports and imports of goods and services, 3.145

      • final consumption expenditure by nonprofit institutions serving households, 3.95

      • GDP by income category, 3.161–3.168

      • government final consumption expenditure, 3.87–3.90

      • gross fixed capital formation, 3.98–3.133

      • household final consumption expenditure, 3.71–3.78

    • Variables, multiple sources for same, 2.42

    • VAT. See Value added tax systems

    • Volume indicators

      • changes in inventories, 3.143

      • construction industry, 3.113–3.115

      • equipment, 3.127

      • exports and imports of goods and services, 3.146–3.147

      • final consumption expenditure by nonprofit institutions serving households, 3.96

      • GDP by income category, 3.169–3.170

      • government final consumption expenditure, 3.91–3.92

      • household final consumption expenditure, 3.79–3.82

    • Volume measures. See also Price and volume measurement

      • distinguished from quantity measures, 3.38

    • WAC (weighted average cost), 3.A1.2

    • Wagging tail problem, 1.20, 6.A1.37–6.A1.38, 6.A2.2, 6.A2.10–6.A2.11, 6.A2.15, 8.36–8.44, 8.39

    • Weighted average cost, 3.A1.2

    • Wholesale and/or retail industries, indicators, 3.49, 3.58

    • Wholesale price index (WP1), 3.54

    • Work-in-progress

      • about, 1.46, 10.1–10.7

      • allocating output to periods, 10.26

      • alternatives for products with long production cycles, 10.18

      • business accounting treatment of, 10.16–10.22

      • contract work, 10.19

      • cost/production profiles, 10.35–10.37

      • delay in recognition of profits and, 10.17

      • economic concepts in measurement, 10.13–10.15

      • effects on main aggregates, 10.A 1. 1—10.A1.7

      • estimating output based on cost plus estimate of markup from other source, 10.33

      • example of ex post situation, 10.27–10.28

      • examples of, 10.3

      • forecasts and, 10.32

      • holding gains and losses considerations, 10.7, 10.17, 10.24

      • input costs, 10.25

      • measurement, 10.13–10.37

      • permutations arising from different data situations, 10.29–10.32

      • recording in 1993 SNA sequence of accounts, 10.A1.1–1.0.A1.7

      • special issues for agriculture, 10.3, 10.5, 10.38–10.50

      • speculative work, 10.20

      • treated as output, 10.4, 10.8–10.12

      • work for own final use, 10.19

    • WPI (wholesale price index), 3.54

    • X-11 family of seasonal adjustment programs

      • basic features, 8.17–8.20

      • calendar-related effects, 8.26–8.30

      • data adjusted for only some seasonal effects, 8.30

      • estimation of other parts of seasonal component in working/trading days, 8.26–8.30

      • holiday adjustment procedures, 8.26, 8.28, 8.29

      • moving average filtering procedure, 8.21–8.24, 8.26

      • multiplicative version of filtering, 8.23

      • preadjustments, 8.25

      • seasonal adjustment diagnostics using, 8.31–8.33

      • X-11-ARIMA, 8.13, 8.17, 8.54, 8.60, 8.61

      • X-12-ARIMA, 8.13, 8.17, 8.54, 8.60, 8.61

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