SERIEs

, Volume 3, Issue 3, pp 311–338 | Cite as

A factor analysis for the Spanish economy

Open Access
Original Article

Abstract

We present a medium-scale dynamic factor model to estimate and forecast the rate of growth of the Spanish economy in the very short term. The intermediate size of the model overcomes the serious specification problems associated with large-scale models and the implicit loss of information of small-scale models. The estimated common factor is used to forecast the gross domestic product by means of a transfer function model. Likewise, the model solves the operational and informational limits posed by the presence of an unbalanced panel of indicators and generates multivariate forecasts of the basic indicators.

Keywords

Dynamic factor model Short-term economic analysis Spanish economy Kalman filter Transfer function Temporal disaggregation Forecasting Nowcasting 

JEL Classification

C22 C53 C82 E27 E32 

References

  1. Abad A, Cuevas A, Quilis EM (2009) ‘Índices trimestrales de volumen encadenados, ajuste estacional y benchmarking. Instituto de Estudios Fiscales, Papeles de Trabajo n. 05.09Google Scholar
  2. Álvarez F (1989) Base estadística en España de la Contabilidad Nacional Trimestral. Revista Española de Economía 6(1–2): 59–84Google Scholar
  3. Álvarez R (2005) Notas sobre fuentes estadísticas, in Servicio de Estudios del Banco de España, El análisis de la economía española, Alianza EditorialGoogle Scholar
  4. Ang A, Piazzesi M (2003) A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. J Monet Econ 50: 745–787CrossRefGoogle Scholar
  5. Angelini E, Camba-Méndez G, Giannone D, Reichlin L, Runstler, G (2008) Short-term forecasts of Euro area GDP growth. CEPR Discussion Paper n. 6746Google Scholar
  6. Aruoba B, Diebold F, Scotti C (2009) Real-time measurement of business conditions. J Bus Econ Stat (forthcoming)Google Scholar
  7. Bai J, Ng S (2008) Large dimensional factor analysis. Found Trends Econ 3(2): 89–163CrossRefGoogle Scholar
  8. Bechikh Y (1998) On deformation of the yield curve. BNP-Paribas. Economic Notes, n. 1998-5Google Scholar
  9. Beguin JM, Gourieroux C, Monfort A (1980) Identification of a mixed autoregressive-moving average process: the corner method. In: Anderson OD (ed) Time series analysis. North-Holland, NYGoogle Scholar
  10. Bernanke B, Boivin J, Eliasz P (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive approach (FAVAR). Q J Econ 120(1): 387–422Google Scholar
  11. Boivin J, Giannoni M (2006) DSGE models in a data-rich environment. National Bureau of Economic Research Technical Working Paper n. 332Google Scholar
  12. Boot JCG, Feibes W, Lisman JHC (1967) Further methods of derivation of quarterly figures from annual data. Appl Stat 16(1): 65–75CrossRefGoogle Scholar
  13. Box GEP, Jenkins GM (1976) Time series analysis, forecasting and control. Holden Day, San FranciscoGoogle Scholar
  14. Box GEP, Tiao GC (1977) A canonical analysis of multiple time series. Biometrika 64(2): 355–365CrossRefGoogle Scholar
  15. Burns A, Mitchell WC (1946) Measuring business cycles. National Bureau of Economic ResearchGoogle Scholar
  16. Camacho M, Pérez-Quirós G (2009a) Introducing the Euro-STING: Short Term Indicator of Euro Area Growth. J Appl Econ (forthcoming)Google Scholar
  17. Camacho M, Pérez-Quirós G (2009b) Spain-STING: Spanish Short Term Indicator of Growth. The Manchester School (forthcoming)Google Scholar
  18. Camacho M, Doménech R (2010) MICA-BBVA: a factor model of economic and financial indicators for short-term GDP forecasting, BBVA Research Department, Working Paper 10/21Google Scholar
  19. Camacho M, Pérez-Quirós G, Saiz, L (2005) Do European business cycles look like one? Bank of Spain, Working Paper n. 0518Google Scholar
  20. Camacho M, Pérez-Quirós G, Poncela P (2010) Green shoots in the Euro area. A real-time measure. Bank of Spain, Working Paper n. 1026Google Scholar
  21. Cancelo JR (2005) Análisis empírico del ciclo económico con un modelo factorial dinámico con cambio de régimen. Estadística Española 47(159): 253–277Google Scholar
  22. Caporello G, Maravall A (2004) Program TSW. Revised manual. Bank of Spain, Working Paper n. 0408Google Scholar
  23. Chamberlain G (1983) Funds, factors, and diversification in arbitrage pricing models. Econometrica 51(5): 1305–1324CrossRefGoogle Scholar
  24. Chamberlain G, Rothschild M (1983) Arbitrage, factor structure, and mean-variance analysis on large asset markets. Econometrica 51(5): 1281–1304CrossRefGoogle Scholar
  25. Chauvet M (1998) An econometric characterization of business cycle dynamics with factor structure and regime switches. Int Econ Rev 39(4): 969–996CrossRefGoogle Scholar
  26. Christensen JHE, López JA, Rudebusch GD (2009) Do Central Bank liquidity facilities affect interbank lending rates? Federal Reserve Bank of San Francisco, Working Paper 2009-13Google Scholar
  27. Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13(3): 134–144Google Scholar
  28. Doz C, Giannone D, Reichlin L (2006) A two-step estimator for large approximate dynamic factor models based on Kalman Filtering. Universite Libre de Bruxelles, mimeoGoogle Scholar
  29. Escribano A, Peña D (1994) Cointegration and common factors. J Time Ser Anal 15(5): 577–586CrossRefGoogle Scholar
  30. Espasa A, Cancelo JR (1993) Métodos cuantitativos para el análisis de la coyuntura económica, Alianza EditorialGoogle Scholar
  31. Evans ChL, Liu CT, Pham-Kanter G (2002) The 2001 recession and the CFNAI: identifying business cycle turning points. Federal Reserve Bank of Chicago. Econ Perspect 26(3): 26–43Google Scholar
  32. Forni M, Hallin M, Lippi M, Reichlin L (2000) The generalized dynamic factor model: identification and estimation. Rev Econ Stat 82(4): 540–554CrossRefGoogle Scholar
  33. Forni M, Hallin M, Lippi M, Reichlin L (2005) The generalized dynamic factor model: one-sided estimation and forecasting. J Am Stat Assoc 100: 830–839CrossRefGoogle Scholar
  34. Forni M, Gambetti L (2010) Macroeconomic Shocks and the Business Cycle: Evidence from a Structural Factor Model. CEPR Discussion Paper Series n. 7692Google Scholar
  35. Galeano P, Peña D (2000) Multivariate analysis in vector time series. Resenhas 4: 383–404Google Scholar
  36. Geweke J (1977) The dynamic factor analysis of economic time series models. In: Aigner DJ, Goldberger AS (eds) Latent variables in socio-economic models. North-Holland, NYGoogle Scholar
  37. Geweke J, Singleton K (1981) Maximum likelihood ‘confirmatory’ analysis of economic time series. Int Econ Rev 22(1): 37–52CrossRefGoogle Scholar
  38. Giannone D, Reichlin L, Small D (2008) Nowcasting: the real-time informational content of macroeconomic data. J Monet Econ 55: 665–676CrossRefGoogle Scholar
  39. Gómez V, Maravall A (1996) Programs TRAMO and SEATS. Bank of Spain, Working Paper n. 9628Google Scholar
  40. Hanssens DM, Liu LM (1983) Lag especification in rational distributed lag structural models. J Bus Econ Stat 1(4): 316–325Google Scholar
  41. INE (1993) Contabilidad Nacional Trimestral de España (CNTR). Metodología y serie trimestral 1970–1992. Instituto Nacional de EstadísticaGoogle Scholar
  42. INE (1994) Sistema de Indicadores Cíclicos de la economía española (SIC), Instituto Nacional de EstadísticaGoogle Scholar
  43. INE (2002) Ajuste estacional y extracción de señales en la Contabilidad Nacional Trimestral. Bank of Spain, Working Paper n. 0210Google Scholar
  44. Kim C-J, Nelson CR (1999) State-space models with regime switching. MIT Press, CambridgeGoogle Scholar
  45. Knez J, Litterman RB, Scheinkman J (1994) Explorations into factors explaining money market returns. J Finance 49(5): 861–1882CrossRefGoogle Scholar
  46. Künsch HR (1989) The jacknife and the bootstrap for general stationary observations. Ann Stat 17(3): 1217–1241CrossRefGoogle Scholar
  47. Litterman R, Scheinkman J (1988) Common factors affecting bond returns. Goldman, Sachs & Co., Financial Strategies Group, Technical Report n. 62Google Scholar
  48. Liu LM, Hanssens DM (1982) Identification of multiple-input transfer function models. Commun Stat Theory Methods 11(3): 297–314CrossRefGoogle Scholar
  49. Liu LM (2005) Time series analysis and forecasting. Scientific Computing Associates (SCA) CorpGoogle Scholar
  50. Lütkepohl H (1991) Introduction to multiple time series analysis. Springer, BerlinCrossRefGoogle Scholar
  51. Maravall A (1993) Stochastic linear trends. Models and estimators. J Econ 56: 5–37Google Scholar
  52. Mardia KV, Kent JT, Bibby JM (1979) Multivariate analysis. Chapman and Hall,Google Scholar
  53. Mariano R, Murasawa Y (2003) A new coincident index of business cycles based on monthly and quarterly series. J Appl Econ 18: 427–443CrossRefGoogle Scholar
  54. Martínez A, Melis F (1989) La demanda y la oferta de estadísticas coyunturales. Revista Española de Economía 6(1–2): 7–58Google Scholar
  55. Melis F (1991) La estimación del ritmo de variación en series económicas. Estadística Española 126: 7–56Google Scholar
  56. O’Connel PE (1984) Kalman filtering. In: Lederman LV (ed) Handbook of applicable mathematics, vol VI. Wiley, New YorkGoogle Scholar
  57. Peña D, Box GEP (1987) Identifying a simplifying structure in time series. J Am Stat Assoc 82(399): 836–842Google Scholar
  58. Peña D, Poncela P (2004) Forecasting with non-stationary dynamic factor models. J Econ 119: 291–321CrossRefGoogle Scholar
  59. Peña D, Poncela P (2006a) Non-stationary dynamic factor analysis. J Stat Plan Inference 136: 1237–1257CrossRefGoogle Scholar
  60. Peña D, Poncela P (2006b) Dimension reduction in multivariate time series. In: Balakrishnan N, Castillo E, Sarabia JM (eds) Advances in distribution theory. Birkhauser, BaselGoogle Scholar
  61. Politis DN, Romano JP (1994) The stationary bootstrap. J Am Stat Assoc 89(428): 1303–1313CrossRefGoogle Scholar
  62. Priestley MB, Subba-Rao T, Tong H (1974) Applications of principal components and factor analysis in the indentification of multivariable systems. IEEE Trans Autom Control 19(6): 730–734CrossRefGoogle Scholar
  63. Reimers M., Zerbs M (1999) A multifactor statistical model for interest rates. Algo Res Q 2(3): 53–63Google Scholar
  64. Reinsel G (1993) Elements of multivariate time series analysis. Springer, BerlinCrossRefGoogle Scholar
  65. Sargent ThJ, Sims ChA (1977) Business cycle modelling without pretending to have too much a priori economic theory. In: Sims ChA (ed) New methods in business cycle research. Federal Reserve Bank of MinneapolisGoogle Scholar
  66. Singleton K (1983) Real and nominal factors in the cyclical behavior of interest rates, output, and money. J Econ Dyn Control (5):289–309Google Scholar
  67. Stock JH, Watson MW (1988) Testing for common trends. J Am Stat Assoc 83(404): 1097–1107CrossRefGoogle Scholar
  68. Stock JH, Watson MW (1991) A probability model of the coincident economic indicators. In: Lahiri K, Moore GH (eds) Leading economic indicators: new approaches and forecasting records. Cambridge University Press, CambridgeGoogle Scholar
  69. Stock JH, Watson MW (2002) Macroeconomic forecasting using diffusion indexes. J Bus Econ Stat 20(2): 147–162CrossRefGoogle Scholar
  70. Stock JH, Watson MW (2005) Implications of dynamic factor models for VAR analysis. In: Prepared for the conference “Macroeconomics and Reality, 25 Years Later”. Bank of Spain-CREI, Barcelona, April 7–8Google Scholar
  71. Stock JH, Watson MW (2010) “Dynamic factor models”. In: Clements P, Hendry DF (eds) Oxford handbook of economic forecasting. Oxford University Press, OxfordGoogle Scholar
  72. Tiao GC, Box GEP (1981) Modeling multiple time series with applications. J Am Stat Assoc 76(376): 802–816Google Scholar
  73. Tiao GC (2001) Vector ARMA models. In: Peña D, Tiao GC, Tsay RS (eds) A course in time series analysis. Wiley, LondonGoogle Scholar
  74. Tsay RS, Tiao GC (1985) Use of canonical analysis in time series model identification. Biometrika 72(2): 299–315CrossRefGoogle Scholar
  75. Tsay RS, Wu CS (2003) Forecasting with leading indicators revisited. J Forecast 22: 603–617CrossRefGoogle Scholar
  76. Watson MW, Engle RF (1983) Alternative algorithms for the estimation of dynamic factor, MIMIC and varying coefficient regression models. J Econ (23):385–400Google Scholar
  77. Watson MW, Kraft DF (1984) Testing the interpretation of indices in a macroeconomic index model. J Monet Econ (13):165–181Google Scholar

Copyright information

© The Author(s) 2011

This article is published under license to BioMed Central Ltd. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.Research UnitMinistry of Industry, Tourism and TradeMadridSpain
  2. 2.Macroeconomic Research DepartmentMinistry of Economy and FinanceMadridSpain

Personalised recommendations