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Interpolation and backdating with a large information set

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Author Info
Elena Angelini () (European Central Bank, Postfach 160319, 60311 Frankfurt am Main, Germany.)
Jerome Henry () (European Central Bank, Postfach 160319, 60311 Frankfurt am Main, Germany.)
Massimiliano Marcellino () (University of Bocconi - Innocenzo Gasparini Institute for Economic Research (IGIER), Via Salasco 5, 20136 Milan, Italy.)

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Abstract

Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until recently. Nowadays large datasets are readily available, and models with hundreds of parameters fastly estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of the available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also using actual macroeconomic series. Our method seems more robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, depending on the type of interpolation but again be reduced with multivariate approaches, including factor-based ones. JEL Classification: C32; C43; C82.

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Paper provided by European Central Bank in its series Working Paper Series with number 252.

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Length: 41 pages
Date of creation: Aug 2003
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Handle: RePEc:ecb:ecbwps:20030252

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Related research
Keywords: Interpolation; factor model; Kalman filter; spline.;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Nijman, T E & Palm, F C, 1986. "The Construction and Use of Approximations for Missing Quarterly Observations: A Model-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 47-58, January.
  2. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  3. Michael ARTIS & Anindya BANERJEE & Massimiliano MARCELLINO, 2001. "Factor Forecasts for the UK," Economics Working Papers ECO2001/15, European University Institute. [Downloadable!]
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  4. Fernandez, Roque B, 1981. "A Methodological Note on the Estimation of Time Series," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 471-76, August. [Downloadable!] (restricted)
  5. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January. [Downloadable!] (restricted)
  6. Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-91, September. [Downloadable!] (restricted)
  7. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank. [Downloadable!]
  2. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
  3. Marcellino, Massimiliano & Schumacher, Christian, 2008. "Factor-MIDAS for now- and forecasting with ragged-edge data: A model comparison for German GDP," CEPR Discussion Papers 6708, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  4. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute. [Downloadable!]
    Other versions:
  5. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany. [Downloadable!]
  6. Laurent Maurin & Matthieu Darracq Pariès, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production. Perspective from Large Panel factor models," Working Paper Series 894, European Central Bank. [Downloadable!]
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