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The role of indicator selection in nowcasting euro-area GDP in pseudo-real time

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Abstract

Building on the literature on regularization and dimension reduction methods, the paper presents a quarterly forecasting model for euro-area GDP. The pseudo-real-time nature of the information set is accounted for as the pattern of publication lags is explicitly considered. Forecast evaluation exercises show that predictions obtained through various dimension reduction methods outperform both the benchmark AR and the diffusion index model without preselected indicators. Moreover, forecast combination significantly reduces forecast error.

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Notes

  1. See, for example, Baffigi et al. (2004), Bodo et al. (2000), Forni et al. (2001), Grassman and Keereman (2001), Marcellino et al. (2003) and Rünstler and Sedillot (2003).

  2. Examples of FM–BM comparison are in Klein and Ozmucur (2008) for the USA, Bulligan et al. (2010) for Italy, Antipa et al. (2012) for Germany and Brunhes-Lesage and Darné (2012) for France.

  3. Other alternative techniques to perform this information reduction are in Boivin and Ng (2006), Kaufmann and Schumacher (2013) and Alvarez et al. (2012).

  4. Bayesian model averaging and selection (Koop and Potter 2004) could be an alternative to BM based on statistical procedures and not on ex ante selection. However, Mol et al. (2008) show that Bayesian shrinkage forecasts are highly correlated to the FM forecasts, and Stock and Watson (2012) find that shrinkage forecasts are inferior to the FM forecasts (more encouraging results using Bayesian methods have been recently reported in Bencivelli et al. 2012 and Carriero et al. (2015). Therefore, in this paper we will concentrate on the well established BM and its FM polar approach.

  5. Which is, for example, the aim of Giannone et al. (2008), who introduce a method to track in real-time indicators for GDP nowcasting. For a survey, see Banbura et al. (2013).

  6. See Huang and Lee (2010), and Hendry et al. (2011).

  7. The pseudo-real time experiments mimic the actual situation faced by forecasters in terms of the schedule of data releases and therefore the availability of monthly indicators. Angelini et al. (2011), Banbura and Rünstler (2011), Drechsel and Maurin (2011), Giannone et al. (2009), Rünstler et al. (2009), Gayer et al. (2015) show that the depth in accounting for the timing of data releases plays a crucial role in forecast performance.

  8. See, for example, Hendry and Clements (2002), Stock and Watson (2004), Costantini and Pappalardo (2010), Kisinbay (2010) and Costantini and Kunst (2011).

  9. Although FM do not require as BM the ex ante selection of specific indicators, the researcher must—among the others—address the issues of estimating factors with either static or dynamic principal components (see Stock and Watson 2002a and Stock and Watson 2002b, henceforth SW, and Forni et al. 2005, FHLR). In any case, a key point is that all these approaches estimate the factors without any reference to the target variable.

  10. For this, we will devote particular care to excluding composite leading and coincident indexes from our indicator data set, as they are affected by a spurious forecasting performance when using revised data (Diebold and Rudebusch 1991; Heij et al. 2011). In addition, we will also account for revisions related to the indicators statistical treatment before their use (check for outliers, forecast to balance the panel, quarterly aggregation and seasonal adjustment) in order to exactly mimic the steps adopted in the genuine out-of-sample forecasting practice.

  11. Bulligan et al. (2010) and D’Agostino and Giannone (2012) found that SW and FHLR perform similarly and produce highly collinear forecasts, corroborating our choice of focusing on SW alone as the “representative method” of FM factor extraction in order to simplify result reporting. Boivin and Ng (2005) have shown that SW (i.e. our choice here) performs systematically better when more complicated, but realistic error structures are considered. Alvarez et al. (2012) have shown that FHLR results in similar problems as SW when the number of indicators in the data set is large.

  12. Few exceptions regard modelling in data parsimonious environments, such as the BM approach of Diron (2008) and Ferrara et al. (2010) and the FM approach of Camacho and Perez-Quiros (2010).

  13. Details about the panel of indicators are in Appendix A.1.

  14. An overview of these methods can be found in Bai and Ng (2008) and Ng (2013), among others.

  15. The inclusion of US figures aims at capturing the linkages related to the transmission of international business cycles; see, for example, Bodo et al. (2000).

  16. Appendix A.1 describes the composition of the indicators’ data set and lists them together with the relevant metadata.

  17. Appendix A.2 details the composition and calendar of the three vintages.

  18. Another strategy with FM (but not with BM) would be the use of more specialized techniques rooted in the FM approach; see, for example, the shifting operator (by which all indicators with missing observations for the latest month are shifted in time so as to have a balanced panel) and the expectation-maximization algorithm of Stock and Watson (2002a). Other specialized techniques involve use of approximate Kalman filter models, of Markov switching dynamic factors, of nonparametric methods, of mixed-frequency VARs and of MIDAS regressions; see Camacho and Perez-Quiros (2010), Camacho et al. (2012), Ferrara et al. (2010), Giannone et al. (2008, (2009), Kuzin et al. (2011) and Clements and Galvão (2008).

  19. Details are given in Appendix A.3. Financial variables are released daily and are always available for all the quarters to be forecast (i.e. they never need to be predicted). Although we refer to missing data extrapolation as if all indicators were at monthly intervals, the same steps are followed to predict quarterly indicators, when they are missing for the quarter to be forecast.

  20. In detail, the total number of 148 rounds is obtained by accounting for the 3 different monthly vintages available for each quarter of the 49 rolling windows (the former is from 1990q1 to 2000q4 and the latter from 2002q1 to 2012q4) plus one additional vintage for the period 2002q2–2013q1.

  21. Given a sample of \(N = 259\) indicators, we expanded it in order to account for the leading properties of indicators up to the fourth order. This choice delivers a panel of \(259 \times 5 = 1295\) variables which both enter the factor extraction in FM and the prescreening procedure in PFM.

  22. It could be argued that the great recession of 2008–2009 favours more those models which exploit indicators compared to the AR benchmark as during deep recession episodes indicators embody such news in the nowcast. Therefore, forecasting ability over the AR benchmark may be overstated. However, our aim was to rank the forecasting performance of BM, PFM and FM, i.e. of models that exploit (albeit to a different extent) indicator information.

  23. We have also computed other measures of forecasting accuracy, i.e. mean errors (ME) and mean absolute errors (MAE) that qualitatively give us the same outcomes as those with RMSE (these unreported results are available upon request).

  24. Indicators composition and calendar are detailed in Appendix A.2.

  25. Details are given in Appendix A.4.

  26. These promising results from the use of soft-thresholding techniques are confirmed in Kapetanios et al. (2016) who present a similar application.

  27. Note also that ortogonal regressors improve the ability of our automatic general-to-specific modelling procedure of discovering the better diffusion index model specification.

  28. The procedure is sketched in Appendix A.5.

  29. In fact, after the first training sample needed to estimate the quantiles of the distribution of picks’ frequency in the PFM approach, the available span of data here cannot allow for a second training sample to run ex ante the hierarchical forecast combination, in order to run fully ex ante the procedure to assess the forecasting ability.

  30. Detailed outcomes are given in Appendix A.6.

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Authors and Affiliations

  1. Econometric Studies and Economic Forecasting Division, Italian National Institute of Statistics (ISTAT), Rome, Italy

    Alessandro Girardi

  2. Department of Economics, University of Bologna, Bologna, Italy

    Roberto Golinelli

  3. Macroeconomic Analysis Department, Parliamentary Budget Office, Rome, Italy

    Carmine Pappalardo

Authors
  1. Alessandro Girardi
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  2. Roberto Golinelli
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  3. Carmine Pappalardo
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Correspondence to Roberto Golinelli.

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Girardi, A., Golinelli, R. & Pappalardo, C. The role of indicator selection in nowcasting euro-area GDP in pseudo-real time. Empir Econ 53, 79–99 (2017). https://doi.org/10.1007/s00181-016-1151-z

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