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Nowcasting Swedish GDP with a large and unbalanced data set

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Abstract

We evaluate pseudo-real-time out-of-sample nowcasts for Swedish GDP employing factor models and mixed-data sampling regressions with single predictor variables. These two model classes can handle the data irregularities of a ragged-edge sample and differing sampling frequencies. The results show that pooling of the nowcasts outperforms a simple benchmark, even though only very few of the underlying specifications achieve improved accuracy individually. Moreover, we assess the accuracy of the density forecasts, i.e., the uncertainty around the point forecasts. The post-crisis period after 2008 turns out to be a more difficult period to nowcast precisely. However, indicator variables prove more useful post-crisis as then the performance relative to univariate benchmarks improves.

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Notes

  1. See also the manual of the MIDAS MATLAB toolbox.

  2. GDP is typically published in the second month after the corresponding quarter, which amounts to 5 months after the latest observation \(T_q\).

  3. There exist other alternative lag polynomials. The two most commonly used are the Almon lag and the beta polynomial (see Ghysels et al. (2007)).

  4. The q dynamic factors are estimated as the principal components of the spectral density matrix, which is obtained by smoothing the periodogram with a Bartlett lead-lag window of \(H=60\) months. The periodogram is estimated based on auto-covariances up to \(M=24\) months of leads and lags. Moreover, the smooth relationship is obtained by filtering out fluctuations at frequencies larger than \(\pi /6\). See “Appendix B” in Marcellino and Schumacher (2010) for details.

  5. Note that the F-VAR specification implies a multivariate likelihood and cannot be directly compared to the BIC of the MIDAS equations. The class of F-VAR models is therefore excluded in this weighting scheme.

  6. In Swedish: Konjunkturinstitutets Konjunkturbarometer, see http://www.konj.se/publikationer/konjunkturbarometern.html.

  7. Due to the first-differencing operator, the 1993q1 value disappears.

  8. Which is unlikely when each test is based on the same sample.

  9. For example, the F-MID-EC specifications with \(w=3\) months of information show very low MSEs (see “Appendix A”, Fig. 7). According to the DM test, the MSE is, however, not significantly lower than the benchmark at 5% significance level.

  10. The factor MIDAS specifications F-MID-EM, F-MAR-EM, F-MID-EC, F-MAR-EC are grouped together and labeled F-MIDAS.

  11. Note that flash estimates are only released regarding the second quarter; hence, quarterly National Accounts statistics are published five times a year. Moreover, the Riksbank publishes forecasts underlying monetary policy decisions six times a year. Hence, two forecast occasions are based on the same National Account statistics.

  12. The GDP series is though the August snapshot with the 2014q2 number being the flash estimate.

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Authors and Affiliations

  1. Sveriges Riksbank, 10337, Stockholm, Sweden

    Ard den Reijer

  2. Stockholm School of Economics, 11383, Stockholm, Sweden

    Andreas Johansson

Authors
  1. Ard den Reijer
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  2. Andreas Johansson
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Correspondence to Ard den Reijer.

Additional information

The analysis is to a large extent based on MATLAB code kindly provided by Christian Schumacher. The source code for the model specifications is (indirectly) provided by Marta Bańbura, Mario Forni, James LeSage, Gerhard Rünstler, Kevin Sheppard and Arthur Sinko. All remaining errors are ours.

Appendices

Appendix A: MSE ratio of all models

The figures show the relative nowcast accuracy of the MID, MAR and factor model specifications, respectively (Fig. 5). Overall, most model specifications perform poorly, no significant improvement in nowcast accuracy in comparison with the benchmark. There are some economic indicators that seem better though, e.g., kibar14 and kibar28 in Fig. 6. kibar14 is the confidence indicator for the manufacturing industry, and kibar28 is the current backlog of the manufacturing industry, both variables are part of the Economic Tendency Survey (Table 3).

Appendix B: Data

The data set consists of 92 predictor variables. Around half of the variables originate from the Economic Tendency Survey of the Swedish National Institute of Economic Research. Other survey series consist of different indices of the Purchasing Managers Index (PMI) regarding mainly Sweden and some regarding the US. The remaining data originate from Statistics Sweden or are financial data.

The times series are rendered stationary by following one of the codes: (1) = no transformation; (2) first differences; (3) first differences of logarithms. The stationarity inducing transformations are imposed without employing formal unit root testing procedures. Most of the time series are downloaded in an already seasonally adjusted format, denoted by SA in the description. The remaining time series with seasonal fluctuations are adjusted using Census-X12 prior to the forecast simulations. All the series are, moreover, screened for extreme outliers, which are defined as observations that differ from their sample median by more than six times their sample interquartile range.

The sample period consists of the first month of 1993, 1993m1 until 2014m6, and correspondingly 1993q1–2014q2 for quarterly GDP growth rates \(y_{t_q}\). The data set is downloaded in July 2014Footnote 12 and, hence, represents only the fully revised historical series, or the 2014m7 snapshot, or vintage, of the data.

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den Reijer, A., Johansson, A. Nowcasting Swedish GDP with a large and unbalanced data set. Empir Econ 57, 1351–1373 (2019). https://doi.org/10.1007/s00181-018-1500-1

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