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Empirical Economics

Erschienen in:

27.03.2019

Business cycle dating and forecasting with real-time Swiss GDP data

verfasst von: Christian Glocker, Philipp Wegmueller

Erschienen in: Empirical Economics | Ausgabe 1/2020

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Abstract

We develop a small-scale dynamic factor model for the Swiss economy allowing for nonlinearities by means of a two-state Markov chain. The selection of an appropriate set of indicators utilizes a combinatorial algorithm. The model’s forecasting performance is as good as that of peers with richer dynamics. It proves particularly useful for a timely assessment of the business cycle stance, as the recessionary regime probabilities tend to have a leading property. The model successfully anticipated the downturn of the 2008–2009 recession and promptly indicated a fall in GDP growth following the discontinuation of the exchange rate floor of the Swiss Franc.

Vorheriger Artikel Nowcasting Finnish real economic activity: a machine learning approach

Nächster Artikel Evaluation of economic forecasts for Austria

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To the surprise of markets and institutions, the Swiss National Bank (SNB) decided for a discontinuation of the Euro-Swiss Franc (CHF) on January 15, 2015, which had been introduced on September 6, 2011.

Applications of such linear models to countries include for instance Argentina, Canada, Czech Republic, Spain, Switzerland, etc. (Chernis and Sekkel 2017; Camacho and Pérez-Quirós 2011; Camacho et al. 2015a; Rusnák 2016; Marcellino et al. 2016; Galli 2018).

See for instance Giannone et al. (2008), Rünstler et al. (2009), Barhoumi et al. (2010), Marcellino and Schumacher (2010), Bańbura and Rünstler (2011), Aastveit and Trovik (2012), and D’Agostino et al. (2012); see Bańbura et al. (2010) for a review.

Camacho et al. (2015b) show that this one-step estimation outperforms the estimation of a Markov-switching process on the factor in a second step.

See for instance Faust and Wright (2013), Umer et al. (2018), among others.

See for instance Stock and Watson (1999).

Alternatively, one could also consider three states as for instance in Carstensen et al. (2017), who distinguish between normal and severe recessions in an application to the German economy.

Since the evolution of macroeconomic series is smooth enough, such an approximation is appropriate. For instance, Proietti and Moauro (2006) avoid this approximation at the cost of moving to a complicated nonlinear state-space model.

In Switzerland, two distinct authorities are responsible for quarterly and yearly GDP estimates. Based on the yearly GDP measures from the Federal Statics Office (FSO), the State Secretariat of Economic Affairs (SECO) uses temporal disaggregation methods to estimate quarterly GDP figures, which are published periodically about 65 days after the end of a quarter. Revisions to the real-time measure from SECO can stem from different sources: (1) revisions to quarterly indicators; (2) revisions to annual base data; (3) changes in the methodology of national accounts (benchmark revisions); (4) minor changes in the quarterly estimation methods; (5) technical reasons like changes in seasonal adjustment.

We start the revision analysis for the vintage 2004-Q1 up to the final vintage 2016-Q4. Our results are qualitatively robust for using different vintages of \(y_t^{f}\).

The terminology as regards soft versus hard indicators is not necessarily restricted to truly soft or hard indicators. In fact, if a hard indicator, as for instance industrial production, were to be used in the model as year-over-year growth rate, then this transformed variable would have to be treated in the model as a soft indicator.

The AR(2) assumption can be considered as a parsimonious specification: (1) It only requires the estimation of two parameters; (2) it allows a rich dynamic pattern since the roots of \(\phi _q(L)\) and \(\varvec{\Phi }_u(L)\) can be complex.

We omitted autoregressive terms in Eq. (5) as they turned out to be not significantly different from zero.

Means, medians or zeros are valid alternatives.

Technical details are explained in online Appendix.

In the case of Stock and Watson (1991), they chose the four monthly coincident variables comprised in the Index of Coincident Economic Indicators (CEI) compiled by the US Department of Commerce (DOC). In particular industrial production, total personal income less transfer payments, total manufacturing and trade sales and employees on non-agricultural payrolls.

Imports, exports, overnight stays, retail sales, new car registrations, energy consumption, term spread, Swiss market index (SMI), Swiss performance index (SPI), oil price, real and nominal effective exchange rate, bank assets, loans, KOF industrial orders, PMI, UBS consumer survey, KOF industry and construction surveys, vacancy postings, unemployment rate, social security contributions, CPI, EPI, IFO survey, ZEW survey.

In principle, we could also choose \(k=3\) or \(k=5\). We chose \(k=4\) keeping in mind that the DSFM of Stock and Watson (1991) was built on the same number of indicators.

With our approach, the maximum number of variables being considered in the selection algorithm is fixed—this can indeed weigh on the fit of the model. In Sect. 5.2 we show in how far the inclusion of further variables changes the fit of our final-model.

Optimally, we would choose the same variables as in Stock and Watson (1991). For Switzerland, however, such data do not exist, either because of the frequency (e.g., employment is only available on a quarterly frequency) or lack of data (e.g., industrial production).

Both indicators exhibit higher correlation with the year-on-year GDP growth rate than with quarter-on-quarter rate, which is why they load with 11 lags on the common factor. The model outcome does not change qualitatively when they are specified as hard indicators, i.e., loading contemporaneously on the factor.

An interesting alternative to stock market volatility would be a general measure of financial market stress (Duprey et al. 2017; Glocker and Kaniovski 2014) or a measure of business uncertainty (Glocker and Hölzl 2019); unfortunately data for these measures are not available.

The information used in the model stems entirely from business cycle indicators. Economic policy as such does not enter. However, our model is flexible enough so that it could be extended to combine the following two pieces of information on economic policy in real time: (i) the ex-ante path of policy as published/announced by policy makers; (ii) incoming, observed data on the actual degree of implementation of ongoing plans. In this context Pérez-Quirós et al. (2015), Riguzzi and Wegmueller (2015), Glocker (2013, 2012), among others, show that government (consumption) spending conveys useful information about ex-post policy developments relevant for GDP.

The calculations are explained in detail in online Appendix.

We consider our decision rule as rather agnostic—a commonly used alternative threshold is a value of 0.5 [see for instance (Hamilton 1989; Carstensen et al. 2017)]. According to our results, the choice of threshold is of second-order importance, as the smoothed state probabilities quickly jumps to one whenever a technical recession materialized.

ECRI classifies an episode as a recession in which companies dismiss employees, incomes fall, spending goes down, and output declines—the co-movement of all four variables is key. According to Lakshman and Banerji (2004), this definition provides clarity when it comes to determining if a recession has begun, unlike the popular “two quarters of negative GDP growth” rule of thumb, according to which, if GDP falls for two straight quarters, we have met the “technical” definition of a recession. GDP is just a measure of an economy’s output. But if employment, income, and sales do not fall at the same time, the temporary period of negative-output growth will not catch on and spread, and no recession will occur.

For completeness, we have also compared our recession estimates to the business cycle dates published by the OECD. Rather than recessions, this approach identifies the time between a business cycle peak and trough. The OECD business cycle phases are based on the growth-cycle approach, where cycles and turning points are measured and identified in the deviation from trend-series. Against this background, the OECD business cycle phases comprise only a vague basis of comparison. Nevertheless, our recession probabilities are well in line with the identified downswings—we would like to thank an anonymous referee who pointed this out.

An evaluation of forecast errors by using the ex-post data for a specific point in time is questionable since measures of forecast errors—as root-mean-squared error (RMSE)—can be deceptively lower when using ex-post data for GDP rather than real-time data (Stark and Croushore 2002).

To the best of our knowledge, there is no real-time data of monthly Swiss economic indicators publicly available. Of the ten monthly indicators, only imports and sales might have undergone substantial revisions. Financial variables are not revised, and revisions to survey data are seldom and at most marginal.

Diebold and Mariano (1995) provide a pairwise test to analyze whether the differences between two or more competing models are statistically significant. As there is potentially a short-sample problem, we apply the modified version of the Diebold–Mariano test according to Harvey et al. (1997).

Participants of the meeting are the State Secretariat for Economic Affairs (SECO), the Federal Customs Administration (FCA), the Swiss Federal Statistical Office (FSO), the Federal Finance Administration (FFA) and the Swiss National Bank (SNB).

For instance major banks or economic research institutes, among others.

Consider online Appendix for the technical details on the calculation of annual GDP growth rates from quarterly growth rates.

We have omitted the confidence bands for better visibility of the point estimates.

In this context, news does not refer to data revisions as in Sect. 2.2, but rather to economic sentiment and the surprises therein.

The results for this are available upon request.

This implies that Eq. (16) comprises two independent autoregressive processes. We maintain a lag-order of two for all lag polynomials. The state-space representation of the two-factor model comprises a straight forward extension to the one of the single-factor model outlined in online Appendix. Details on this and on the estimated coefficients of the model are available upon request.

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Titel: Business cycle dating and forecasting with real-time Swiss GDP data
verfasst von: Christian Glocker
Philipp Wegmueller
Publikationsdatum: 27.03.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 1/2020
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-019-01666-9

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