Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland

Siliverstovs, Boriss; Kholodilin, Konstantin A.

doi:10.1515/jbnst-2012-0404

Cited by 17 publications

(17 citation statements)

References 22 publications

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“…In this paper we put a large-scale factor model developed in Siliverstovs and Kholodilin (2012) to a forecasting exercise in real-time squared. That is, we strictly use only the information that was available to us at a time of making forecasts and we announce our forecasts at the same time we made those.…”

Section: Resultsmentioning

confidence: 99%

“…The data set of monthly indicators is essentially the same as used in Siliverstovs and Kholodilin (2012). The data set of monthly indicators consists of 558 indicators sub-divided into the following 9 blocks 3 : Purchasing Managers Index in manufacturing supplied by Credit Suisse (9 time series, "PMGR"), consumer price indices (28, "CPI"), labor market indicators (6, "LABOUR"), producer price indices (13, "PPI"), business tendency surveys in manufacturing collected at the KOF Swiss Economic Institute (150, "CHINOGA"), exports and imports (249, "TRADE"), stock market indices (80, "STMKT"), interest rates (20, "INT.RATE"), and exchange rates (3, "EXCH.RATE").…”

Section: Datamentioning

confidence: 99%

“…The final forecast or, more precisely, nowcast of GDP growth in the current quarter is fixed at this date. According to the results reported in Siliverstovs and Kholodilin (2012), such timing of a final forecast release delivers most accurate predictions. Parameters of the large-scale factor model are estimated using the approach of Giannone et al (2008), which proves to be a popular nowcasting method as summarised in the devoted website http://now-casting.com/research.…”

Section: Introductionmentioning

confidence: 96%

“…Nevertheless, it has a number of distinctive features concerning target variable, timing and frequency of release, and econometric approach. Essentially, this paper is an extension of 1 Siliverstovs and Kholodilin (2012), where we investigate forecasting performance of a large-scale factor model in a pseudo real-time framework. Based on this ex post forecasting experiment we selected factor model specification that delivered largest forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Keeping a Finger on the Pulse of the Economy: Nowcasting Swiss GDP in Real-Time Squared

Siliverstovs

2012

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This study evaluates forecasting performance of a large-scale factor model developed in Siliverstovs and Kholodilin (2012) in a genuine ex ante forecasting exercise. We perform our forecast of GDP growth in Switzerland in real time using real-time data vintages collected at weekly frequency. This allows us to monitor how newly released economic and financial data influence our forecasts and hence capture prevailing tendencies in current course of economic development. Terms of use: Documents in EconStor may

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Section: Resultsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Keeping a Finger on the Pulse of the Economy: Nowcasting Swiss GDP in Real-Time Squared

Siliverstovs

2012

SSRN Journal

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“…The data set of monthly indicators, comprising 559 variables, is essentially the same as used in Siliverstovs and Kholodilin (2012). The complete list of variables and their transformations is given in Appendix.…”

Section: Datamentioning

confidence: 99%

Short-Term Forecasting with Mixed-Frequency Data: A MIDASSO Approach

Siliverstovs

2015

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractIn this paper we extend the targeted-regressor approach suggested in Bai and Ng (2008) for variables sampled at the same frequency to mixed-frequency data. Our MIDASSO approach is a combination of the unrestricted MIxed-frequency DAta-Sampling approach (U-MIDAS) (see Foroni et al., 2015;Castle et al., 2009;Bec and Mogliani, 2013), and the LASSO-type penalised regression used in Bai and Ng (2008), called the elastic net (Zou and Hastie, 2005). We illustrate our approach by forecasting the quarterly real GDP growth rate in Switzerland.

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Now-Casting and the Real-Time Data Flow

Banbura

Giannone

Modugno

et al. 2013

Handbook of Economic Forecasting

306

263

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In 2013 all ECB publications feature a motif taken from the €5 banknote.note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. All rights reserved. ISSN 1725-2806 (online) EU Catalogue NoQB-AR-13-061-EN-N (online)Any reproduction, publication and reprint in the form of a different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors. Lucrezia ReichlinLondon Business School and CEPR; e-mail: lreichlin@london.edu AbstractThe term now-casting is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics. In this paper we survey recent developments in economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroeconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations. (Prepared for G. Elliott and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 2, Elsevier-North Holland). Keywords Non-technical summaryNow-casting is defined as the prediction of the present, the very near future and the very recent past. The term is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics. Now-casting is relevant in economics because key statistics on the present state of the economy are available with a significant delay. This is particularly true for those collected on a quarterly basis, with Gross Domestic Product (GDP) being a prominent example. For instance, the first official estimate of GDP in the United States (US) or in the United Kingdom is published approximately one month after the end of the reference quarter. In the euro area the corresponding publication lag is 2-3 weeks longer. Now-casting can also be meaningfully applied to other target variables revealing particular aspects of the state of the economy and thereby followed closely by markets. An example is labor market variables.The basic principle of now-casting is the exploitation of the information which is published early and possibly at higher frequencies than the target variable of interest in order to obtain an 'early estimate' before the official figure becomes available. If the focus is on tracking GDP, one may look at its expenditure components, like for example personal consumption, which for the US is available at a monthly frequency, or variables related to the production side such as industry output. In addition, one may consider information contained in surveys or in forward looking indicators such as financial variables. The idea here is that both 'hard' information like industrial production and 'soft' information like sur...

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Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland

Cited by 17 publications

References 22 publications

Keeping a Finger on the Pulse of the Economy: Nowcasting Swiss GDP in Real-Time Squared

Keeping a Finger on the Pulse of the Economy: Nowcasting Swiss GDP in Real-Time Squared

Short-Term Forecasting with Mixed-Frequency Data: A MIDASSO Approach

Now-Casting and the Real-Time Data Flow

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