Arbeitspapier

Imputing Monthly Values for Quarterly Time Series. An Application Performed with Swiss Business Cycle Data

This paper documents a comparative application of algorithms to deal with the problem of missing values in higher frequency data sets. We refer to Swiss business tendency survey (BTS) data which are conducted in both monthly and quarterly frequency, where an information sub-set is collected at quarterly frequency only. This occurs in many countries, for example, the harmonised survey programme of the European Union also has this frequency pattern. There is a wide range of ways to address this problem, comprising univariate and multivariate approaches. To evaluate the suitability of the different approaches, we apply them to series that are artificially quarterly, i.e., de facto monthly, from which we create quarterly data by deleting two out of three data points from each quarter. The target series for imputation of missing (deleted) observations comprise the set of time series from the monthly KOF manufacturing BTS survey. At the same time, theses series are ideal to deliver higher frequency information for multivariate imputation algorithms, as they share a common theme, the Swiss business cycle. With this set of indicators, we conduct the different imputations. On this basis, we then run standard tests of forecasting accuracy by comparing the imputed monthly series to the original monthly series. Finally, we take a look at the congruence of the imputed monthly series from the quarterly survey question on firms' technical capacities with existing monthly data on the Swiss economy. The results show that for our data corpus, algorithms based on the approach suggested by Chow and Lin deliver the most precise imputations, followed by multiple OLS regressions.

Sprache
Englisch

Erschienen in
Series: CESifo Working Paper ; No. 10191

Klassifikation
Wirtschaft
Econometric and Statistical Methods: Other
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Forecasting Models; Simulation Methods
Thema
temporal disaggregation
business tendency surveys
out-of-sample validation
mixed-frequency data

Ereignis
Geistige Schöpfung
(wer)
Abberger, Klaus
Graff, Michael
Müller, Oliver
Siliverstovs, Boriss
Ereignis
Veröffentlichung
(wer)
Center for Economic Studies and ifo Institute (CESifo)
(wo)
Munich
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Abberger, Klaus
  • Graff, Michael
  • Müller, Oliver
  • Siliverstovs, Boriss
  • Center for Economic Studies and ifo Institute (CESifo)

Entstanden

  • 2022

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