Nowcasting Austrian Short Term Statistics

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Early estimates for Austrian short term indices were produced using multivariate time-series models. The article presents a simulation study with different models (vector error correction models, vector autoregressive models in levels – both with unadjusted and seasonally adjusted time-series) used for estimating total turnover, production, etc. In a preliminary step, before time-series were provided for nowcasting, the data had to undergo an editing process. In this case a time-series approach was selected for data-editing as well, because of the very specific structure of Austrian enterprises. For this task basically the seasonal adjustment program X13Arima-Seats was used for identifying and replacing outlying observations, imputation of missing values and generating univariate forecasts for every single time series.

Bankowska, K., M. Osiewicz, and S. Perez-Duarte. 2015. “Measuring Nonresponse Bias in a Cross-Country Enterprise Survey.” Statistics Paper Series, European Central Bank. Doi:

Bodo, G., A. Cividini, and L. Signorini. 1991. “Forecasting the Italian Industrial Production Index in Real Time.” Journal of Forecasting 10: 285–299. Doi:

Box, G., G. Jenkins, and G. Reinsel. 2008. Time Series Analysis. Wiley.

Canova, F. and B. Hansen. 1995. “Are Seasonal Patterns Constant Over Time? A Test for Seasonal Stability.” Journal of Business and Economic Statistics 13: 237–252. Doi:

Chang, I., G. Tiao, and C. Chen. 1988. “Estimation of Time Series Parameters in the Presence of Outliers.” Technometrics 30: 193–204. Doi:

Chatfield, C. 2001. Time Series Forecasting. Chapman Hall/CRC.

Cleveland, R., W. Cleveland, J. McRae, and I. Terpenning. 1990. “STL: A Seasonal-Trend Decomposition Procedure Based on Loess.” Journal of Official Statistics 6: 3–73.

Engle, R. and C. Granger. 1987. “Co-Integration and Error Correction: Representation, Estimation and Testing.” Econometrica 55: 251–276.

Engle, R. and B. Yoo. 1987. “Forecasting and Testing in Cointegrated Systems.” Journal of Econometrics 35: 143–159. Doi:

European Commission. 1998. “Council Regulation (EC) No 1165/98 concerning Short-Term Statistics.” Official Journal of the European Communities.

European Commission. 2005. “Regulation (EC) No 1158/2005 of the European Parliament and of the Council, amending Council Regulation (EC) No 1165/98 concerning Short-Term Statistics.” Official Journal of the European Communities.

Findley, D., B. Monsell, W. Bell, M. Otto, and B. Chen. 1998. “New Capabilities and Methods of the X12-ARIMA Seasonal-Adjustment Program.” Journal of Business and Economic Statistics 16: 127–152. Doi:

Friedman, J. 1984a. A Variable Span Scatterplot Smoother. Laboratory for Computational Statistics, Stanford University – Technical Report No. 5.

Friedman, J. 1984b. SMART User’s Guide. Laboratory for Computational Statistics, Stanford University – Technical Report No. 1.

Fröhlich, M., J. Hameseder, and L. Milota. 2010. “Neue Substitutionsmethode für die Konjunkturstatistik im Produzierenden Bereich.” Statistische Nachrichten. Statistics Austria.

Hylleberg, S., R. Engle, C. Granger, and B. Yoo. 1990. “Seasonal Integration and Cointegration.” Journal of Econometrics 44: 215–238. Doi:

Hyndman, R. and Y. Khandakar. 2008. “Automatic Time Series Forecasting: The Forecast Package for R.” Journal of Statistical Software 27(3): 1–22. Doi:

Kowarik, A., A. Meraner, M. Templ, and D. Schopfhauser. 2014. “Seasonal Adjustment with the R-Packages x12 and x12GUI.” Journal of Statistical Software 62. Doi:

Kwiatkowski, D., P. Phillips, P. Schmidt, and Y. Shin. 1992. “Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root. How sure are we that Economic Time Series have a Unit Root.” Journal of Econometrics 54: 159–178. Doi:

Ladiray, D. and D. O’Brian. 2003. “Nowcasting Eurozone Industrial Production.” Working papers and studies. Available at: (accessed May 2018).

Lineback, J. and K. Thompson. 2010. “Conducting Nonresponse Bias Analysis for Business Surveys.” Joint Statistical Meetings (JSM, Vancouver – Canada). Available at: (accessed May 2018).

Lütkepohl, H. 2005. New Introduction to Multiple Time Series Analysis. Springer.

Mazzi, G. and R. Cannata, 2016. Rapid Estimates: Different Products for Different Purposes. Handbook on Rapid Estimates. Doi:

Pfaff, B. 2008. “VAR, SVAR and SVEC Models: Implementation within R Package vars.” Journal of Statistical Software 27/4: 1–32. Doi:

R Core Team. 2010. R: A Language and Environment for Statistical Computing. R. Foundation for Statistical Computing, Vienna, Austria. Available at: (accessed May 2018).

Statistics Finland. 2004a. “Country-Stratified European Sample for Retail Trade Index – Finland.” Statistics Finland, Business Trends – TK-66-783-02. Available at: (accessed April 2018).

Statistics Finland. 2004b. “Turnover in other Services and Production in Construction – Improvement of Timeliness.” Statistics Finland. Business Trends – TK-66-463-03. Available at: (accessed May 2018).

Stier, W. 2001. Methoden der Zeitreihenanalyse. Springer.

Tsay, R.S. 2014. Multivariate Time Series Analysis. Wiley.

U.S. Census Bureau. 2017. X13-ARIMA-SEATS Reference Manual. Version 1.1 edition. Available at: (accessed May 2018).

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