This paper aims at presenting practical applications of latent variable extraction method based on second generation dynamic factor models, which use modified Kalman Filter combined with Maximum Likehood Method and can be applied for time series with mixed frequencies (mainly monthly and quarterly) and unbalanced beginning and the end of the data sample (ragged edges). These applications embrace short-term forecasting of Polish GDP and construction of composite coincident indicator of economic activity in Poland. Presented approach adopts the idea of short-term forecasting used by Camacho and Perez-Quirioz in Banco de Espana and concept of Arouba, Diebold and Scotti index compiled in the FRB of Philadelphia. According to the author’s knowledge, it is the first such adaptation for Central and Eastern Europe country. Quality of the forecast obtained with these models is compared with standard methods used for short-term forecasting with series of statistical tests in the pseudo real-time forecasting exercise. Moreover described method is applied for construction of composite coincident indicator of economic activity in Polish economy. This newly-created coincident indicator is compared with first generation coincident indicator, based on standard dynamic factor model (Stock and Watson) approach, which has been computed by the author for Polish economy since 2006.
short-term forecasting, coincident indicators, factor models, mixed frequencies, ragged edges
 Arouba S.B., Diebold F.X., Scotti C., 2009, Real-Time Measurement of Business Conditions, Journal of Business and Economic Statistics, vol 27 (4), pp. 417-27.
 Barhoumi K., Benk S., Cristadoro R., Den Reijer A., Jakaitiene A., Jelonek P., Rua A., R¨unstler G., Ruth K. and Van Nieuwenhuyze Ch., 2004, Short-Term Forecasting of GDP Using Large Monthly Datasets a Pseudo Real-Time Forecast Evaluation Exercise, ECB Occasional Working Paper, no. 84.
 Boivin J., Ng S., 2006, ”Are More Data Always Better for Factor Analysis?”, Journal of Econometrics, 132, pp. 169-194.
 Bry G., Boschan C., 1971, Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, NBER.
 Burns, A.F., Mitchell W.C., 1946, Measuring Business Cycles, NBER.
 Camacho M., Perez-Quiros G., 2009, ”N~ -STING: Espan~a Short Term INdicator of Growth,” Banco de Espana Working Papers 0912, Banco de Espa~na.
 Camacho M., P´erez-Quiros G., Poncela P., 2010, ”Green Shoots? Where, When and How?”, Working Papers 2010-04, FEDEA.
 Forni M., et al, 2001, ”Coincident and Leading Indicators for the Euro Area,” Economic Journal, Royal Economic Society, vol. 111(471), pp. 62-85.
 Fernandez-Macho F.J., 1997, A Dynamic Factor Model for Economic Time Series, Kybernetika, vol. 33 (6), pp. 583-606.
 Mariano R.S., Murasawa Y., 2003, ”A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series,” Journal of Applied Econometrics, vol. 18 (4), pp 427-443, John Wiley&Sons.
 Inklar R., Jacobs J., Romp W. E., Business Cycle Indexes: Does a Heap of Data Help?, CCSO Working Paper 2003/12.
 http://www.rug.nl/staff/r.c.inklaar/research (accessed: 16.04.2010).
 Kim Ch. J., Nelson Ch. R., 1999, State Space Models with Regime Switching, MIT.
 Kowal P., 2005, ”Matlab Implementation of Commonly used Filters” Computer Programs 0507001, EconWPA.
 Lupinski M., 2007, Konstrukcja wskaznika wyprzedzajacego aktywnoscie ekonomicznej w Polsce, (Construction of Polish Economic Activity Leading Indicator), PhD Dissertation, Warsaw University.
 Lupinski M., 2009, Four Years After Expansion. Are Czech Republic, Hungary and Poland Closer to Core or Periphery of EMU?, Economics, vol. 22, pp. 75-103.
 Stock J. H., Watson M. W., 1989, ”New Indexes of Coincident and Leading Economic Indicators” NBER Chapters, in: NBER Macroeconomics Annual 1989, vol. 4, pp. 351-409, NBER.
 Stock J. H., Watson M. W., 1998, ”Business Cycle Fluctuations in U.S. Macroeconomic Time Series,” NBER Working Papers 6528, NBER.
 Shumway R.H., Stoffer D.S., 1982, An Approach to Time Series Smoothing and Forecasting using the EM algorithm, Journal of Time Series Analysis, vol. 3, pp. 253-264.