1. Forecasting economic activity using a neural network in uncertain times: Monte Carlo evidence and application to the German GDP. (2024). Holtemöller, Oliver ; Holtemoller, Oliver ; Kozyrev, Boris.
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2. ifo Konjunkturprognose Winter 2020: Das Coronavirus schlägt zurück – erneuter Shutdown bremst Konjunktur ein zweites Mal aus. (2020). Wollmershäuser, Timo ; Wolf, Anna ; Reif, Magnus ; Menkhoff, Manuel ; Link, Sebastian ; Lehmann, Robert ; Lautenbacher, Stefan ; Göttert, Marcell ; Grimme, Christian ; Šauer, Radek ; Mohrle, Sascha ; Sandqvist, Anna Pauliina ; Rathje, Ann-Christin ; Wollmershauser, Timo ; Sauer, Stefan ; Stockli, Marc ; Gottert, Marcell.
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1. Improving the Median CPI: Maximal Disaggregation Isnt Necessarily Optimal. (2025). Zaman, Saeed ; Verbrugge, Randal ; Garciga, Christian.
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2. Real-time nowcasting with sparse factor models. (2022). Hauber, Philipp.
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3. Dynamic factor models: Does the specification matter?. (2022). Ruiz, Esther ; Poncela, Pilar ; Miranda, Karen.
   In: SERIEs: Journal of the Spanish Economic Association.
   RePEc:spr:series:v:13:y:2022:i:1:d:10.1007_s13209-021-00248-2.

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4. On the applicability of dynamic factor models for forecasting real GDP growth in Armenia. (2021). Poghosyan, Karen.
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5. Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach. (2021). Proietti, Tommaso ; Giovannelli, Alessandro ; Ricchi, Ottavio ; Tinti, Cristina ; Tegami, Christian ; Citton, Ambra.
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6. Dynamic factor models: does the specification matter?. (2021). Poncela, Pilar ; Miranda, Karen Alejandra ; Ortega, Esther Ruiz.
   In: DES - Working Papers. Statistics and Econometrics. WS.
   RePEc:cte:wsrepe:32210.

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7. Euro Area Income and Wealth Effects: Aggregation Issues. (2021). Zekaite, Zivile ; de Bondt, Gabe ; Herrero, Pablo ; Gieseck, Arne.
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8. Evaluating early warning and coincident indicators of business cycles using smooth trends. (2020). Bujosa, Marcos ; de Juan, Aranzazu ; Arroyo, Antonio Martin ; Ferrer, Antonio Garcia.
   In: Journal of Forecasting.
   RePEc:wly:jforec:v:39:y:2020:i:1:p:1-17.

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9. A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth. (2020). Cheung, Calista ; Chernis, Tony ; Velasco, Gabriella.
   In: International Journal of Forecasting.
   RePEc:eee:intfor:v:36:y:2020:i:3:p:851-872.

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10. ifoCAST: Der neue Prognosestandard des ifo Instituts. (2020). Wollmershäuser, Timo ; Reif, Magnus ; Lehmann, Robert ; Wollmershauser, Timo.
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11. Domestic and Global Uncertainty: A Survey and Some New Results. (2019). Castelnuovo, Efrem.
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12. Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models. (2019). Luciani, Matteo ; Barigozzi, Matteo.
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13. Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model. (2018). Galli, Alain.
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14. A dynamic factor model for nowcasting Canadian GDP growth. (2017). Sekkel, Rodrigo ; Chernis, Tony.
    In: Empirical Economics.
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15. Determining the number of factors after stationary univariate transformations. (2017). Ruiz, Esther ; Poncela, Pilar ; Corona, Francisco.
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16. Which indicators matter? Analyzing the Swiss business cycle using a large-scale mixed-frequency dynamic factor model. (2017). Galli, Alain.
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17. Mixed-frequency models for tracking short-term economic developments in Switzerland. (2017). Scheufele, Rolf ; Hepenstrick, Christian ; Galli, Alain ; Alain, Rolf Scheufele .
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18. Nowcasting Slovak GDP by a Small Dynamic Factor Model. (2017). Tóth, Peter ; Toth, Peter.
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19. Macroeconomic nowcasting and forecasting with big data. (2017). Tambalotti, Andrea ; Sbordone, Argia ; Giannone, Domenico ; Caratelli, Daniele ; Bok, Brandyn.
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20. Common Factors, Trends, and Cycles in Large Datasets. (2017). Luciani, Matteo ; Barigozzi, Matteo.
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21. Nowcasting BRIC+M in real time. (2017). Vasishtha, Garima ; Guenette, Justin-Damien ; Dahlhaus, Tatjana.
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22. Business tendency surveys and macroeconomic fluctuations. (2017). Scheufele, Rolf ; Kaufmann, Daniel.
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23. A now-casting model for Canada: Do U.S. variables matter?. (2017). Modugno, Michele ; Bragoli, Daniela.
    In: International Journal of Forecasting.
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24. Now-casting the Japanese economy. (2017). Bragoli, Daniela.
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25. Common factors of commodity prices. (2017). Giannone, Domenico ; Ferrara, Laurent ; Delle Chiaie, Simona.
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26. Accurate Subsampling Intervals of Principal Components Factors. (2017). Ruiz, Esther ; de Vicente, Javier.
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27. NOWCASTING THE NEW TURKISH GDP. (2017). Yazgan, Ege ; Soybilgen, Barış.
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28. Common Factors of Commodity Prices. (2017). Giannone, Domenico ; Ferrara, Laurent ; Delle Chiaie, Simona.
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29. A Financial Conditions Index for the CEE economies. (2017). Auer, Simone.
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30. A Dynamic Factor Model for Nowcasting Canadian GDP Growth. (2017). Sekkel, Rodrigo ; Chernis, Tony.
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31. A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth. (2017). Cheung, Calista ; Chernis, Tony ; Velasco, Gabriella.
    In: Discussion Papers.
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32. Global Real Activity for Canadian Exports: GRACE. (2017). de Munnik, Daniel ; Chernis, Tony ; Binette, Andre.
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33. Nowcasting Indian GDP. (2016). Fosten, Jack ; Bragoli, Daniela.
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34. Nowcasting UK GDP during the depression. (2016). Smith, Paul ; Paul, Smith.
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35. Forecasting Czech GDP Using Mixed-Frequency Data Models. (2016). Rusnák, Marek ; Havrlant, David ; Franta, Michal.
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36. On the optimal number of indicators – nowcasting GDP growth in CESEE. (2016). Woerz, Julia ; Tóth, Peter ; Havrlant, David ; Worz, Julia.
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37. Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises. (2016). Scotti, Chiara.
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38. Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics. (2016). Watson, M W ; Stock, J H.
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39. Metro business cycles. (2016). Gascon, Charles ; Arias, Maria A ; Rapach, David E.
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40. Nowcasting Turkish GDP and news decomposition. (2016). Yazgan, Ege ; Soybilgen, Barış ; Modugno, Michele.
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41. Forecasting and nowcasting economic growth in the euro area using factor models. (2016). Koopman, Siem Jan ; Hindrayanto, Irma ; de Winter, Jasper.
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42. Aggregate versus disaggregate information in dynamic factor models. (2016). Perez Quiros, Gabriel ; Camacho, Maximo ; Alvarez, Rocio ; Perez-Quiros, Gabriel.
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    RePEc:eee:intfor:v:32:y:2016:i:3:p:680-694.

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43. Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts. (2016). Jansen, W. Jos ; de Winter, Jasper ; Jin, Xiaowen.
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44. Predicting Finnish economic activity using firm-level data. (2016). Fornaro, Paolo.
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45. The role of country-specific fundamentals in sovereign CDS spreads: Eastern European experiences. (2016). Monostori, Zoltán ; Kocsis, Zalan.
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46. Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data. (2016). Zhang, Zhaoyong ; Koopman, Siem Jan ; Blasques, Francisco ; Mallee, M.
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47. Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs. (2016). Sivec, Vasja ; Marcellino, Massimiliano.
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48. Improving model-based near-term GDP forecasts by subjective forecasts: A real-time exercise for the G7 countries. (2016). Jansen, Jos ; de Winter, Jasper.
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49. Adaptive state space models with applications to the business cycle and financial stress. (2016). Venditti, Fabrizio ; Petrella, Ivan ; Delle Monache, Davide.
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50. Tracking the Slowdown in Long-Run GDP Growth. (2016). Petrella, Ivan ; Drechsel, Thomas ; Antolin-Diaz, Juan.
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51. Tracking the slowdown in long-run GDP growth. (2016). Petrella, Ivan ; Drechsel, Thomas ; Antolin-Diaz, Juan.
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52. Suite of Latvias GDP forecasting models. (2015). Bessonovs, Andrejs.
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53. Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models. (2015). Modugno, Michele ; Lenza, Michele ; Giannone, Domenico ; D'Agostino, Antonello.
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54. A Global Trade Model for the Euro Area. (2015). Osbat, Chiara ; Modugno, Michele ; D'Agostino, Antonello.
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55. Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections. (2015). Lenza, Michele ; Giannone, Domenico ; Banbura, Marta ; Babura, Marta.
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56. Euro area business cycles in turbulent times: convergence or decoupling?. (2015). Klaus, Benjamin ; ferroni, filippo.
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57. Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment. (2015). Ruiz, Esther ; Poncela, Pilar.
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58. Monetary, Fiscal and Oil Shocks: Evidence based on Mixed Frequency Structural FAVARs. (2015). Sivec, Vasja ; Marcellino, Massimiliano.
    In: CEPR Discussion Papers.
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59. Can we use seasonally adjusted variables in dynamic factor models?. (2015). Perez Quiros, Gabriel ; Lovcha, Yuliya ; Camacho, Maximo.
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60. Fast ML estimation of dynamic bifactor models: an application to European inflation.. (2015). Sentana, Enrique ; Galesi, Alessandro ; Fiorentini, Gabriele.
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61. Nowcasting BRIC+M in Real Time. (2015). Vasishtha, Garima ; Guenette, Justin-Damien ; Dahlhaus, Tatjana.
    In: Staff Working Papers.
    RePEc:bca:bocawp:15-38.

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62. Combined Density Nowcasting in an Uncertain Economic Environment. (2014). van Dijk, Herman ; Ravazzolo, Francesco ; Aastveit, Knut Are.
    In: Tinbergen Institute Discussion Papers.
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63. Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP. (2014). Tóth, Peter.
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64. Conditional Forecasts and Scenario Analysis with Vector Autoregressions for Large Cross-Sections. (2014). Lenza, Michele ; Giannone, Domenico ; Banbura, Marta.
    In: Working Papers ECARES.
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65. Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections. (2014). Lenza, Michele ; Giannone, Domenico ; Banbura, Marta.
    In: CEPR Discussion Papers.
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66. Following the Trend: Tracking GDP when Long-Run Growth is Uncertain. (2014). Petrella, Ivan ; Drechsel, Thomas ; Antolin-Diaz, Juan.
    In: CEPR Discussion Papers.
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