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1. Mixed Sampling Panel Data Model for Regional Job Vacancies Forecasting. (2017). .
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   RePEc:eub:ecoecr:v:3:y:2017:i:1:p:3-20.

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2. COSMO: A new COre Structural MOdel for Ireland. (2017). Smith, Donal ; Morgenroth, Edgar ; Holland, Dawn ; Conroy, Niall ; Bergin, Adele ; McInerney, Niall ; Rodriguez, Abian Garcia.
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3. On the use of high frequency measures of volatility in MIDAS regressions. (2016). Andreou, Elena.
   In: University of Cyprus Working Papers in Economics.
   RePEc:ucy:cypeua:03-2016.

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4. Revisiting the transitional dynamics of business-cycle phases with mixed frequency data. (2016). Bessec, Marie.
   In: Working Papers.
   RePEc:hal:wpaper:hal-01358595.

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5. The Estimation of Continuous Time Models with Mixed Frequency Data. (2016). Chambers, Marcus.
   In: Economics Discussion Papers.
   RePEc:esx:essedp:15988.

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6. Recovering historical inflation data from postal stamps prices. (2016). Franses, Philip Hans ; Janssens, E.
   In: Econometric Institute Research Papers.
   RePEc:ems:eureir:93332.

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7. Yet another look at MIDAS regression. (2016). Franses, Philip Hans.
   In: Econometric Institute Research Papers.
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8. A comparison of MIDAS and bridge equations. (2016). Schumacher, Christian.
   In: International Journal of Forecasting.
   RePEc:eee:intfor:v:32:y:2016:i:2:p:257-270.

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9. Estimating dynamic equilibrium models using mixed frequency macro and financial data. (2016). van der Wel, Michel ; Posch, Olaf ; Christensen, Bent Jesper.
   In: Journal of Econometrics.
   RePEc:eee:econom:v:194:y:2016:i:1:p:116-137.

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10. The estimation of continuous time models with mixed frequency data. (2016). Chambers, Marcus.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:193:y:2016:i:2:p:390-404.

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11. On the use of high frequency measures of volatility in MIDAS regressions. (2016). Andreou, Elena.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:193:y:2016:i:2:p:367-389.

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12. Macroeconomics and the reality of mixed frequency data. (2016). Ghysels, Eric.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:193:y:2016:i:2:p:294-314.

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13. On the use of high frequency measures of volatility in MIDAS regressions. (2016). Andreou, Elena.
    In: CEPR Discussion Papers.
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14. Nowcasting in Real Time Using Popularity Priors. (2015). Monokroussos, George.
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15. Benchmarking Liquidity Proxies: Accounting for Dynamics and Frequency Issues. (2015). Papanagiotou, Evangelia ; Monokroussos, George ; Langedijk, Sven.
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16. Revisiting the transitional dynamics of business-cycle phases with mixed frequency data. (2015). .
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17. The State and the Future of Cyprus Macroeconomic Forecasting. (2015). Andreou, Elena ; Kourtellos, Andros.
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    RePEc:erc:cypepr:v:9:y:2015:i:1:p:73-90.

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18. Does the Greenspan era provide evidence on leadership in the FOMC?. (2015). Jung, Alexander ; El-Shagi, Makram.
    In: Journal of Macroeconomics.
    RePEc:eee:jmacro:v:43:y:2015:i:c:p:173-190.

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19. Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?. (2015). Mogliani, Matteo ; Bec, Frédérique.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:31:y:2015:i:4:p:1021-1042.

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20. Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. (2015). Kilian, Lutz ; Guérin, Pierre ; Baumeister, Christiane.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:31:y:2015:i:2:p:238-252.

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21. Revisiting the transitional dynamics of business-cycle phases with mixed frequency data. (2015). .
    In: Economics Papers from University Paris Dauphine.
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22. MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area. (2014). Schumacher, Christian.
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23. MIDAS and bridge equations. (2014). Schumacher, Christian.
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24. Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis. (2014). Freitag, Lennart ; Freitag L., .
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25. Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series. (2014). Miller, J. ; Ghysels, Eric.
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26. Forecasting Chinese GDP Growth with Mixed Frequency Data. (2014). Mikosch, Heiner ; Zhang, Ying.
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    RePEc:kof:wpskof:14-359.

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27. Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences. (2014). Potter, Simon ; Peach, Richard ; onorante, luca ; Ghysels, Eric ; Alessi, Luci .
    In: Staff Reports.
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28. Forecasting inflation using commodity price aggregates. (2014). Turnovsky, Stephen J ; Chen, Yu-Chin ; Zivot, Eric.
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    RePEc:eee:econom:v:183:y:2014:i:1:p:117-134.

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29. Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences. (2014). Potter, Simon ; Peach, Richard ; onorante, luca ; Alessi, Lucia ; Ghysels, Eric.
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30. Variable Selection in Predictive MIDAS Models. (2014). Marsilli, Clément.
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31. Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work. (2014). Kilian, Lutz ; Guérin, Pierre ; Baumeister, Christiane ; Guerin, Pierre.
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32. Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. (2013). Kilian, Lutz ; Guérin, Pierre ; Baumeister, Christiane.
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33. Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach. (2013). Asgharian, Hossein ; Javed, Farrukh ; Hou, Ai Jun.
    In: Knut Wicksell Working Paper Series.
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34. Nowcasting US GDP: The role of ISM business surveys. (2013). Monokroussos, George ; Lahiri, Kajal.
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    RePEc:eee:intfor:v:29:y:2013:i:4:p:644-658.

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35. Changes in predictive ability with mixed frequency data. (2013). Galvão, Ana ; Galvo, Ana Beatriz.
    In: International Journal of Forecasting.
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36. Advances in Forecasting under Instability. (2013). Rossi, Barbara.
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37. Nowcasting GDP: Electronic Payments, Data Vintages and the Timing of Data Releases. (2013). Tkacz, Greg ; Galbraith, John.
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38. Real-time forecast density combinations (forecasting US GDP growth using mixed-frequency data). (2012). Hecq, Alain ; Götz, Thomas ; Gotz Thomas B., .
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39. Forecasting Mixed Frequency Time Series with ECM-MIDAS Models. (2012). Hecq, Alain ; Götz, Thomas.
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40. Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures. (2012). Miller, J..
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41. Predicting quarterly aggregates with monthly indicators. (2012). Winkelried, Diego.
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42. Testing the functional constraints on parameters in regressions with variables of different frequency. (2012). Zemlys, Vaidotas ; Kvedaras, Virmantas.
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43. U-MIDAS: MIDAS regressions with unrestricted lag polynomials. (2011). Schumacher, Christian ; Marcellino, Massimiliano ; Foroni, Claudia.
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44. Estimating VARs sampled at mixed or irregular spaced frequencies : a Bayesian approach. (2011). Kim, Tae Bong ; Foerster, Andrew ; Chiu, Ching-Wai (Jeremy) ; Seoane, Hernan D. ; Eraker, Bjorn.
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45. Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models. (2011). Chevallier, Julien.
    In: Economic Modelling.
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46. Advances in Forecasting Under Instability. (2011). Rossi, Barbara.
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47. Growth and chronic poverty: Evidence from rural communities in Ethiopia. (2011). Hoddinott, John ; Dercon, Stefan ; Woldehanna, Tassew.
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48. Vector autoregression with varied frequency data. (2010). Qian, Hang.
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49. Linear regression using both temporally aggregated and temporally disaggregated data: Revisited. (2010). Qian, Hang.
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50. MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area. (2009). Schumacher, Christian ; Marcellino, Massimiliano ; Kuzin, Vladimir.
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