1. Amemiya, T., & Wu, R. Y. (1972). The Effect of Aggregation on Prediction in the Autoregressive Model. Journal of the American Statistical Association, 67(339), 628-632. [
DOI:10.1080/01621459.1972.10481264]
2. Andreou, E., Ghysels, E., & Kourtellos, A. (2010). Regression Models with Mixed Sampling Frequencies. Journal of Econometrics, 158(2), 246-261. [
DOI:10.1016/j.jeconom.2010.01.004]
3. Bai, J., Ghysels, E., & Wright, J. H. (2013). State Space Models and MIDAS Regressions. Econometric Reviews, 32(7), 779-813. [
DOI:10.1080/07474938.2012.690675]
4. Breitung, J., & Swanson, N. R. (2002). Temporal Aggregation and Spurious Instantaneous Causality in Multiple Time Series Models. Journal of Time Series Analysis, 23(6), 651-665. [
DOI:10.1111/1467-9892.00284]
5. Chambers, M. J. (2019). Frequency Domain Estimation of Continuous Time Co-Integrated Models with Mixed Frequency and Mixed Sample Data. Journal of Time Series Analysis, 40(6), 887-913. [
DOI:10.1111/jtsa.12461]
6. Chen, X., & Ghysels, E. (2011). News-Good or Bad-and Its Impact on Volatility Predictions Over Multiple Horizons. The Review of Financial Studies, 24(1), 46-81. [
DOI:10.1093/rfs/hhq071]
7. Clements, M. P., & Galvão, A. B. (2008). Macroeconomic Forecasting with Mixed-Frequency Data: Forecasting Output Growth in the United States. Journal of Business & Economic Statistics, 26(4), 546-554. [
DOI:10.1198/073500108000000015]
8. Del Barrio Castro, T., & Hecq, A. (2016). Testing for Deterministic Seasonality in Mixed-Frequency VARs. Economics Letters, 149(1), 20-24. [
DOI:10.1016/j.econlet.2016.09.030]
9. Engle, R. F., & Kozicki, S. (1993). Testing for Common Features. Journal of Business & Economic Statistics, 11(4), 369-380. [
DOI:10.1080/07350015.1993.10509966]
10. Eraker, B., Chiu, C. W., Foerster, A. T., Kim, T. B., & Seoane, H. D. (2014). Bayesian Mixed Frequency VARs. Journal of Financial Econometrics, 13(3), 698-721. [
DOI:10.1093/jjfinec/nbu027]
11. Foroni, C., & Marcellino, M. (2014). Mixed-Frequency Structural Models: Identification, Estimation, and Policy Analysis. Journal of Applied Econometrics, 29(7), 1118-1144. [
DOI:10.1002/jae.2396]
12. Foroni, C., Ghysels, E., & Marcellino, M. (2013). Mixed-Frequency Vector Autoregressive Models. Advances in Econometrics, 32(1), 247-272. [
DOI:10.1108/S0731-9053(2013)0000031007]
13. Forsberg, L., & Ghysels, E. (2007). Why Do Absolute Returns Predict Volatility So Well? Journal of Financial Econometrics, 5(1), 31-67. [
DOI:10.1093/jjfinec/nbl010]
14. Ghysels, E. (2016). Macroeconomics and the Reality of Mixed Frequency Data. Journal of Econometrics, 193(2), 294-314. [
DOI:10.1016/j.jeconom.2016.04.008]
15. Ghysels, E., & Miller, J. I. (2015). Testing for Co-Integration with Temporally Aggregated and Mixed-Frequency Time Series. Journal of Time Series Analysis, 36(6), 797-816. [
DOI:10.1111/jtsa.12129]
16. Ghysels, E., & Wright, J. H. (2009). Forecasting Professional Forecasters. Journal of Business & Economic Statistics, 27(4), 504-516. [
DOI:10.1198/jbes.2009.06044]
17. Ghysels, E., Hill, J. B., & Motegi, K. (2016). Testing for Granger Causality with Mixed Frequency Data. Journal of Econometrics, 192(1), 207-230. [
DOI:10.1016/j.jeconom.2015.07.007]
18. Ghysels, E., Hill, J. B., & Motegi, K. (2018). Testing a Large Set of Zero Restrictions in Regression Models, With An Application to Mixed Frequency Granger Causality. Workshop on Advances in Econometrics 2017 at Hakodate.
19. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Models. CIRANO Working Papers 2004s-20, CIRANO, Montreal, Canada.
20. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a Risk-Return Trade-Off After All. Journal of Financial Economics, 76(3), 509-548. [
DOI:10.1016/j.jfineco.2004.03.008]
21. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2006). Predicting Volatility: Getting the Most Out of Return Data Sampled at Different Frequencies. Journal of Econometrics, 131(1-2), 59-95. [
DOI:10.1016/j.jeconom.2005.01.004]
22. Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS Regressions: Further Results and New Directions. Econometric Reviews, 26(1), 53-90. [
DOI:10.1080/07474930600972467]
23. Götz, T. B., & Hecq, A. (2014). Nowcasting Causality in Mixed Frequency Vector Autoregressive Models. Economics Letters, 122(1), 74-78. [
DOI:10.1016/j.econlet.2013.10.037]
24. Götz, T. B., & Hecq, A. W. (2019). Granger Causality Testing in Mixed-Frequency VARs with Possibly (Co) Integrated Processes. Journal of Time Series Analysis, 40(6), 914-935. [
DOI:10.1111/jtsa.12462]
25. Götz, T. B., Hecq, A., & Smeekes, S. (2016). Testing for Granger Causality in Large Mixed-Frequency VARs. Journal of Econometrics, 193(2), 418-432. [
DOI:10.1016/j.jeconom.2016.04.015]
26. Götz, T. B., Hecq, A., & Urbain, J. P. (2014). Forecasting Mixed-Frequency Time Series with ECM-MIDAS Models. Journal of Forecasting, 33(3), 198-213. [
DOI:10.1002/for.2286]
27. Götz, T. B., Hecq, A., & Urbain, J.-P. (2013). Testing for Common Cycles in Non-Stationary VARs with Varied Frequency Data, VAR Models in Macroeconomics-New Developments and Applications: Essays in Honor of Christopher A. Sims (Advances in Econometrics, Volume 32): Emerald Group Publishing Limited. [
DOI:10.1108/S0731-9053(2013)0000031010]
28. Götz, T., & Hauzenberger, K. (2018). Large Mixed-Frequency VARs with a Parsimonious Time-Varying Parameter Structure. Deutsche Bundesbank Discussion Paper 40/2018. [
DOI:10.2139/ssrn.3259739]
29. Horvath, M. T., & Watson, M. W. (1995). Testing for Co-Integration When Some of the Co-Integrating Vectors are Prespecified. Econometric Theory, 11(5), 984-1014. [
DOI:10.1017/S0266466600009944]
30. Johansen, S. (1988). Statistical Analysis of Co-Integration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254. [
DOI:10.1016/0165-1889(88)90041-3]
31. Kuzin, V., Marcellino, M., & Schumacher, C. (2011). MIDAS vs. Mixed-Frequency VAR: Nowcasting GDP in the Euro Area. International Journal of Forecasting, 27(2), 529-542. [
DOI:10.1016/j.ijforecast.2010.02.006]
32. Kvedaras, V., & Račkauskas, A. (2010). Regression Models with Variables of Different Frequencies: The Case of a Fixed Frequency Ratio. Oxford Bulletin of Economics and Statistics, 72(5), 600-620. [
DOI:10.1111/j.1468-0084.2010.00585.x]
33. Lütkepohl, H. (1984). Forecasting Contemporaneously Aggregated Vector ARMA Processes. Journal of Business & Economic Statistics, 2(3), 201-214. [
DOI:10.1080/07350015.1984.10509388]
34. Marcellino, M. (1999). Some Consequences of Temporal Aggregation in Empirical Analysis. Journal of Business & Economic Statistics, 17(1), 129-136. [
DOI:10.1080/07350015.1999.10524802]
35. Marcellino, M., & Schumacher, C. (2010). Factor MIDAS for Nowcasting and Forecasting With Ragged-Edge Data: A Model Comparison for German GDP. Oxford Bulletin of Economics and Statistics, 72(4), 518-550. [
DOI:10.1111/j.1468-0084.2010.00591.x]
36. Miller, J. I. (2014). Mixed-Frequency Co-Integrating Regressions with Parsimonious Distributed Lag Structures. Journal of Financial Econometrics, 12(3), 584-614. [
DOI:10.1093/jjfinec/nbt010]
37. Miller, J. I. (2016). Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series. Econometric Reviews, 35(6), 1142-1171. [
DOI:10.1080/07474938.2014.976527]
38. Miller, J. I. (2019). Testing Co-Integrating Relationships Using Irregular and Non-Contemporaneous Series with an Application to Paleoclimate Data. Journal of Time Series Analysis, 40(6), 936-950. [
DOI:10.1111/jtsa.12469]
39. Pettenuzzo, D., Timmermann, A., & Valkanov, R. (2016). A MIDAS Approach to Modeling First and Second Moment Dynamics. Journal of Econometrics, 193(2), 315-334. [
DOI:10.1016/j.jeconom.2016.04.009]
40. Qian, H. (2016). A Computationally Efficient Method for Vector Auto-Regression with Mixed Frequency Data. Journal of Econometrics, 193(2), 433-437. [
DOI:10.1016/j.jeconom.2016.04.016]
41. Rodriguez, A., & Puggioni, G. (2010). Mixed Frequency Models: Bayesian Approaches to Estimation and Prediction. International Journal of Forecasting, 26(2), 293-311. [
DOI:10.1016/j.ijforecast.2010.01.009]
42. Schorfheide, F., & Song, D. (2015). Real-Time Forecasting with a Mixed-Frequency VAR. Journal of Business & Economic Statistics, 33(3), 366-380. [
DOI:10.1080/07350015.2014.954707]
43. Vahid, F., & Engle, R. F. (1993). Common Trends and Common Cycles. Journal of Applied Econometrics, 8(4), 341-360.
44. Zadrozny, P. A. (2016). Extended Yule-Walker Identification of VARMA Models With Single-or Mixed-Frequency Data. Journal of Econometrics, 193(2), 438-446. [
DOI:10.1016/j.jeconom.2016.04.017]