Midas Vs. Mixed-Frequency VAR: Nowcasting GDP in the Euro Area

By V. Kuzin, Massimiliano Marcellino, Christian Schumacher. 2011

327 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/6b0b908cc161eea58d7a832b7152dca7fc78320a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016, unknown): “Mixed-frequency VARs provide a third option; see, for example, Kuzin, Marcellino, and Schumacher (2011).”
Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials (2015, methodology): “Kuzin et al. (2011) compared mixed frequency vector auto-regressive (VAR) processes estimated with the Kalman filter with MIDAS regressions, finding an unclear ranking but confirming the good performance of MIDAS.”
Nowcasting GDP in Real Time: A Density Combination Approach (2014, unknown): “Until recently, the academic literature on nowcasting has focused on developing single models that increase forecast accuracy in terms of point nowcast (see, among others, Evans 2005; Giannone, Reichlin, and Small 2008; Kuzin, Marcellino, and Schumacher 2011).”
Real-Time Forecasting With a Mixed-Frequency VAR (2015, unknown): “Kuzin, Marcellino, and Schumacher (2011) compared the accuracy of Euro Area GDP growth forecasts from MIDAS regressions and MF-VARs estimated by maximum likelihood.”
Testing for Granger Causality in Large Mixed-Frequency VARs (2016, unknown): “, Paparoditis (1996), Kilian (1998) or Cavaliere et al.”
Markov-Switching MIDAS Models (2013, background): “Kuzin, Marcellino, and Schumacher (2011) compared the forecasting performance of MIDAS and MF-VAR models for the prediction of the quarterly GDP growth in the Euro area.”
Now-Casting and the Real-time Data Flow (2013, unknown): “…aggregation linear.3 For example, for quarterly GDP in a monthly model we would have: y3t = yt + 2yt−1 + 3yt−2 + 2yt−3 + yt−4 , t = 3, 6, . . . , see e.g. Bańbura, Giannone, and Reichlin (2011), Bańbura and Modugno (2010), Kuzin, Marcellino, and Schumacher (2011) or Mariano and Murasawa (2003).”
A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, unknown): “…in variables as diverse as GDP growth (Andreou et al. (2013), Carriero et al. (2015), Clements and Galvao (2008), Clements and Galvao (2009), Kuzin et al. (2011), Kuzin et al. (2013), Marcellino et al. (2013)), stock market volatility (Ghysels et al. (2007), Ghysels and Valkanov (2012)) and…”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, background): “Equation (43) describes the law of motion of the latent factors, which are driven by a q−dimensional standardized white noise ηtm, where B is a r × q matrix ( r ≤ q).”