Macroeconomic Forecasting With Mixed-Frequency Data

By Michael P. Clements, A. Galvão. 2008

402 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/824f059495ea48ba7f3dbe424746303ba9221243

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In Economics and Econometrics

Citations in the corpus

Forecasting with Mixed Frequencies (2010, methodology): “For example, Clements and Galvão (2008) introduced a common factor to the MIDAS model with an autoregressive (AR) component.”
Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work (2015, background): “…MIDAS regressions to improve the accuracy of quarterly macroeconomic forecasts based on monthly predictors and the accuracy of monthly forecasts based on daily or weekly predictors (e.g., Andreou, Ghysels, and Kourtellos 2013; Clements and Galvao 2008, 2009; Ghysels and Wright 2009; Hamilton 2008).”
Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016, positive): “…approach to short-term forecasting with mixed-frequency data is based on the MIDAS regressions introduced by Ghysels, Santa-Clara, and Valkanov (2004); see, for example, Clements and Galvão (2008), Foroni and Marcellino (2014), and Marcellino and Schumacher (2010) for macroeconomic applications.”
The Baltic Dry Index as a Predictor of Global Stock Returns, Commodity Returns, and Global Economic Activity (2010, background): “Our measure of real economic activity is industrial production (see also, Stock and Watson (1989, 2003), Croushore and Stark (2003), Clements and Galvao (2008), and Aruoba, Diebold, and Scotti (2009)), and we concentrate on the monthly growth rate of the seasonally adjusted production of total indus”
Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models (2019, methodology): “(2004), is in general use to that end: the MIDAS model has sparked attention since first being proposed and it has been widely used in many fields; meanwhile, its model form has also benefitted from subsequent development (Foroni and Marcellino, 2014; Clements and Galvao, 2008; Andreou et al., 2013)”
Markov-Switching MIDAS Models (2013, methodology): “Ghysels, Santa-Clara, and Valkanov (2006) used the MIDAS framework to predict the volatility of equity returns, while Clements and Galvao (2008) successfully applied MIDAS models to the prediction of quarterly U.S. GDP growth using monthly indicators as highfrequency variables. Andreou, Ghysels, and”
Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials (2015, methodology): “For example, Clements and Galvão (2008, 2009) proposed to use MIDAS for forecasting quarterly GDP growth by using monthly business cycle indicators; see also Kuzin et al. (2011), Bai et al. (2013) and Marcellino and Schumacher (2010), among others.”
Nowcasting GDP in Real Time: A Density Combination Approach (2014, neutral): “Lately, increased interest has also been given to mixed data sampling (MIDAS) models (see, among others, Clements and Galvão 2008, 2009; Ghysels and Wright 2009; Kuzin, Marcellino, and Schumacher 2011).”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “Clements and Galvao (2008) introduce the use of MIDAS regressions in forecasting macroeconomic data.”
Markov-switching dynamic factor models in real time (2018, background): “and argues that it obtains similar, although rather superior results that with the model with changing mean implied by (14).”
A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, neutral): “We use the simpler SV volatility specification in our benchmark to reflect the popularity of this approach in empirical work; see Cogley et al. (2005), D’Agostino et al.”
Testing for Granger Causality in Large Mixed-Frequency VARs (2016, neutral): “These notational conventions have become standard in the mixed-frequency literature and are similar to the ones in G¨otz et al. (2014), Clements and Galv˜ao (2008, 2009) or Miller (2012).”
Now-Casting and the Real-time Data Flow (2013, neutral): “Clements and Galvão (2008) propose how to add a lag of the low frequency variable in order to avoid a seasonal response of the dependent variable to the predictors.”
High‐dimensional macroeconomic forecasting and variable selection via penalized regression (2019, methodology): “The results are not surprising because previous studies, such as Clements and Galvao (2008) and Marcellino and Schumacher (2010), reported the same tendency.”
State Space Models and MIDAS Regressions (2013, background): “…regressions in the context of improving quarterly macro forecasts with monthly data (see e.g. Armesto, Hernandez-Murillo, Owyang, and Piger (2008), Clements and Galvão (2008a), Clements and Galvão (2008b), Galvão (2006), Schumacher and Breitung (2008), Tay (2007)), or improving quarterly and…”