The MIDAS Touch: Mixed Data Sampling Regression Models

By Eric Ghysels, Pedro Santa-clara, Rossen Valkanov. 2004

604 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a7522e0bf105d8a2877454631632e89a6adbd914

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Citations in the corpus

• MIDAS Regressions: Further Results and New Directions (2007, unknown): “and Mykland (2003), Due and Glynn (2001), Dufour and Engle (2000), Engle (2000), Ghysels and Jasiak (1998) , Renault and Werker (2002) for some recent examples and further”
• Markov-Switching MIDAS Models (2013, methodology): “The MIDAS approach by Ghysels, Santa-Clara, and Valkanov (2004) involved the regression of variables sampled at different frequencies.”
• Quantile forecasts of daily exchange rate returns from forecasts of realized volatility (2008, unknown): “…s=0 s+1L s ! log(RV 1=2 t s 1;t s) + "t+h: (3) We consider two regression models that use alternative measures of intraday variation as explanatory variables: the Heterogenous Autoregressive model (HAR) proposed by Corsi (2004), and the MIxed Data Sampling (MIDAS) approach of Ghysels et al. (2004).”
• Forecasting with Mixed Frequencies (2010, unknown): “With parsimony in mind, Ghysels, Santa-Clara, and Valkanov (2004) propose a general framework called mixed data sampling (MIDAS) to use when a forecaster wants to estimate a small number of hyperparameters relative to the sampling rate of the higher-frequency vari- F orecasting macroeconomic…”
• Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models (2019, methodology): “The MIDAS model was proposed by Ghysels et al. (2004), and the basic idea is to ensure a parsimonious model specification while allowing for the use of data sampled at different frequencies.”
• Does implied volatility provide any information beyond that captured in model-based volatility forecasts? (2007, methodology): “Parameter estimation was achieved by nonlinear least squares (Ghysels et al., 2004), minimising the sum of squared residuals in equation (7).”
• Multi-Period Forecasts of Volatility: Direct, Iterated, and Mixed-Data Approaches (2009, unknown): “Specification (2.3) was introduced in Ghysels et al. (2002b, 2004) and is suitable for variance forecasting because it provides positive coefficients, which is necessary for a.s. positive definiteness of the forecasted variance.”
• Macroeconomic Forecasting With Mixed-Frequency Data (2008, unknown): “In this article we explore whether the mixed data sampling (MIDAS) approach of Ghysels, Santa-Clara, and Valkanov (2004) and Ghysels, Sinko, and Valkanov (2006b) can be successfully adapted to the modeling and forecasting of a key U.S. macroeconomic variable: U.S. output growth.”
• Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, unknown): “…in table 1 above, the logarithmic daily realized volatilities are approximately unconditionally normally 16 Related mixed data sampling, or MIDAS in the terminology of Ghysels, Santa-Clara, and Valkanov (2004), regressions have recently been estimated by Ghysels, Santa-Clara, and Valkanov (2006).”
• A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “Therefore, the measurement equation can be written as ( ytm xtm ) = MtmΛzztm , (33)”
• Testing for Granger Causality with Mixed Frequency Data (2016, unknown): “In addition to the causality literature, the present paper also draws upon and contributes to the MIDAS literature originated by Ghysels et al. (2004, 2005).”