A Coincident Index, Common Factors, and Monthly Real GDP*

By Roberto S. Mariano, Yasutomo Murasawa. 2010

175 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/683120cdb270e19cff110d104b8a1ace62c5e995

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

Citations in the corpus

Testing for Granger Causality with Mixed Frequency Data (2016, unknown): “McLeish (1974)), hence we impose a mixing condition.”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “We follow Mariano and Murasawa (2010) in the discussion of estimation of the state-space form and forecasting with the use of Kalman filter and/or smoother.”
Bayesian Mixed Frequency VARs (2015, methodology): “Other mixed-frequency VAR approaches use stockflow relationships for interpolation, such as Zadrozny (1988), Mittnik and Zadrozny (2005), or Mariano and Murasawa (2010).1 The BMF approach, on the other hand, follows the Bayesian approach to missing data similar to, for example, Kim et al. (1998).”
Now-Casting and the Real-time Data Flow (2013, unknown): “15 and to derive the measurement equation using the (approximate) temporal aggregation relationships between the observed variables and Yt as explained in Section 2.1, see e.g. Mariano and Murasawa (2010).”
On factor models with random missing: EM estimation, inference, and cross validation (2020, methodology): “Recognizing the notorious diffi culty in the estimation of discrete structures, such as in variable selection and cluster analysis, Meinshausen and Bühlmann (2010) introduce stability selection based on subsampling in combination with some selection algorithms.”
Nowcasting GDP in Real Time: A Density Combination Approach (2014, unknown): “Accordingly, MF-VARs have recently attracted increased research interest (see, e.g., Giannone, Reichlin, and Simonelli 2009; Mariano and Murasawa 2010; Kuzin, Marcellino, and Schumacher 2011).”
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, methodology): “Moon et al. (2013) start with all the inequalities, then use a modification of Andrews and Soares’s (2010) moment selection procedure to tighten the confidence intervals.”