The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting

By M. Forni, Marc Hallin, Marco Lippi, L. Reichlin. 2003

864 citations recorded by the source corpus.

How researchers see this paper

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1. Jushan Bai
2. Liangjun Su
3. Anindya Banerjee
4. Serena Ng
5. In Choi
6. Shujie Ma
7. Emanuel Moench
8. M. Forni

Citations in the corpus

• Are Disaggregate Data Useful for Factor Analysis in Forecasting French GDP (2010, unknown): “Forni et al. (2004, 2005) To estimate the dynamic factors and their covariances, FHLR(2000, 2001, 2004, 2005) propose dynamic principal analysis in the frequency domain.”
• DYNAMIC FACTOR MODELS (2018, unknown): “Hallin and Liška (2007) propose a frequency-domain procedure based on the observation that the rank of the spectrum of the common component of Xt is q.”
• Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version (2013, unknown): “For estimation we replace the unobservable factors F by their principle component estimate”
• Maximum Likelihood Estimation of Factor Models on Data Sets with Arbitrary Pattern of Missing Data (2010, unknown): “3see e.g. Connor and Korajczyk (1986, 1988, 1993); Forni and Reichlin (1996, 1998); Stock and Watson (2002a); Forni, Hallin, Lippi, and Reichlin (2000); Bai (2003); Giannone, Reichlin, and Small (2008); 8 ECB Working Paper Series No 1189 May 2010 frequency data sets can be easily handled.”
• Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors (2015, unknown): “Similar arguments also underlie forecasting using a large number of regressors popularized recently in econometrics by Stock and Watson (2002) and Forni et al. (2005).”
• Nowcasting: the real time informational content of macroeconomic data releases (2008, unknown): “In such a model, a number of common factors (r) that is large relative to the number of common shocks (q) aims at capturing the lead and lag relations among variables along the business cycle (cf. Forni et al., 2005a for details).”
• Determining the Number of Primitive Shocks in Factor Models (2007, unknown): “DGPs 1 and 2 are dynamic factor models considered by Forni et al. (2000) with q = 2 dynamic factors.”
• Forecasting with Many Predictors (2006, unknown): “However, Boivin and Ng (2003) and Forni et al. (2003b) have proposed feasible versions of (17).”
• EFFICIENT ESTIMATION OF FACTOR MODELS (2011, methodology): “In addition, Forni et al. (2005) introduce an estimator of the factor space, which they call generalized principal components of observed time series.”