OPENING THE BLACK BOX: STRUCTURAL FACTOR MODELS WITH LARGE CROSS SECTIONS

By M. Forni, Domenico Giannone, Marco Lippi, L. Reichlin. Econometric Theory, 2009

440 citations recorded by the source corpus.

This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We argue that all identification schemes employed in structural vector autoregression (SVAR) analysis can be easily adapted in dynamic factor models. Moreover, the “problem of fundamentalness,” which is intractable in SVARs, can be solved, provided that the impulse-response functions are sufficiently heterogeneous. We provide consistent estimators for the impulse-response functions and for (n, T) rates of convergence. An exercise with U.S. macroeconomic data shows that our solution of the fundamentalness problem may have important empirical consequences.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/b2784e268569e1ac352cc9116ea0fd6d6ef897a5

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In Economics and Econometrics

Citations in the corpus

Dynamic factor model with infinite‐dimensional factor space: Forecasting (2016, methodology): “In particular, FHLZ outperforms the other methods, consistently with the results obtained with simulated stationary data in Forni et al. (2017). On the other hand, as soon as the crisis breaks out, as already observed, (I) The targets (1 − L) log(IP) and (1 − L)(1 − L(12)) log(CPI) exhibit unstable”
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, methodology): “Other approaches include Forni et al.’s (2005), which allows for contemporaneous”
News Driven Business Cycles: Insights and Challenges (2014, positive): “Forni et al. (2009) have developed Factor Augmented VARs that use a large number of variables (hundreds) in order to avoid nonfundamentalness.”
A Robust Criterion for Determining the Number of Static Factors in Approximate Factor Models (2008, neutral): “Indeed, it is recognized in the literature that in many empirical applications the information criterion proposed by Bai and Ng (2002) is not able to converge to a minimum even when the maximum number of factors allowed for is set to a high value (see e.g. Forni et al., 2009 ).”
Improved penalization for determining the number of factors in approximate factor models (2010, methodology): “In practice, the method they propose is known to often deliver non-robust results as the number of factors can be over- or under- estimated (see e.g. the application on U.S. macroeconomic data in Forni et al., 2009).”
Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy (2014, positive): “For a formal treatment of the model presented in this section see Forni et al. (2009).”
Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, methodology): “Forni et al. (2009) adapt structural vector autoregression analysis to dynamic factor models.”
Simultaneous multiple change-point and factor analysis for high-dimensional time series (2016, methodology): “We now list and motivate the assumptions imposed on the piecewise stationary factor model see e.g., Stock andWatson (2002), Bai and Ng (2002), Bai (2003), Forni et al. (2009) and Fan et al. (2013) for similar conditions on stationary factor models.”
Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis (2017, methodology): “The proof below closely follows Forni et al. (2009).”
The Effects of Global Shocks on Small Commodity-Exporting Economies: Lessons from Canada (2014, positive): “A natural empirical framework for this exercise is provided by structural dynamic factor models (SDFM) (Stock and Watson 2005; Forni et al. 2009) and factor-augmented VARs (FAVAR) (Bernanke and Boivin 2003; Bernanke, Boivin, and Eliasz 2005; Boivin and Giannoni 2009; Mumtaz and Surico 2009) estimate”