Forecasting in dynamic factor models subject to structural instability

By James H. Stock, Mark W. Watson. 2009

316 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/83e0bb1ef15d5b3ecd5fd78e392f1487835480a9

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Citations in the corpus

Fitting dynamic factor models to non-stationary time series (2009, background): “A recent approach of Stock and Watson (2008) investigates the effect of structural instabilities on the forecasting ability of a dynamic factor model with a possible abruptly changing parameter in an autoregressive factor specification.”
Forecasting with Medium and Large Bayesian VARs (2013, methodology): “Appendix B: Data The data set used in this paper is an updated version of that used in Stock and Watson (2008) and the reader is referred to that paper for more details about the data.”
Consistent Factor Estimation in Dynamic Factor Models with Structural Instability (2013, background): “…nearly all of this theory has been developed for the case in which the DFM parameters are stable, in particular, in which there are no changes in the factor loadings (the coefficients on the factors); among the few exceptions are Stock and Watson (2002, 2009) and Breitung and Eickmeier (2011).”
Testing for Structural Stability of Factor Augmented Forecasting Models (2014, unknown): “Following this intuition, Stock and Watson (2009) argue that similar logic should also apply to diffusion index models.”
Role of macroeconomic variables on firms’ performance: Evidence from the UK (2017, methodology): “The macroeconomic variables chosen for this research were taken from data compiled by Stock and Watson (2008) and the measures of economic activity according the Leading and Coincident Indicators, Supply or Cost-Push Theories, Monetary Economics, and Savings-Investment theories.”
Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models (2019, methodology): “Therefore, with reference to the multivariate combined forecast concept of Timmermann, we use the weighting schemes proposed by Stock and Watson (2004, 2008) and Andreou et al. (2013) to combine several models with high forecast accuracy.”
Detecting big structural breaks in large factor models (2014, methodology): “Stock and Watson (2009) get similar conclusions about the existence of breaks around the early 1980s.”
DYNAMIC FACTOR MODELS (2018, background): “Stock and Watson (2009c) compare factor forecasts to other high-dimensional forecasts in U.S. data and also find mixed results.”
TESTS FOR PARAMETER INSTABILITY IN DYNAMIC FACTOR MODELS (2013, methodology): “To the best of our knowledge, there are three existing tests for structural instability of factor loadings.1 One is proposed by Stock and Watson (2009) (henceforth SW), who regressed each variable on the estimated factors and implemented a Chow test for each of these regressions.”
Testing for Structural Breaks in Dynamic Factor Models (2011, background): “Giordani (2007) has pointed out that, although some series may be I(1) in the total period, they may be stationary in subperiods and differencing them would result in an overdifferencing.”
Large time-varying parameter VARs (2013, methodology): “, Stock and Watson (2008) and recommendations in Carriero, Clark and Marcellino (2011) we transform all variables to stationarity. The choice of which variables are included in which TVP-VAR is motivated by the choices of Banbura et al (2010). The Data Appendix provides a complete listing of the var”
On Time-Varying Factor Models: Estimation and Testing ∗ (2017, methodology): “For example, Su and Chen (2013) consider testing slope homogeneity in linear panel data models with interactive fixed effects and they find that their LM-type statistic has nontrivial power against parametric local alternatives converging to the null at the rate T−1/2N−1/4.”
Estimation of large dimensional factor models with an unknown number of breaks (2018, methodology): “Macroeconomic DataSet (Stock and Watson, 2009) to detect possible structural breaks in the underlying factor model.”