Estimation and forecasting in models with multiple breaks

By Gary Koop, S. Potter. 2007

214 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/4129ef2cd86aea6e5b4938afb3cb8351cc6a9bec

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Citations in the corpus

Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks (2010, methodology): “3In contrast, Koop and Potter (2007) and Pesaran, Pettenuzzo and Timmerman (2006) develop di¤erent models where, even after a structural break occurs, past data has some predictive power.”
The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling (2017, background): “A voluminous literature has demonstrated that structural instability is present in a wide variety of macroeconomic time series (see, e.g., Canova 1993; Cogley and Sargent 2001; Koop and Potter 2007, among many others).”
Dirichlet Process Hidden Markov Multiple Change-point Model (2015, methodology): “The derivations in the previous section and equation (12) follow the nonparametric Bayesian literature (Neal (2000)) and the infinite HMM of Beal et al.”
Detecting and Predicting Forecast Breakdowns (2009, methodology): “This relates our approach to that in Pesaran, Pettenuzzo and Timmermann (2006) and Koop and Potter (2007), who model breaks in parameters as functions of a latent variable.”
Inference on stochastic time-varying coefficient models (2014, background): “As we noted earlier in the paper, these models have been shown to display pathologies that our approach avoids, as documented in Stock and Watson (1998) and Koop and Potter (2008). The theoretical properties of the kernel estimator are obviously only relevant from a classical perspective.”
SMC2: an efficient algorithm for sequential analysis of state space models (2011, background): “…parameter uncertainty is of interest in many settings; a simple example is out-of-sample prediction, and related goodness-of-fit diagnostics based on prediction residuals, which are popular for instance in econometrics; see for example section 4.3 of Kim et al. (1998) or Koop and Potter (2007).”
K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation? (2015, background): “While Chib (1998) and Pesaran et al. (2007) assume constant transition probabilities, Koop and Potter (2007) render the approach more flexible by introducing a hierarchical prior for state duration which induces duration dependent transition probabilities.”
Real-Time Inflation Forecasting in a Changing World (2009, unknown): “More generally, Koop and Potter (2007), through change-point models, and Pesaran, Pettenuzzo, and Timmermann (2006), through a hierarchical hidden Markov chain model, showed that forecast models that incorporate structural breaks exhibit good out-of-sample forecasting performance for a range of…”