Real-Time Inflation Forecasting in a Changing World
By Jan J. J. Groen, Richard Paap, Francesco Ravazzolo. 2009
243 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/3b018c6e0247836fb9f86a279ee45ff89c12a642
Citations in the corpus
- Adaptive Shrinkage in Bayesian Vector Autoregressive Models (2019, unknown): “A plethora of recent studies (see, among others, Giordani and Villani 2010; Clark 2011; Groen, Paap, and Ravazzolo 2013; Clark and Ravazzolo 2015; Carriero, Clark, and Marcellino 2016) emphasize the importance of allowing for heteroscedasticity in a VAR framework to improve inference and obtain…”
- Time-varying sparsity in dynamic regression models (2014, unknown): “Groen et al. (2009) introduced a latent variable which indicates whether a variable is included in or excluded from the model.”
- Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (2019, unknown): “Among the variables used are financial variables (Forni et al. 2003), commodity prices (Chen et al. 2014) and expectation variables (Groen et al. 2013).”
- A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, unknown): “The monthly predictor variables are an updated version of the 132 macroeconomic series used in Ludvigson and Ng (2009) and extended by Jurado et al. (2014) to December 2011. The series are selected to represent broad categories of macroeconomic quantities such as real output and income, employment a”
- Slack and Cyclically Sensitive Inflation (2019, unknown): “This finding of instability, illustrated here for simple forecasting models, is in line with the literature on inflation forecasting, which stresses the prevalence of time-variation in forecasting relations using activity variables (e.g. Groen, Paap, and Ravazzolo (2013)).”
- Bayesian Multivariate Time Series Methods for Empirical Macroeconomics (2010, unknown): “Another interesting approach with a similar methodology is given by Groen et al. (2008).”
- Complete subset regressions (2013, unknown): “2:+1 = + (1:) + 2:+1, (17) where 2:+1 is the equity premium variable defined above, 1: is the full regressor matrix, 2:+1 is a vector of error terms, and are unknown parameters estimated by OLS, and is a diagonal selector matrix whose unity elements determine which variables get”