G2TT
来源类型Discussion paper
规范类型论文
来源IDDP11388
DP11388 In-sample Inference and Forecasting in Misspecified Factor Models
Barbara Rossi
发表日期2016-07-12
出版年2016
语种英语
摘要This paper considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension reduction devices: principal compo- nents, Ridge, Landweber Fridman, and Partial Least Squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross- validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting ináation and output growth in the U.S. show that data-reduction methods out- perform conventional methods in several relevant settings, and might e§ectively guard against instabilities in predictorsíforecasting ability.
主题Monetary Economics and Fluctuations
关键词Forecasting Regularization methods Factor models Ridge Partial least squares Principal components Sparsity Large datasets Variable selection Gdp forecasts
URLhttps://cepr.org/publications/dp11388
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/540201
推荐引用方式
GB/T 7714
Barbara Rossi. DP11388 In-sample Inference and Forecasting in Misspecified Factor Models. 2016.
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