Macroeconomic forecasting using penalized regression methods
نویسندگان
چکیده
منابع مشابه
Macroeconomic Forecasting Using Diffusion Indexes
This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts. The method is used to construct 6-, 12-, an...
متن کاملCluster-based regularized sliced inverse regression for forecasting macroeconomic variables
This article concerns the dimension reduction in regression for large dataset. We introduce a new method based on the sliced inverse regression approach, called cluster-based regularized sliced inverse regression. Our method not only keeps the merit of considering both response and predictors information, but also enhances the capability of handling highly correlated variables. It is justified ...
متن کاملCompound identification using penalized linear regression
s Service (CAS) registry number. In the simulation studies, we consider the mass spectra extracted from the NIST Chemistry WebBook (NIST library) as a reference library and the repetitive library as query (experimental) data. In addition, since we assume that the NIST library has the mass spectrum information for all the
متن کاملOutlier Detection Using Nonconvex Penalized Regression
This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the n data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual L1 penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The L1 penalty corresponds to ...
متن کاملSparse Brain Network using Penalized Linear Regression
Sparse partial correlation is a useful connectivity measure for brain networks, especially, when it is hard to compute the exact partial correlation due to the small-n large-p situation. In this paper, we consider a sparse linear regression model with a l1-norm penalty for estimating sparse brain connectivity based on the partial correlation. For the numerical experiments, we construct the spar...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Forecasting
سال: 2018
ISSN: 0169-2070
DOI: 10.1016/j.ijforecast.2018.01.001