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Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which...
Persistent link: https://www.econbiz.de/10010316630
Persistent link: https://www.econbiz.de/10012669217
Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which...
Persistent link: https://www.econbiz.de/10009779502
Persistent link: https://www.econbiz.de/10011782002
Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two...
Persistent link: https://www.econbiz.de/10011708094
Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which...
Persistent link: https://www.econbiz.de/10010955462
One of the most studied questions in economics and finance is whether equity returns or premiums can be predicted by empirical models. While many authors favor the historical mean or other simple parametric methods, this article focuses on nonlinear relationships. A straightforward...
Persistent link: https://www.econbiz.de/10010548008
Persistent link: https://www.econbiz.de/10014266744
In most situations, modern technological developments give rise to the cases where samples are drawn from a population of real random functions. Functional Data Analysis (FDA) is an appropriate multivariate statistical approximation since the classical multivariate methods can not be used when a...
Persistent link: https://www.econbiz.de/10005012089
Microeconomic theory often yields models with multiple nonlinear equations, nonseparable unobservables, nonlinear cross equation restrictions, and many potentially multicollinear covariates. We show how statistical dimension reduction techniques can be applied in models with these features. In...
Persistent link: https://www.econbiz.de/10005027818