Showing 1 - 10 of 48
In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The...
Persistent link: https://www.econbiz.de/10012817069
There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse...
Persistent link: https://www.econbiz.de/10012817070
In this paper a new model of mixture of distributions is proposed, where the mixing structure is determined by a smooth transition tree architecture. Models based on mixture of distributions are useful in order to approximate unknown conditional distributions of multivariate data. The tree...
Persistent link: https://www.econbiz.de/10011807362
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse,high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly,...
Persistent link: https://www.econbiz.de/10011807426
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume that both the number of covariates in the model and the number of candidate variables can increase with the sample size (polynomially orgeometrically). In other...
Persistent link: https://www.econbiz.de/10011807460
In this paper we show the validity of the adaptive LASSO procedure in estimating stationary ARDL(p,q) models with GARCH innovations. We show that, given a set of initial weights, the adaptive Lasso selects the relevant variables with probability converging to one. Afterwards, we show that the...
Persistent link: https://www.econbiz.de/10011807461
In this paper we consider modeling and forecasting of large realized covariance matrices by penalized vector autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong theoretical performance guarantees on the forecast...
Persistent link: https://www.econbiz.de/10010491375
We examine how inequality measures, data sources, income brackets, ranking variables of tabulated tax data, underestimation of incomes in the bottom of the distribution and the methodology used to correct inequality affects the trends of inequality in total income among adults in Brazil between...
Persistent link: https://www.econbiz.de/10011444832
Persistent link: https://www.econbiz.de/10011807281
This paper is concerned with modelling time series by single hidden layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using existing techniques. The problem of selecting the number of hidden units...
Persistent link: https://www.econbiz.de/10011807289