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Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform KU Leuven
Sliced average variance estimation for multivariate time series KU Leuven
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Supervised dimension reduction for time series is challenging as there may be temporal dependence between the response y and the predictors (Formula presented.). Recently a time series version of sliced inverse regression, TSIR, was suggested, which applies approximate joint diagonalization of several supervised lagged covariance matrices to consider the temporal nature ...
An algorithm for the multivariate group lasso with covariance estimation KU Leuven
We study a group lasso estimator for the multivariate linear regression model that accounts for correlated error terms. A block coordinate descent algorithm is used to compute this estimator. We perform a simulation study with categorical data and multivariate time series data, typical settings with a natural grouping among the predictor variables. Our simulation studies show the good performance of the proposed group lasso estimator compared to ...
Multi-class vector autoregressive models for multi-store sales data KU Leuven
Retailers use the Vector AutoRegressive (VAR) model as a standard tool to estimate the effects of prices, promotions and sales in one product category on the sales of another product category. Besides, these price, promotion and sales data are available for not just one store, but a whole chain of stores. We propose to study cross-category effects using a multi-class VAR model: we jointly estimate cross-category effects for several distinct but ...
An algorithm for the multivariate group lasso with covariance estimation KU Leuven
We study a group lasso estimator for the multivariate linear regression model that accounts for correlated error terms. A block coordinate descent algorithm is used to compute this estimator. We perform a simulation study with categorical data and multivariate time series data, typical settings with a natural grouping among the predictor variables. Our simulation studies show the good performance of the proposed group lasso estimator compared to ...
Multi-class vector autoregressive models for multi-store sales data KU Leuven
Retailers use the Vector AutoRegressive (VAR) model as a standard tool to estimate the effects of prices, promotions and sales in one product category on the sales of another product category. Besides, these price, promotion and sales data are available for not just one store, but a whole chain of stores. We propose to study cross-category effects using a multi-class VAR model: we jointly estimate cross-category effects for several distinct but ...
Linearly transforming variables in the VAR model, how does it change the impulse response KU Leuven
This paper analyzes the impulse response function of vector autoregression models for variables that are linearly transformed. The impulse response is equal to the linear transformation of the original impulse response if and only if the shock is equal to the linear transformation of the original shock. In particular, we consider shocks in one error term only, generalized shocks, structural shocks identified by short-run recursive restrictions ...