… in economics and … of sparse models for high-dimensional time series, which is the focus of
our discussion, is primarily concerned with structural estimation, whereas non-sparse models …

… In summary, solving complex high-dimensional dynamic stochastic economic models
numerically in reasonable time imposes several roadblocks. However, the rapidly-evolving field of …

… sparse and dense representations of predictive models in … a single sparse model, but on a
wide set of models that often … and avoid overfitting for high-dimensional models. Third, it treats …

We introduce a novel numerical framework for pricing American options in high dimensions.
Our scheme manages to alleviate the problem of dimension scaling through the use of …

… model (Fama and French, 2015). Typically, each factor in these models has significant
economic … type test for alpha in high-dimensional LFPM with sparse alternatives, explain its …

… Because Uit is high-dimensional, we impose that θ and γd are sparse to facilitate informative
estimation and inference for α. Following Hahn, Mukeherjee, and Carvalho (2013), we refer …

… The next assumptions concern sparse high-dimensional estimation. Assumption 7 imposes
approximate sparsity for h 0 ( z ) and for projections φ ( x ) onto q L ( z ) . Assumption 8 is a …

… of considering structural factor models in which the economic shocks of interest affect both
… that has a sparse VAR structure. Although high-dimensional modeling approaches provide a …

We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects
dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and …

… Fitting sparse models to high-dimensional time series is an important area of statistical
inference. In this paper, we consider sparse vector autoregressive models and develop …