A survey of estimation methods for sparse high-dimensional time series models
S Basu, DS Matteson - arXiv preprint arXiv:2107.14754, 2021 - arxiv.org
… 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 …
our discussion, is primarily concerned with structural estimation, whereas non-sparse models …
Sparse grids for dynamic economic models
… In summary, solving complex high-dimensional dynamic stochastic economic models
numerically in reasonable time imposes several roadblocks. However, the rapidly-evolving field of …
numerically in reasonable time imposes several roadblocks. However, the rapidly-evolving field of …
Economic predictions with big data: The illusion of sparsity
… 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 …
wide set of models that often … and avoid overfitting for high-dimensional models. Third, it treats …
Pricing American options under high-dimensional models with recursive adaptive sparse expectations
S Scheidegger, A Treccani - Journal of Financial Econometrics, 2021 - academic.oup.com
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 …
Our scheme manages to alleviate the problem of dimension scaling through the use of …
High-dimensional test for alpha in linear factor pricing models with sparse alternatives
L Feng, W Lan, B Liu, Y Ma - Journal of Econometrics, 2022 - Elsevier
… 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 …
economic … type test for alpha in high-dimensional LFPM with sparse alternatives, explain its …
The factor-lasso and k-step bootstrap approach for inference in high-dimensional economic applications
C Hansen, Y Liao - Econometric Theory, 2019 - cambridge.org
… 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 …
estimation and inference for α. Following Hahn, Mukeherjee, and Carvalho (2013), we refer …
Inference in additively separable models with a high-dimensional set of conditioning variables
D Kozbur - Journal of Business & Economic Statistics, 2021 - Taylor & Francis
… 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 …
approximate sparsity for h 0 ( z ) and for projections φ ( x ) onto q L ( z ) . Assumption 8 is a …
Structural inference in sparse high-dimensional vector autoregressions
… 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 …
… that has a sparse VAR structure. Although high-dimensional modeling approaches provide a …
Uniform inference in high-dimensional dynamic panel data models with approximately sparse fixed effects
AB Kock, H Tang - Econometric Theory, 2019 - cambridge.org
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 …
dynamic panel data models. The inequalities are valid for the coefficients of the dynamic and …
Bootstrap based inference for sparse high-dimensional time series models
… 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 …
inference. In this paper, we consider sparse vector autoregressive models and develop …
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