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Lasso maximum likelihood estimation of parametric models with singular information matrices

Jin, Fei; Lee, Lung-Fei - In: Econometrics 6 (2018) 1, pp. 1-24

regular case. We propose to estimate such models by the adaptive lasso maximum likelihood and propose an information criterion …

Persistent link: https://www.econbiz.de/10011995209

Building news measures from textual data and an application to volatility forecasting

Caporin, Massimiliano; Poli, Francesco - In: Econometrics 5 (2017) 3, pp. 1-46

We retrieve news stories and earnings announcements of the S&P 100 constituents from two professional news providers, along with ten macroeconomic indicators. We also gather data from Google Trends about these firms' assets as an index of retail investors' attention. Thus, we create an extensive...

Persistent link: https://www.econbiz.de/10011995242

Debiased/double machine learning for instrumental variable quantile regressions

Chen, Jau-er; Huang, Chien-Hsun; Tien, Jia-Jyun - In: Econometrics 9 (2021) 2, pp. 1-18

In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous...

Persistent link: https://www.econbiz.de/10012696320

Two-step Lasso estimation of the spatial weights matrix

Ahrens, Achim; Bhattacharjee, Arnab - In: Econometrics 3 (2015) 1, pp. 128-155

sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two … larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo …

Persistent link: https://www.econbiz.de/10011755274

Two-Step Lasso Estimation of the Spatial Weights Matrix

Ahrens, Achim; Bhattacharjee, Arnab - In: Econometrics 3 (2015) 1, pp. 128-155

approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics … variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte …

Persistent link: https://www.econbiz.de/10011196471