Similar Search Results

Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions

Andersen, Torben - 2020

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the...

Persistent link: https://www.econbiz.de/10012831312

An Analysis of the Indicator Saturation Estimator as a Robust Regression Estimator

Johansen, Soren - 2008

An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of finding an estimator that is robust to outliers and structural breaks. This estimator is an example of a one-step M-estimator based on Huber's skip...

Persistent link: https://www.econbiz.de/10012723996

An Analysis of the Indicator Saturation Estimator as a Robust Regression Estimator

Johansen, Soren - 2008

Persistent link: https://www.econbiz.de/10012724437

A penalized two-pass regression to predict stock returns with time-varying risk premia

Bakalli, Gaetan; Guerrier, Stéphane; Scaillet, Olivier - 2021

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

Persistent link: https://www.econbiz.de/10012487589

Forecasting Oil Prices With Penalized Regressions, Variance Risk Premia and Google Data

Lycheva, Maria; Mironenkov, Alexey; Kurbatskii, Alexey; … - 2023

variables, a Hierarchical Vector Auto-Regression model estimated with the LASSO, and a set of forecasting models using a …

Persistent link: https://www.econbiz.de/10014349277

Forecasting using sparse cointegration

Wilms, Ines; Croux, Christophe - In: International journal of forecasting 32 (2016) 4, pp. 1256-1267

Persistent link: https://www.econbiz.de/10011622146

Local projection inference in high dimensions

Adamek, Robert; Smeekes, Stephan; Wilms, Ines - 2024

Persistent link: https://www.econbiz.de/10015357780

Machine Learning Time Series Regressions With an Application to Nowcasting

Babii, Andrii; Ghysels, Eric; Striaukas, Jonas - 2021

at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and … outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that … allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than …

Persistent link: https://www.econbiz.de/10013238628

High Dimensional Threshold Regression with Common Stochastic Trends

Massacci, Daniele; Trapani, Lorenzo - 2022

We study inference for threshold regression in the context of a large panel factor model with common stochastic trends. We develop a Least Squares estimator for the threshold level, deriving almost sure rates of convergence and proposing a novel, testing based, way of constructing confidence...

Persistent link: https://www.econbiz.de/10014082424

Variable selection in high dimensional linear regressions with parameter instability

Chudik, Alexander; Pesaran, M. Hashem; Sharifvaghefi, Mahrad - 2023

forecasting stage. The benefits of the proposed method as compared to Lasso, Adaptive Lasso and Boosting are illustrated by Monte …

Persistent link: https://www.econbiz.de/10013494088