scholarly journals The Highly Adaptive Lasso Estimator

2016 ◽  
Author(s):  
David Benkeser ◽  
Mark Van Der Laan
Keyword(s):  
Adaptive Lasso ◽  
Lasso Estimator

Multiple structural breaks in cointegrating regressions: a model selection approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexander Schmidt ◽  
Karsten Schweikert
Keyword(s):  
Structural Breaks ◽  
Penalized Regression ◽  
Adaptive Lasso ◽  
Test Statistics ◽  
New Approach ◽  
Multiple Structural Breaks ◽  
Long Run ◽  
Model Selection Approach ◽  
Regression Techniques ◽  
Lasso Estimator

Abstract In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with known breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.

scholarly journals Catching Growth Determinants with the Adaptive Lasso

2012 ◽  
Vol 13 (1) ◽  
pp. 71-85 ◽  
Author(s):  
Ulrike Schneider ◽  
Martin Wagner
Keyword(s):  
Economic Growth ◽  
Computational Cost ◽  
Adaptive Lasso ◽  
The European Union ◽  
Initial Activity ◽  
Efficient Procedure ◽  
Growth Regressions ◽  
Working Age ◽  
Low Educated ◽  
Lasso Estimator

Abstract This paper uses the adaptive Lasso estimator to determine variables important for economic growth. The adaptive Lasso estimator is a computationally very efficient procedure that simultaneously performs model selection and parameter estimation. The computational cost of this method is negligibly small compared with standard approaches in the growth regressions literature. We apply this method for a regional dataset for the European Union covering the 255 NUTS2 regions in the 27 member states over the period 1995-2005. The results suggest that initial GDP per capita (with an implied convergence speed of about 1.5% per annum), human capital ( proxied by the shares of highly and medium educated in the working age population), structural labor market characteristics (the initial unemployment rate and the initial activity rate of the low educated) as well as being a capital region are important for economic growth.

scholarly journals UNIFORM ASYMPTOTICS AND CONFIDENCE REGIONS BASED ON THE ADAPTIVE LASSO WITH PARTIALLY CONSISTENT TUNING

2021 ◽  
pp. 1-26
Author(s):  
Nicolai Amann ◽  
Ulrike Schneider
Keyword(s):  
Confidence Regions ◽  
Adaptive Lasso ◽  
Confidence Set ◽  
Tuning Parameters ◽  
Open Set ◽  
Consistent Model ◽  
Statistical Planning ◽  
Low Dimensional ◽  
Lasso Estimator ◽  
Consistency Properties

We consider the adaptive Lasso estimator with componentwise tuning in the framework of a low-dimensional linear regression model. In our setting, at least one of the components is penalized at the rate of consistent model selection and certain components may not be penalized at all. We perform a detailed study of the consistency properties and the asymptotic distribution which includes the effects of componentwise tuning within a so-called moving-parameter framework. These results enable us to explicitly provide a set $\mathcal {M}$ such that every open superset acts as a confidence set with uniform asymptotic coverage equal to 1, whereas removing an arbitrarily small open set along the boundary yields a confidence set with uniform asymptotic coverage equal to 0. The shape of the set $\mathcal {M}$ depends on the regressor matrix as well as the deviations within the componentwise tuning parameters. Our findings can be viewed as a broad generalization of Pötscher and Schneider (2009, Journal of Statistical Planning and Inference 139, 2775–2790; 2010, Electronic Journal of Statistics 4, 334–360), who considered distributional properties and confidence intervals based on components of the adaptive Lasso estimator for the case of orthogonal regressors.

scholarly journals On the distribution of the adaptive LASSO estimator

2009 ◽  
Vol 139 (8) ◽  
pp. 2775-2790 ◽  
Author(s):  
Benedikt M. Pötscher ◽  
Ulrike Schneider
Keyword(s):  
Adaptive Lasso ◽  
Lasso Estimator

A robust adaptive Lasso estimator for the independent contamination model

2020 ◽  
Vol 174 ◽  
pp. 107608
Author(s):  
Jasin Machkour ◽  
Michael Muma ◽  
Bastian Alt ◽  
Abdelhak M. Zoubir
Keyword(s):  
Adaptive Lasso ◽  
Robust Adaptive ◽  
Lasso Estimator

scholarly journals Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan G. De Gooijer ◽  
Dawit Zerom
Keyword(s):  
Risk Premium ◽  
Empirical Studies ◽  
Forecast Accuracy ◽  
Equity Premium ◽  
Adaptive Lasso ◽  
Large Set ◽  
Usual Practice ◽  
Conditional Quantile ◽  
Equity Risk ◽  
Non Parametric

Abstract We propose a hybrid penalized averaging for combining parametric and non-parametric quantile forecasts when faced with a large number of predictors. This approach goes beyond the usual practice of combining conditional mean forecasts from parametric time series models with only a few predictors. The hybrid methodology adopts the adaptive LASSO regularization to simultaneously reduce predictor dimension and obtain quantile forecasts. Several recent empirical studies have considered a large set of macroeconomic predictors and technical indicators with the goal of forecasting the S&P 500 equity risk premium. To illustrate the merit of the proposed approach, we extend the mean-based equity premium forecasting into the conditional quantile context. The application offers three main findings. First, combining parametric and non-parametric approaches adds quantile forecast accuracy over and above the constituent methods. Second, a handful of macroeconomic predictors are found to have systematic forecasting power. Third, different predictors are identified as important when considering lower, central and upper quantiles of the equity premium distribution.

scholarly journals Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4173
Author(s):  
Rangan Gupta ◽  
Christian Pierdzioch
Keyword(s):  
Crude Oil ◽  
Predictive Value ◽  
Realized Variance ◽  
International Spillovers ◽  
Forecasting Models ◽  
West Texas ◽  
Out Of Sample ◽  
One Year ◽  
Lasso Estimator

We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.

scholarly journals Factors Affecting Salt Reduction Measure Adoption among Chinese Residents

2021 ◽  
Vol 18 (2) ◽  
pp. 445
Author(s):  
Zeying Huang ◽  
Di Zeng
Keyword(s):  
Logistic Regression ◽  
Food Intake ◽  
Nutrition Education ◽  
Online Survey ◽  
Adaptive Lasso ◽  
Stepwise Logistic Regression ◽  
Salt Reduction ◽  
Salt Restriction ◽  
Salt Diet ◽  
Factors Affecting

China has the highest mortality rate caused by diseases and conditions associated with its high-salt diet. Since 2016, China has initiated a national salt reduction campaign that aims at promoting the usage of salt information on food labels and salt-restriction spoons and reducing condiment and pickled food intake. However, factors affecting individuals’ decisions to adopt these salt reduction measures remain largely unknown. By comparing the performances of logistic regression, stepwise logistic regression, lasso logistic regression and adaptive lasso logistic regression, this study aims to fill this gap by analyzing the adoption behaviour of 1610 individuals from a nationally representative online survey. It was found that the practices were far from adopted and only 26.40%, 22.98%, 33.54% and 37.20% reported the adoption of labelled salt information, salt-restriction spoons, reduced condiment use in home cooking and reduced pickled food intake, respectively. Knowledge on salt, the perceived benefits of salt reduction, participation in nutrition education and training programs on sodium reduction were positively associated with using salt information labels. Adoption of the other measures was largely explained by people’s awareness of hypertension risks and taste preferences. It is therefore recommended that policy interventions should enhance Chinese individuals’ knowledge of salt, raise the awareness of the benefits associated with a low-salt diet and the risks associated with consuming excessive salt and reshape their taste choices.

scholarly journals The log-linear group-lasso estimator and its asymptotic properties

Bernoulli ◽  
2012 ◽  
Vol 18 (3) ◽  
pp. 945-974 ◽  
Author(s):  
Yuval Nardi ◽  
Alessandro Rinaldo
Keyword(s):  
Linear Group ◽  
Asymptotic Properties ◽  
Group Lasso ◽  
Log Linear ◽  
Lasso Estimator

scholarly journals Variable selection for causal mediation analysis using LASSO-based methods

2021 ◽  
pp. 096228022199750
Author(s):  
Zhaoxin Ye ◽  
Yeying Zhu ◽  
Donna L Coffman
Keyword(s):  
Propensity Score ◽  
Indirect Effect ◽  
Adaptive Lasso ◽  
Mediation Effect ◽  
Regularization Methods ◽  
Causal Mediation ◽  
Outcome Information ◽  
Causal Mediation Analysis ◽  
Selection For ◽  
Mimic Iii

Causal mediation effect estimates can be obtained from marginal structural models using inverse probability weighting with appropriate weights. In order to compute weights, treatment and mediator propensity score models need to be fitted first. If the covariates are high-dimensional, parsimonious propensity score models can be developed by regularization methods including LASSO and its variants. Furthermore, in a mediation setup, more efficient direct or indirect effect estimators can be obtained by using outcome-adaptive LASSO to select variables for propensity score models by incorporating the outcome information. A simulation study is conducted to assess how different regularization methods can affect the performance of estimated natural direct and indirect effect odds ratios. Our simulation results show that regularizing propensity score models by outcome-adaptive LASSO can improve the efficiency of the natural effect estimators and by optimizing balance in the covariates, bias can be reduced in most cases. The regularization methods are then applied to MIMIC-III database, an ICU database developed by MIT.

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