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LASSO-type penalization in the framework of generalized additive models for location, scale and shape
Groll, Andreas; Hambuckers, Julien; Kneib, Thomas; … - 2018
models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high … dimensional data set-ups classical fitting procedures for the GAMLSS often become rather unstable and methods for variable … GAMLSS. It is designed for linear covariate effects and is based on L1-type penalties. The following three penalization …
Persistent link: https://www.econbiz.de/10011930753
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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
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Permutation tests on returns to scale and common production frontiers in nonparametric models
Rønn-Nielsen, Anders; Kronborg, Dorte; Asmild, Mette - 2022
Permutation techniques, where one recompute the test statistic over permutations of data, have a long history in statistics and have become increasingly useful as the availability of computational power has increased. Until now, no permutation tests for examining returns to scale assumptions,...
Persistent link: https://www.econbiz.de/10013346916
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Generalized information matrix tests for detecting model misspecification
Golden, Richard M.; Henley, Steven S.; White, Halbert; … - In: Econometrics 4 (2016) 4, pp. 1-24
Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies. In this paper, a unified GIMT framework is developed for the purpose of identifying,...
Persistent link: https://www.econbiz.de/10011755349
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Consistent estimation, model selection and averaging of dynamic panel data models with fixed effect
Li, Guangjie - 2009
In the context of an autoregressive panel data model with fixed effect, we examine the relationship between consistent parameter estimation and consistent model selection. Consistency in parameter estimation is achieved by using the tansformation of the fixed effect proposed by Lancaster (2002)....
Persistent link: https://www.econbiz.de/10010288764
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A correction function approach to solve the incidental parameter problem
Li, Guangjie; Leon-Gonzalez, Roberto - 2009
Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model....
Persistent link: https://www.econbiz.de/10010288792
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Relating Stochastic Volatility Estimation Methods
Bos, Charles S. - 2011
Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less widespread. In this article it is argued that one...
Persistent link: https://www.econbiz.de/10010325989
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Decomposing the composition effect
Rothe, Christoph - 2012
This paper proposes a decomposition of the composition effect, i.e. the part of the observed between-group difference in the distribution of some economic outcome that can be explained by differences in the distribution of covariates. Our decomposition contains three types of components: (i) the...
Persistent link: https://www.econbiz.de/10010282410
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Skewed logistic distribution for statistical temperature post-processing in mountainous areas
Gebetsberger, Manuel; Stauffer, Reto; Mayr, Georg J.; … - 2018
Non-homogeneous post-processing is often used to improve the predictive performance of probabilistic ensemble forecasts. A common quantity to develop, test, and demonstrate new methods is the near-surface air temperature frequently assumed to follow a Gaussian response distribution. However,...
Persistent link: https://www.econbiz.de/10011930743
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Towards the interpretation of time-varying regularization parameters in streaming penalized regression models
Zbonakova, Lenka; Pio Monti, Ricardo; Härdle, Wolfgang Karl - 2018
High-dimensional, streaming datasets are ubiquitous in modern applications. Examples range from nance and e-commerce to the study of biomedical and neuroimaging data. As a result, many novel algorithms have been proposed to address challenges posed by such datasets. In this work, we focus on the...
Persistent link: https://www.econbiz.de/10012433208