A robust generalization and asymptotic properties of the model selection criterion family

2017 ◽  
Vol 47 (3) ◽  
pp. 532-547 ◽  
Author(s):  
Sumito Kurata ◽  
Etsuo Hamada
Keyword(s):  
Model Selection ◽  
Asymptotic Properties ◽  
Selection Criterion ◽  
Model Selection Criterion

scholarly journals Model Selection in a Composite Likelihood Framework Based on Density Power Divergence

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 270
Author(s):  
Elena Castilla ◽  
Nirian Martín ◽  
Leandro Pardo ◽  
Konstantinos Zografos
Keyword(s):  
Model Selection ◽  
Simulation Study ◽  
Asymptotic Properties ◽  
Selection Criterion ◽  
Composite Likelihood ◽  
Tuning Parameter ◽  
Density Power Divergence ◽  
Numerical Examples ◽  
Model Selection Criterion ◽  
Power Divergence

This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α . After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion.

scholarly journals Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

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