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This Chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed. And a variety of different specifications and model evaluation tests due to various authors including Christoffersen and...
Persistent link: https://www.econbiz.de/10002432791
This paper outlines testing procedures for assessing the relative out-of-sample predictive accuracy of multiple conditional distribution models. The tests that are discussed are based on either the comparison of entire conditional distributions or the comparison of predictive confidence...
Persistent link: https://www.econbiz.de/10002433009
This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various authors including Christoffersen and...
Persistent link: https://www.econbiz.de/10014052432
This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, the authors first outline a simple simulation-based framework for constructing predictive densities for one-factor and stochastic volatility models....
Persistent link: https://www.econbiz.de/10014202226
If the intensity parameter in a jump diffusion model is identically zero, then parameters characterizing the jump size density cannot be identified. In general, this lack of identification precludes consistent estimation of identified parameters. Hence, it should be standard practice to...
Persistent link: https://www.econbiz.de/10014144974
Cross-validation is the most common data-driven procedure for choosing smoothing parameters in nonparametric regression. For the case of kernel estimators with iid or strong mixing data, it is well-known that the bandwidth chosen by cross-validation is optimal with respect to the average squared...
Persistent link: https://www.econbiz.de/10012969769
Self-reported survey data are often plagued by the presence of heaping. Accounting for this measurement error is crucial for the identification and consistent estimation of the underlying model (parameters) from such data. This paper introduces two Stata commands. The first command, heapmph,...
Persistent link: https://www.econbiz.de/10012914106