Misspecification Testing in a Class of Conditional Distributional Models
نویسندگان
چکیده
منابع مشابه
Misspecification Testing in a Class of Conditional Distributional Models
Misspecification Testing in a Class of Conditional Distributional Models We propose a specification test for a wide range of parametric models for the conditional distribution function of an outcome variable given a vector of covariates. The test is based on the Cramer-von Mises distance between an unrestricted estimate of the joint distribution function of the data, and a restricted estimate t...
متن کاملwww.econstor.eu Misspecification Testing in GARCH-MIDAS Models
We develop a misspecification test for the multiplicative two-component GARCHMIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly timevarying long-term component which is driven by the dynamics of a macroeconomic explanatory variable. We suggest a Lagrange Multiplier statistic for testing the null hypothes...
متن کاملTesting Models or Fitting Models? Identifying Model Misspecification in PLS
Partial Least Squares (PLS) is a statistical technique that is widely used in the Information Systems discipline to estimate statistical models with structural equations and latent variables. While PLS does not provide a statistical test of model fit to data, its proponents have suggested a set of criteria that good PLS models should fulfill. Conversely, when a model does not satisfy these crit...
متن کاملTesting Conditional Factor Models∗
We develop a methodology for estimating time-varying alphas and factor loadings based on nonparametric techniques. We test whether conditional alphas and long-run alphas, which are averages of conditional alphas, are equal to zero and derive test statistics for the constancy of factor loadings. The tests can be performed for a single asset or jointly across portfolios. The traditional Gibbons, ...
متن کاملTesting for misspecification in generalized linear mixed models.
Generalized linear mixed models have become a frequently used tool for the analysis of non-Gaussian longitudinal data. Estimation is often based on maximum likelihood theory, which assumes that the underlying probability model is correctly specified. Recent research shows that the results obtained from these models are not always robust against departures from the assumptions on which they are ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2013
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2012.736903