Improved Output Gap Estimates and Forecasts Using a Local Linear Regression
نویسندگان
چکیده
منابع مشابه
Improved double kernel local linear quantile regression
As sample quantiles can be obtained as maximum likelihood estimates of location parameters in suitable asymmetric Laplace distributions, so kernel estimates of quantiles can be obtained as maximum likelihood estimates of location parameters in a general class of distributions with simple exponential tails. In this paper, this observation is applied to kernel quantile regression. In so doing, a ...
متن کاملFuzzy estimates of regression parameters in linear regression models for imprecise input and output data
The method for obtaining the fuzzy estimates of regression parameters with the help of “Resolution Identity” in fuzzy sets theory is proposed. The -level least-squares estimates can be obtained from the usual linear regression model by using the -level real-valued data of the corresponding fuzzy input and output data. The membership functions of fuzzy estimates of regression parameters will be ...
متن کاملLiu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors
In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcom...
متن کاملLocal Linear Spatial Regression
A local linear kernel estimator of the regression function x 7→ g(x) := E[Yi|Xi = x], x ∈ R , of a stationary (d+1)-dimensional spatial process {(Yi,Xi), i ∈ Z } observed over a rectangular domain of the form In := {i = (i1, . . . , iN ) ∈ Z N |1 ≤ ik ≤ nk, k = 1, . . . ,N}, n = (n1, . . . , nN ) ∈ Z N , is proposed and investigated. Under mild regularity assumptions, asymptotic normality of th...
متن کاملNonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness
Gaussian processes using nonstationary covariance functions are a powerful tool for Bayesian regression with input-dependent smoothness. A common approach is to model the local smoothness by a latent process that is integrated over using Markov chain Monte Carlo approaches. In this paper, we show that a simple approximation that uses the estimated mean of the local smoothness yields good result...
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: Engineering Proceedings
سال: 2021
ISSN: 2673-4591
DOI: 10.3390/engproc2021005032