73,240 research outputs found
Estimating the intercept in an orthogonally blocked experiment when the block effects are random.
Abstract: For an orthogonally blocked experiment, Khuri (1992) has shown that the ordinary least squares estimator and the generalized least squares estimator of the factor effects in a response surface model with random block effects coincide. However, the equivalence does not hold for the estimation of the intercept when the block sizes are heterogeneous. When the block sizes are homogeneous, ordinary and generalized least squares provide an identical estimate for the intercept.Effects;
Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models
This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define a generalized two-stages least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their joint asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.higher-order spatial dependence, generalized moments estimation, two-stages least squares, asymptotic statistics
Inference on Time-Invariant Variables using Panel Data: A Pre-Test Estimator with an Application to the Returns to Schooling
This paper proposes a new pre-test estimator of panel data models including time invariant variables based upon the Mundlak-Krishnakumar estimator and an "unrestricted” Hausman-Taylor estimator. The paper evaluates the biases of currently used restricted estimators, omitting the average-over-time of at least one endogenous time-varying explanatory variable. Repeated Between, Ordinary Least Squares, Two stage restricted Between and Oaxaca-Geisler estimator, Fixed Effect Vector Decomposition, Generalized least squares may lead to wrong conclusions regarding the statistical significance of the estimated parameter values of time-invariant variables.Time-Invariant Variables, Panel data, Time-Series Cross-Sections, Pre-Test Estimator, Mundlak Estimator, Fixed Effects Vector Decomposition
Cancerphobia: Electromagnetic Fields and Their Impact on Residential Loan Values
This article provides a matrix representation of the adjustment grid estimator. From this representation, one can invoke the Gauss-Mrkov theorem to examine the efficiency of ordinary least squares (OLS) and the grid estimator that uses OLS estimates of the adjustments (the "plug-in" grid method). In addition, this matrix representation suggests a generalized least squares version of the grid method, labeled herin as the total grid estimator. Based on the empirical experiments, the total grid estimator outperformed the plug-in grid estimator, which in turn outperformed the OLS.
MELE: MAXIMUM ENTROPY LEUVEN ESTIMATORS
Multicollinearity hampers empirical econometrics. The remedies proposed to date suffer from pitfalls of their own. The ridge estimator is not generally accepted as a vital alternative to the ordinary least-squares (OLS) estimator because it depends upon unknown parameters. The generalized maximum entropy (GME) estimator of Golan, Judge and Miller depends upon subjective exogenous information that affects the estimated parameters in an unpredictable way. This paper presents novel maximum entropy estimators inspired by the theory of light that do not depend upon any additional information. Monte Carlo experiments show that they are not affected by any level of multicollinearity and dominate OLS uniformly. The Leuven estimators are consistent and asymptotically normal.multicollinearity, mean squared error, ordinary least squares, generalized maximum entropy, Research Methods/ Statistical Methods, C2,
A Semi-Parametric Bayesian Generalized Least Squares Estimator
In this paper we propose a semi-parametric Bayesian Generalized Least Squares
estimator. In a generic GLS setting where each error is a vector, parametric
GLS maintains the assumption that each error vector has the same covariance
matrix. In reality however, the observations are likely to be heterogeneous
regarding their distributions. To cope with such heterogeneity, a Dirichlet
process prior is introduced for the covariance matrices of the errors, leading
to the error distribution being a mixture of a variable number of normal
distributions. Our methods let the number of normal components be data driven.
Two specific cases are then presented: the semi-parametric Bayesian Seemingly
Unrelated Regression (SUR) for equation systems; as well as the Random Effects
Model (REM) and Correlated Random Effects Model (CREM) for panel data. A series
of simulation experiments is designed to explore the performance of our
methods. The results demonstrate that our methods obtain smaller posterior
standard deviations than the parametric Bayesian GLS. We then apply our
semi-parametric Bayesian SUR and REM/CREM methods to empirical examples.Comment: 32 pages, 2 figures, 18 table
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