Finite sample power of linear regression autocorrelation tests

1990 ◽  
Vol 43 (3) ◽  
pp. 363-372 ◽  
Author(s):  
Walter Krämer ◽  
Helmut Zeisel
Keyword(s):  
Linear Regression ◽  
Finite Sample

Testing Impact Measures in Spatial Autoregressive Models

2019 ◽  
Vol 43 (1-2) ◽  
pp. 40-75 ◽  
Author(s):  
Giuseppe Arbia ◽  
Anil K. Bera ◽  
Osman Doğan ◽  
Süleyman Taşpınar
Keyword(s):  
Linear Regression ◽  
Spatial Data ◽  
Regression Models ◽  
Delta Method ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Spatial Panel Data Models ◽  
Spatial Panel Data ◽  
Spatial Autoregressive ◽  
The Impact

Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel data models. In this article, we compare three procedures for testing the significance of impact measures in the spatial linear regression models. These procedures include (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulation method. In a Monte Carlo study, we compare the finite sample properties of these procedures.

scholarly journals Preliminary-Test Estimation of the Error Variance in Linear Regression

1987 ◽  
Vol 3 (2) ◽  
pp. 299-304 ◽  
Author(s):  
Judith A. Clarke ◽  
David E. A. Giles ◽  
T. Dudley Wallace
Keyword(s):  
Linear Regression ◽  
Mean Squared Error ◽  
Preliminary Test ◽  
Error Variance ◽  
Error Component ◽  
Quadratic Loss ◽  
Finite Sample ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Preliminary Test Estimation

We derive exact finite-sample expressions for the biases and risks of several common pretest estimators of the scale parameter in the linear regression model. These estimators are associated with least squares, maximum likelihood and minimum mean squared error component estimators. Of these three criteria, the last is found to be superior (in terms of risk under quadratic loss) when pretesting in typical situations.

scholarly journals Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 22 ◽  
Author(s):  
Pierre Perron ◽  
Yohei Yamamoto
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Structural Changes ◽  
Error Variance ◽  
Regression Coefficients ◽  
Linear Regression Models ◽  
Finite Sample ◽  
Finite Sample Properties ◽  
Joint Approach ◽  
Size Distortions

In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power.

Finite Sample Inference for Multiply Imputed Synthetic Data under a Multiple Linear Regression Model

2019 ◽  
Vol 71 (2) ◽  
pp. 63-82
Author(s):  
Martin D. Klein ◽  
John Zylstra ◽  
Bimal K. Sinha
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Synthetic Data ◽  
Multiple Linear Regression Model ◽  
Finite Sample ◽  
Current State ◽  
Finite Sample Inference ◽  
Multiply Imputed

In this article, we develop finite sample inference based on multiply imputed synthetic data generated under the multiple linear regression model. We consider two methods of generating the synthetic data, namely posterior predictive sampling and plug-in sampling. Simulation results are presented to confirm that the proposed methodology performs as the theory predicts and to numerically compare the proposed methodology with the current state-of-the-art procedures for analysing multiply imputed partially synthetic data. AMS 2000 subject classification: 62F10, 62F25, 62J05

ESTIMATION OF OPTIMAL PORTFOLIO WEIGHTS

2008 ◽  
Vol 11 (03) ◽  
pp. 249-276 ◽  
Author(s):  
YAREMA OKHRIN ◽  
WOLFGANG SCHMID
Keyword(s):  
Linear Regression ◽  
Empirical Study ◽  
Linear Regression Model ◽  
Asset Returns ◽  
Optimal Portfolio ◽  
Finite Sample ◽  
Portfolio Returns ◽  
The Third ◽  
Finite Sample Properties ◽  
Portfolio Return

The paper discusses finite sample properties of optimal portfolio weights, estimated expected portfolio return, and portfolio variance. The first estimator assumes the asset returns to be independent, while the second takes them to be predictable using a linear regression model. The third and the fourth approaches are based on a shrinkage technique and a Bayesian methodology, respectively. In the first two cases, we establish the moments of the weights and the portfolio returns. A consistent estimator of the shrinkage parameter for the third estimator is then derived. The advantages of the shrinkage approach are assessed in an empirical study.

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