Relative Efficiency of Single-Outlier Discordancy Tests for Processing Geochemical Data on Reference Materials and Application to Instrumental Calibrations by a Weighted Least-Squares Linear Regression Model

In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected volumes of the confidence sets based on the feasible generalized least-squares estimator and the Stein rule estimator and discuss their ranking.

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The relative efficiency of a robust generalized Bayes estimator in a linear regression model with multicollinearity

Economics Letters ◽

10.1016/0165-1765(86)90138-2 ◽

1986 ◽

Vol 22 (1) ◽

pp. 33-38

Author(s):

Thomas B. Fomby ◽

R.Carter Hill

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Relative Efficiency ◽

Bayes Estimator ◽

Generalized Bayes ◽

Generalized Bayes Estimator

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On Least Squares Estimation in a Simple Linear Regression Model with Periodically Correlated Errors: A Cautionary Note

Austrian Journal of Statistics ◽

10.17713/ajs.v41i3.175 ◽

2016 ◽

Vol 41 (3) ◽

Cited By ~ 2

Author(s):

Abdullah A. Smadi ◽

Nour H. Abu-Afouna

Keyword(s):

Linear Regression ◽

Regression Model ◽

Least Squares ◽

Autoregressive Model ◽

Relative Efficiency ◽

Generalized Least Squares ◽

Correlated Errors ◽

Simple Linear Regression ◽

Simple Regression Model ◽

Autocorrelated Errors

In this research the simple linear regression (SLR) model with autocorrelated errors is considered. Traditionally, correlated errors are assumed to follow the autoregressive model of order one (AR(1)). Beside this model we will also study the SLR model with errors following the periodic autoregressive model of order one (PAR(1)). The later model is useful for modeling periodically autocorrelated errors. In particular, it is expected to beuseful when the data are seasonal. We investigate the properties of the least squares estimators of the parameters of the simple regression model when the errors are autocorrelated and for various models. In particular, the relative efficiency of those estimates are obtained and compared for the white noise, AR(1) and PAR(1) models. Also, the generalized least squares estimates for the SLR with PAR(1) errors are derived. The relative efficiency of the intercept and slope estimates based on both methods is investigated via Monte-Carlo simulation. An application on real data set is also provided.It should be emphasized that once there are sufficient evidences that errors are autocorrelated then the type of this autocorrelation should be uncovered. Then estimates of model’s parameters should be obtained accordingly, using some method like the generalized least squares but not the ordinary least squares.

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Extreme return, extreme volatility and investor sentiment

Filomat ◽

10.2298/fil1615949g ◽

2016 ◽

Vol 30 (15) ◽

pp. 3949-3961 ◽

Cited By ~ 7

Author(s):

Xu Gong ◽

Fenghua Wen ◽

Zhifang He ◽

Jia Yang ◽

Xiaoguang Yang ◽

...

Keyword(s):

Linear Regression ◽

Quantile Regression ◽

Regression Model ◽

Least Squares ◽

Linear Regression Model ◽

Investor Sentiment ◽

Generalized Least Squares ◽

Regression Methods ◽

Leading Role ◽

The Impact

The extreme return and extreme volatility have great influences on the investor sentiment in stock market. However, few researchers have taken the phenomenon into consideration. In this paper, we first distinguish the extreme situations from non-extreme situations. Then we use the ordinary generalized least squares and quantile regression methods to estimate a linear regression model by applying the standardized AAII, the return and volatility of SP 500. The results indicate that, except for extremely negative return, other return sequences can cause great changes in investor sentiment, and non-extreme return plays a leading role in affecting the overall American investor sentiment. Extremely positive (negative) return can rapidly improve (further reduce) the level of investor sentiment when investors encounter extremely pessimistic situations. The impact gradually decreases with improvement of the sentiment until the situation turns optimistic. In addition, we find that extreme and non-extreme volatility cannot a_ect the overall investor sentiment.

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