Finite Sample Efficiency of Ordinary Least Squares in the Linear Regression Model with Autocorrelated Errors

1980 ◽  
Vol 75 (372) ◽  
pp. 1005 ◽  
Author(s):  
Walter Kramer
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Ordinary Least Squares ◽  
Finite Sample ◽  
Autocorrelated Errors

Wouldn't It Be Nice …? The Automatic Unbiasedness of OLS (and GLS)

2008 ◽  
Vol 16 (3) ◽  
pp. 345-349 ◽  
Author(s):  
Robert C. Luskin
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Recent Issue ◽  
The Face ◽  
Best Linear Unbiased

In a recent issue of this journal, Larocca (2005) makes two notable claims about the best linear unbiasedness of ordinary least squares (OLS) estimation of the linear regression model. The first, drawn from McElroy (1967), is that OLS remains best linear unbiased in the face of a particular kind of autocorrelation (constant for all pairs of observations). The second, much larger and more heterodox, is that the disturbance need not be assumed uncorrelated with the regressors for OLS to be best linear unbiased. The assumption is unnecessary, Larocca says, because “orthogonality [of disturbance and regressors] is a property of all OLS estimates” (p. 192). Of course OLS's being best linear unbiased still requires that the disturbance be homoskedastic and (McElroy's loophole aside) nonautocorrelated, but Larocca also adds that the same automatic orthogonality obtains for generalized least squares (GLS), which is also therefore best linear unbiased, when the disturbance is heteroskedastic or autocorrelated.

scholarly journals ANALISIS JUMLAH KENDARAAN BERMOTOR DI DAERAH ISTIMEWA YOGYAKARTA (1990–2012)

KINERJA ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 68
Author(s):  
I Agus Wantara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Traffic Congestion ◽  
Ordinary Least Squares ◽  
T Test ◽  
Estimation Technique ◽  
The People ◽  
Log Linear

In the last few years, traffic congestions are often occurred in Yogyakarta. This situation is caused by the increasing number of vehicles in Yogyakarta.This study evaluates the effect of the gross regional domesticproduct (PDRB), the people of Daerah Istimewa Yogyakarta (JP), and region income (PD) to the number of vehicles in Daerah Istimewa Yogyakarta (JKB). The model consists of one behavioral equation: the number of vehicles equation. The estimation technique uses Ordinary Least Squares (OLS). MacKinnon, White, and Davidson test (MWD test) is used to choose between the two models: linear regression model or log-linearregression model.The sample covers observations for 23 years (1990 - 2012). The data are obtained from (1) Bank Indonesia (2) Badan Pusat Statistik DIY and various other sources. It is found that individually lnJP andlnPD are statistically significant (positive) except ln PDRB on the basis of (separate) t test. It is also found that on the basis of the F test collectively all the regressors have a significant effect on the regressand lnJKB.Keywords: the number of vehicles, traffic congestion, linear regression model, log-linear regression model.

scholarly journals Point and interval forecasts of electricity demand with Reg-SARMA models

2021 ◽  
Author(s):  
Ilaria Lucrezia Amerise ◽  
Agostino Tarsitano
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Moving Average ◽  
Current Model ◽  
Information Criterion ◽  
Ordinary Least Squares ◽  
Electricity Demand ◽  
Regression Residuals

AbstractThis paper deals especially with a two-stage approach to forecasting hourly electricity demand by using a linear regression model with serially correlated residuals. Firstly, ordinary least squares are applied to estimate a linear regression model based on purely deterministic predictors (essentially, polynomials in time and calendar dummy variables). In the case wherein the regression residuals are not a white noise series, a SARMA (seasonal autoregressive moving average) process is applied to the estimated regression residuals. After examining a vast set of potential representations, the stationary and invertible process associated with the smaller Akaike information criterion and the smaller Ljung–Box statistic is selected. Secondly, two sets of instrumental predictors are added to the current model: the estimated residuals of the first regression model plus the estimated errors of the chosen SARMA process. The new regression model is estimated by again using ordinary least squares, but taking advantage of the fact that the new regressors eliminate serial correlation. Practical issues in points and interval forecasting are illustrated with reference to nine-day ahead prediction performance for short-term electric loads in Italy.

scholarly journals A Treatise on Ordinary Least Squares Estimation of Parameters of Linear Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 518
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
C. Narayana ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Linear Models ◽  
Ordinary Least Squares ◽  
Linear Estimation ◽  
Estimation Of Parameters ◽  
Parametric Function ◽  
Ols Regression

This research article primarily focuses on the estimation of parameters of a linear regression model by the method of ordinary least squares and depicts Gauss-Mark off theorem for linear estimation which is useful to find the BLUE of a linear parametric function of the classical linear regression model. A proof of generalized Gauss-Mark off theorem for linear estimation has been presented in this memoir.  Ordinary Least Squares (OLS) regression is one of the major techniques applied to analyse data and forms the basics of many other techniques, e.g. ANOVA and generalized linear models [1]. The use of this method can be extended with the use of dummy variable coding to include grouped explanatory variables [2] and data transformation models [3]. OLS regression is particularly powerful as it relatively easy to check the model assumption such as linearity, constant, variance and the effect of outliers using simple graphical methods [4]. J.T. Kilmer et.al [5] applied OLS method to evolutionary and studies of algometry.  

ESTIMATING TRENDING VARIABLES IN THE PRESENCE OF FRACTIONALLY INTEGRATED ERRORS

2000 ◽  
Vol 16 (3) ◽  
pp. 324-346 ◽  
Author(s):  
Wen-Jen Tsay
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Error Term ◽  
Convergence Rates ◽  
Ordinary Least Squares ◽  
Spurious Regression

This paper considers the problems of estimation and inference in the linear regression model with fractionally integrated errors. The ordinary least squares (OLS) and the first differenced (FD) estimators are studied. Relative to the OLS estimators, a substantial increase in the convergence rates of the coefficient estimator for the stochastic regressor can be achieved by the FD estimators when the error term is nonstationary. However, the preceding decisive results can not always sustain when the error term is stationary. We also find that the FD estimators can eliminate the spurious regression because the FD t-ratio for the coefficient estimators never diverges.

scholarly journals Variation Comparison of OLS and GLS Estimators using Monte Carlo Simulation of Linear Regression Model with Autoregressive Scheme

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Sajid Ali Khan ◽  
Sayyad Khurshid ◽  
Tooba Akhtar ◽  
Kashmala Khurshid
Keyword(s):  
Monte Carlo ◽  
Linear Regression ◽  
Regression Model ◽  
Sample Size ◽  
Least Squares ◽  
Linear Regression Model ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Small Samples ◽  
Sample Sizes

In this research we discusses to Ordinary Least Squares and Generalized Least Squares techniques and estimate with First Order Autoregressive scheme from different correlation levels by using simple linear regression model. A comparison has been made between these two methods on the basis of variances results. For the purpose of comparison, we use simulation of Monte Carlo study and the experiment is repeated 5000 times. We use sample sizes 50, 100, 200, 300 and 500, and observe the influence of different sample sizes on the estimators. By comparing variances of OLS and GLS at different values of sample sizes and correlation levels with , we found that variance of ( ) at sample size 500, OLS and GLS gives similar results but at sample size 50 variance of GLS ( ) has minimum values as compared to OLS. So it is clear that variance of GLS ( ) is best. Similarly variance of ( ) from OLS and GLS at sample size 500 and correlation -0.05 with , GLS give minimum value as compared to all other sample sizes and correlations. By comparing overall results of Ordinary Least Squares (OLS) and Generalized Least Squares (GLS), we conclude that in large samples both are gives similar results but small samples GLS is best fitted as compared to OLS.

scholarly journals A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model

Stats ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 526-541
Author(s):  
Issam Dawoud ◽  
B. M. Golam Kibria
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Real Life ◽  
Multiple Linear Regression Model ◽  
Ordinary Least Squares ◽  
Least Squares Estimator ◽  
Linear Regression Models ◽  
Ordinary Least Squares Estimator

In a multiple linear regression model, the ordinary least squares estimator is inefficient when the multicollinearity problem exists. Many authors have proposed different estimators to overcome the multicollinearity problem for linear regression models. This paper introduces a new regression estimator, called the Dawoud–Kibria estimator, as an alternative to the ordinary least squares estimator. Theory and simulation results show that this estimator performs better than other regression estimators under some conditions, according to the mean squares error criterion. The real-life datasets are used to illustrate the findings of the paper.

Sign in / Sign up

Export Citation Format

Share Document