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On the Omission of Intercept Term in Model Estimation: A Monte Carlo Simulation Study

Ijomah Maxwell Azubuike, Nwakuya Murren Tobechukwu



The issue of having an intercept term in linear regression model is an unsettled issue in the literature. It can be said that, generally, including the constant term depends on the research. But it must be known that the estimation of parameters differs according to involvement of the constant. If an intercept term exists in the model, the least squares estimate of the slope parameter will be unbiased. In this paper, we compare the results of models that include intercept and do not include intercept term on hypothetic data sets. The approaches are demonstrated via both simulation studies. Simulation-based investigation is carried out under various different scenarios using SAS 9.0 software. Results from the simulation study will be presented. We will discuss scenarios in which case they are advantageous.

Keywords: Intercept, linear regression, zero-constant, slope parameter, collinearity

Cite this Article

Ijomah Maxwell Azubuike, Nwakuya Murren Tobechukwu. On the Omission of Intercept Term in Model Estimation: A Monte Carlo Simulation Study. Research & Reviews: Journal of Statistics. 2020; 9(1): 1-8p.


Intercept; linear regression; zero-constant;

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Ding CS. Using regression mixture analysis in educational research. Practical Assessment, Research, and Evaluation. 2006;11(1):11.

El-Salam MEFA. An efficient estimation procedure for determining ridge regression parameter. Asian J. Maths, Stat. 2011;4(2):90–97.

Khan S, Hoque Z, Saleh AK. Estimation of the slope parameter for linear regression model with uncertain prior information. Journal of Statistical Research. 2002;36:55–73.

Be1s1ey DA. Demeaning Conditioning Diagnostics through Centering. The American Statistician. 1984;3892:73–77.

Casella G. Leverage and regression through the origin. The American Statistician. 1983;37(2):147–152.

Kelley J, Evans MDR, Lowman J, Lykes V. Group-mean-centering independent variables in multi-level models is dangerous. Quality & Quantity. 2017;51(1):261–283.

Theil H. Introduction to Econometrics, No. 04; HB139, T4. Prentice Hall, Englewood Cliffs: N.J. 1978.

Hocking RR. Methods and Applications of Linear Models: Regression and the Analysis of Variance. John Wiley & Sons; 2013.

Simon SD, Lesage JP. Benchmarking numerical accuracy of statistical algorithms. Computational Statistics & Data Analysis. 1988;7(2):197–209.

Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied Linear Statistical Models, Vol. 5. New York: McGraw-Hill Irwin. 2005.

Myers RH, Myers RH. Classical and Modern Regression with Applications, Vol. 2. Belmont, CA: Duxbury press. 1990.

Kozak A, Kozak RA. Notes on regression through the origin. The Forestry Chronicle. 1995;71(3):326–330.

Montgomery DC, Peck EA. Induction to linear regression analysis. Second edition. New York: John Wiley; 1992. p. 527.

Kmenta J. Elements of econometrics. 2nd edition Macmillan. New York. American Journal of Agricultural Economics. 1986;70(1):1998.

Gordon HA. Errors in computer packages. Least squares regression through the origin. Journal of the Royal Statistical Society: Series D (The Statistician). 1981;30(1):23–29.

Kvålseth TO. Cautionary note about R2. The American Statistician. 1985;39(4):279–285.

Marquardt DW, Snee RD. Test statistics for mixture models. Technometrics. 1974;16(4):533–537.

Maddala GS. Disequilibrium, Self-Selection, and switching Models. Handbook of Econometrics. 1986;3:1633–1688.

Gordon RA. Regression Analysis for the Social Sciences. Routledge. 2015.

Hahn GJ. Fitting regression models with no intercept term. Journal of Quality Technology. 1977;9(2):56–61.



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