Abstract: This study focuses on Monte Carlo estimation of production function with policy relevance. The ordinary least square (OLS) method is used to estimate the unknown parameters. The Monte Carlo simulation methods are used for the data generating process. In tables 1 to 3, the mean square error (MSE) of 1 are 0.007678, 0.001972 and 0.001253 respectively for sample sizes 20, 40 and 80. Our finding showed that the mean square error (MSE) value varies with the sum of the powers of the input variables, the smaller the mean square error the lesser the viability and the better the estimator. In addition, use of parameters estimated to guide policy formulation to producing firms or industries is treated
Keywords: Cobb-Douglas model, Monte Carlo estimation, production function, returns to scale, Capital
[1]. Ashfag, A. and Muhammad, K. (2015) Estimating the Cobb-Douglas Production Function. International Journal of Research in Business Studies and Management, 2(5): 32 – 33.
[2]. Bhatia, H.L. (1994), Public Finance, 18th Edition. New Deihi: Vikas Publishing House PVT Limited.
[3]. Essi, I.D., 2002 "Econometric Models with Mis-specified Error Terms, "Abacus (Journal of the Mathematical Association of Nigeria), Vol 29 (2). 152 – 160.
[4]. Essi, I. D. (2009) Computing Leaf Rectangularity Index under Alternative Error Specifications AMSE Journal of Modeling C Vol. 70 (1), 67 – 79.
[5]. Essi, I.D., (2010) Computing Leaf Rectangularity Index: An Estimation Problem When the Parameter is a Norm of a vector. African Journal of Mathematics and Computer Science Research, 3 (7), 79-82.