The main purpose of this article is to introduce the technique of panel data analysis in econometrics modeling. The elasticity of labour, capital and economic of scale for twenty two food manufacturing firms covering from 1989 to 1993 is estimated using the Cobb-Douglas model. The three main techniques of panel data analysis discussed are least square dummy variables (LSDV), analysis of covariance (ANCOVA) and generalized least square (GLS). Ordinary Least Square (OLS) method is included as the basis of comparison.
This paper investigates the confidence intervals of R2 MAD, the coefficient of determination based on
median absolute deviation in the presence of outliers. Bootstrap bias-corrected accelerated (BCa)
confidence intervals, known to have higher degree of correctness, are constructed for the mean and standard deviation of R2 MAD for samples generated from contaminated standard logistic distribution. The results indicate that by increasing the sample size and percentage of contaminants in the samples, and perturbing the location and scale of the distribution affect the lengths of the confidence intervals. The results obtained can also be used to verify the bound of R2 MAD.
The main objective of this paper is to explore the varying volatility dynamic of inflation rate in Malaysia for the period from January 1980 to December 2004. The GARCH, GARCH-Mean, EGARCH and EGARCH-Mean models are used to capture the stochastic variation and asymmetries in the economic instruments. Results show that the EGARCH model gives better estimates of sub-periods volatility. Further analysis using Granger causality test show that there is sufficient empirical evidence that higher inflation rate level will result in higher future inflation uncertainty and higher level of inflation uncertainty will lead to lower future inflation rate.