SAS/Ets User's Guide 136. Provides detailed reference material for using SAS/ETS software and guides you through the analysis and forecasting of features such as univariate and multivariate time series, cross-sectional time series, seasonal adjustments, multiequational nonlinear models, discrete choice models, limited dependent variable models, portfolio analysis, and generation of financial reports, with introductory and advanced examples for each procedure. You can also find complete information about two easy-to-use point-and-click applications: the Time Series Forecasting System, for automatic and interactive time series modeling and forecasting, and the Investment Analysis System, for time-value of money analysis of a variety of investments | 1342 F Chapter 19 The PANEL Procedure The constants 11 12 21 22 are given by 0 1 0 0 0 1 0 0 1 11 tri X X X Z0Z0Xl - tri IX Xj X P0X iX Xj X Z0Z0X 12 M - N - K trf xx 1 X0P0X 21 M - 2tr X0X 1 x0 Z0 z0x tri X0 x 1 X0 P0X 22 N - tr f X0 x 1 X0 P0X where tr is the trace operator on a square matrix. Solving this system produces the variance components. This method is applicable to balanced and unbalanced panels. However there is no guarantee of positive variance components. Any negative values are fixed equal to zero. Nerlove s Method The Nerlove method for estimating variance components can be obtained by setting VCOMP NL. The Nerlove method see Baltagi 1995 page 17 is assured to give estimates of the variance components that are always positive. Furthermore it is simple in contrast to the previous estimators. If yi is the ith fixed effect Nerlove s method uses the variance of the fixed effects as the estimate of 02. You have 02 PLj rN -1 where y is the mean fixed effect. The estimate of 02 is simply the residual sum of squares of the one-way fixed-effects regression divided by the number of observations. With the variance components in hand from any method the next task is to estimate the regression model of interest. For each individual you form a weight 0i as follows 0i 1 - 0 wi w2 Ti 02 02 where Ti is the ith cross section s time observations. Taking the 0i you form the partial deviations yit yit - Oiyi- Xit Xit - di Xi. where _yi. and X . are cross section means of the dependent variable and independent variables including the constant if any respectively. The random-effects is then the result of simple OLS on the transformed data. The Two-Way Random-Effects Model F 1343 The Two-Way Random-Effects Model The specification for the two-way random-effects model is uit Vi et it As in the one-way random-effects model the PANEL procedure provides four options for variance component estimators. Unlike the one-way random-effects model unbalanced panels present some .
