Estimating ARMAX(p,q) models by ESTIMATEA general ARMAX(p,q) model is (1) Y = X*beta + u, where Y is an n vector of regressand, X is an n*m matrix of m regressors, beta is m vector of regression coefficients, u is an n vector of residuals u(1),...,u(n) and the residual u(j) related to the j'th observation depends on p preceding residuals u(j-1),...,u(j-p) and q independent N(0,s^2) error terms e(j),e(j-1),...,e(j-q) as follows (2) u(j)=r(1)*u(j-1)+...+r(p)*u(j-p)+e(j)+a(1)*e(j-1)+...+a(q)*e(j-q), where r(1),...,r(p) and a(1),...,a(q) are unknown parameters. In fact, also non-linear models, where X*beta (the part of exogenous variables) is replaced by a more general function f(x,beta), may be estimated by the following method. A conditional maximum likelihood method (see e.g. Hamilton: Time Series Analysis, 1994, Chapter 5.6) will be applied. The conditional log likelihood function (with respect to first observations and error terms) is a sum of expressions (3) -0.5*(log(s^2)+e(j)^2/s^2+log(2*pi)) where e(j)'s are determined by (2) and u(j)'s are obtained from (1). Since e(j) depends on previous values e(j-1),...,e(j-q), during the estimation process the values of e variable have to be updated always when values of parameters to be estimated s,beta,r(1),...,r(p),a(1),...,a(q) are changed. To provide this feature in ESTIMATE, an extra variable (say EPS) must be defined in the original data set with initial values 0. Updating of e values is imposed by an UPDATE specification. The final values of e will be saved in the data set after the estimation. For example, in the model Y=b0+b1*X1+b2*X2+b3*X3+b4*X4+u, where u has the ARMA(3,1) structure, the ESTIMATE scheme is written as follows: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 11 *{A0}=Y-b0-b1*X1-b2*X2-b3*X3-b4*X4 12 *{AR1}=r1*(Y[-1]-b0-b1*X1[-1]-b2*X2[-1]-b3*X3[-1]-b4*X4[-1]) 13 *{AR2}=r2*(Y[-2]-b0-b1*X1[-2]-b2*X2[-2]-b3*X3[-2]-b4*X4[-2]) 14 *{AR3}=r3*(Y[-3]-b0-b1*X1[-3]-b2*X2[-3]-b3*X3[-3]-b4*X4[-3]) 15 *{EPS}={A0}-{AR1}-{AR2}-{AR3}-a1*EPS[-1] 16 *UPDATE=EPS 17 * 18 *MODEL ARMA 19 *LOGDENSITY=-0.5*(log(s*s)+({EPS})^2/(s*s)) 20 * 21 *ESTIMATE <data>,ARMA,CUR+1 22 * - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - The model ARMA (lines 18-19) is defined according to (3) and the {EPS} expression, e(j) solved from (2), is given on line 15. Various components of the model (in braces) are on lines 11-14. The specification UPDATE=EPS quarantees that ESTIMATE will update EPS according to expression {EPS} (line 15) and saves the final values as variable EPS. UPDATE admits even a list of variables which may be useful in more general models. Because the likelihood function has typically many local maxima, one has to be careful when selecting initial values for parameters. Good values for s and beta may be obtained from OLS estimation (by LINREG or REGDIAG). Initial values for ARMA parameters may be found by making a grid search (METHOD=G in ESTIMATE). 1 = More information about ESTIMATE operation

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