hi, using articial data, i'm supposed to estimate model y(t) = beta(1) + beta(2)*x(t) + u(t), u(t) = gamma*u(t-1) + v(t), t 1,...,100 which is correctly specified in two ways: ML ommiting the first observation, and ML using all 100 observation. since i'm still learning how to use R, i would like to know how MLE works. there is neither information about the distribution of v(t) nor if u(t) follows a stationary process. suppose that v(t) is normaly distributed - so we want to estimate beta(1), beta(2) and sigma2 (the variance of v(t)). thanks in advance! alexandre bonnet getulio vargas foundation, brazil [[alternative HTML version deleted]]
*hi,* *using articial data, i'm supposed to estimate model* *y(t) = beta(1) + beta(2)*x(t) + u(t), u(t) = gamma*u(t-1) + v(t), t 1,...,100* *which is correctly specified in two ways: ML ommiting the first observation, and ML using all 100 observation.* *since i'm still learning how to use R, i would like to know how MLE works.* *there is neither information about the distribution of v(t) nor if u(t) follows a stationary process.* *suppose that v(t) is normaly distributed - so we want to estimate beta(1), beta(2) and sigma2 (the variance of v(t)).* *thanks in advance!* *alexandre bonnet getulio vargas foundation, brazil* -- Alexandre Bonnet [[alternative HTML version deleted]]