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Maximum Likelihood Estimation of an ARMA(p,q) …
Maximum Likelihood Estimation of an ARMA(p,q) Model Constantino Hevia The World Bank. DECRG. October 2008. This note describes the Matlab function that computes the Maximum Likelihood estimates of a stationary ARMA(p,q) model. Problem: To t an ARMA(p,q) model to a vector of time series fy1 ; y2 ; :::; yT g with zero unconditional mean. An ARMA(p,q) process is given by yt = 1 yt 1 + ::: + p yt p + "t + 1 "t 1 + ::: + q "t q ;. where "t is an shock normally distributed with mean zero and variance 2 . If the original PT. series do not have zero mean, we rst construct y~t = yt s=1 ys =T and then t the ARMA. model to y~t . Usage: results = arma_mle(y,p,q,[info]). Arguments: y = vector of observed time series with mean zero. p = length of the autoregressive part (AR) of the ARMA model (integer). q = length of the moving average part (MA) of the ARMA model (integer). info = [optional] If info is not zero, the program prints information about the convergence of the optimization algorithm.
Maximum Likelihood Estimation of an ARMA(p,q) Model Constantino Hevia The World Bank. DECRG. October 2008 This note describes the Matlab function arma_mle.m that computes the maximum likelihood
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