re[R-sig-finance] gression problem
You might try MLE, construct the liklihood function and then optimize it by experimenting different choices of parameters. I have doubt how LS estimation procedure can be employed here as parameters are nonlinear in nature
rechtsteiner wrote:
dear useRs, i'm working with a mean reverting model of the following specification: y = mu + beta(x - mu) + errorterm, where mu is a constant currently I estimate just y = x (with lm()) to get beta and then calculate mu = estimated intercept / (1-beta). but I'd like to estimate mu and beta together in one regression-step and also get the test-statistics (including parameter variance) for mu as well as for beta in the summary of the regression. could you please help me? thanks very much in advance! josuah
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