Find a transformation of data to be able to use same data

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Reference no: EM13156830

we have a true model Yt = b0 + b1Xt + b2Yt + Et where the error term follows a weird serial correlation: Et = Et-1 + Ut. Ut is perfectly well-behaved random variable with 0 mean and constant variance. cov(Ut,Us) = 0 when t is different from s.

a. state the consequences of estimating this model using OLS? what kind of Gauss-Markow assumptions that it happens to violate.

b. find a transformation of the data to be able to use the same data to estimate a model that satisfies the Gauss-Markov assumptions. be clear and explicit about the process. clearly explain why the transformed model meets the Gauss-markov assumption.

this kind of model is random walk serial correlation.

Reference no: EM13156830

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