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    Ordinary least square estimation in regression analysis

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    OLS estimates and coefficients. See attached file for full problem description.
    I need help solving question #2. Please provide me with detailed explanations for #2.

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    Solution Preview

    Please see the attached file.

    The following regression was fitted by OLS , using 32 annual observations on time series data:
    Yt = 4.52 - 0.62 X1t, +0.93 X2t +0.61 X3t +0.16 X 4t Adjusted R2 = 0.638
    S.E (1.23) (0.28) (0.38) (0.21) (0.12) DW=0.61

    Note that Y = quantity of wheat exports from Canada
    X1 = Price of Canadian wheat in international market
    X2 = Quantity of Canadian wheat harvested
    X3 = GNP per capita in countries importing Canadian wheat
    X4 = price of barley in world market.
    (a) Test at 5% level of significance the hypothesis that, all else equal, the coefficient on GNP per capita is less than 1.

    Here the null hypothesis can be written as

    The test statistics is given by t =
    where b is the regression coefficient, se(b) is the standard error of the regression coefficient, N is the number of subjects, and k is the number of predictor variables. The resulting t is on N - k - 1 degrees of freedom.

    Here given that
    b3 = 0.61
    se(b3) ...

    Solution Summary

    Estimation of regression coefficients, Durbin Watson Statistic, Serial correlation in Ordinary least square estimation.