Problem 2 Consider the following regression model: log(y;) = 6o + Bilog(x1:) + B2x2i + B3x3i + u; (a) Is this model linear in parameters? Explain. (b) If we want to test the joint significance of the coefficients on x2 and x3, what are the null and alternative hypotheses of such test? Hint: since you cannot write Greek letters in R, you may use b or beta in your text to represent your regression parameters. (c) The variance of this regression's error term is 0.025. Is this information enough to assess CLRM Assumption V, on the constant/non-constant variance of the error term? (d) The correlation coefficient between variables x1 and x3 is 0.005. Should I be worried about a possible violation of CLRM Assumption VI?

Question
Problem 2
Consider the following regression model:
log(y;) = 6o + Bilog(x1:) + B2x2i + B3x3i + u;
(a) Is this model linear in parameters? Explain.
(b) If we want to test the joint significance of the coefficients on x2 and x3, what are the null and
alternative hypotheses of such test? Hint: since you cannot write Greek letters in R, you may
use b or beta in your text to represent your regression parameters.
(c) The variance of this regression's error term is 0.025. Is this information enough to assess CLRM
Assumption V, on the constant/non-constant variance of the error term?
(d) The correlation coefficient between variables x1 and x3 is 0.005. Should I be worried about a
possible violation of CLRM Assumption VI?
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Estimation and test of significance

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Transcribed Image Text

Problem 2 Consider the following regression model: log(y;) = 6o + Bilog(x1:) + B2x2i + B3x3i + u; (a) Is this model linear in parameters? Explain. (b) If we want to test the joint significance of the coefficients on x2 and x3, what are the null and alternative hypotheses of such test? Hint: since you cannot write Greek letters in R, you may use b or beta in your text to represent your regression parameters. (c) The variance of this regression's error term is 0.025. Is this information enough to assess CLRM Assumption V, on the constant/non-constant variance of the error term? (d) The correlation coefficient between variables x1 and x3 is 0.005. Should I be worried about a possible violation of CLRM Assumption VI?