Consider the following regression output: Pt = 0.2033 + 0.656Xt se = (0.0976) (0.1961) r² = 0.397 RSS = 0.0544 ESS = 0.0358 Where: Y = labor force participation rate (LFPR) of women in 1972 and X = LFPR of women in 1968. The regression results were obtained from a sample of 19 cities in the United States.

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Test the hypothesis: H0: β2 = 1 against H1: β2 > 1. Which test do you use? And why? What are the underlying assumptions of the test(s) you use?
Consider the following regression output:
Ŷt
= 0.2033 +0.656Xt
se (0.0976) (0.1961)
r² = 0.397 RSS=0.0544 ESS = 0.0358
Where:
Y = labor force participation rate (LFPR) of women in 1972 and
X = LFPR of women in 1968. The regression results were obtained from a sample of 19 cities in the
United States.
Transcribed Image Text:Consider the following regression output: Ŷt = 0.2033 +0.656Xt se (0.0976) (0.1961) r² = 0.397 RSS=0.0544 ESS = 0.0358 Where: Y = labor force participation rate (LFPR) of women in 1972 and X = LFPR of women in 1968. The regression results were obtained from a sample of 19 cities in the United States.
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