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Lab_Six_Recap_Takehome_One

# Dev skewness kurtosis 120 80 40 116e 11 0010179

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Unformatted text preview: 6101.095 0.000000 0 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 Orthogonal but not normal 28 Save the residuals from the model as Save resdm resdm 29 Transform dhoust i.e. [Bx /Ax ]*y(t)=h(z)*[Bx /Ax ]*x(t) + [Bx /Ax ]* e(t), Or w(t) = h(z)* wnx (t) + resid(t) Bdmortg *dhoust = h(z) *wndmortg + error(t) error(t) dmortg *dhoust dmortg B = [1 – 0.538z + 0.319z2 ] 30 31 W: trace W 800 600 400 200 0 -200 -400 -600 -800 1975 1980 1985 1990 1995 2000 2005 32 2010 W: histogram 80 Series: W Sample 1971M04 2013M12 O b s e r va t io n s 5 0 1 70 60 Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis 40 30 20 10 - 1.505425 2.044000 624.9830 - 725.6230 156.0426 - 0.056943 4.696756 Jarque-Bera Probability 50 60.36950 0.000000 0 -600 -400 -200 0 200 400 600 33 W: correlogram 34 Schematic: bivariate model of y(t) x(t) h(z) h(z) black box What are the lags? What are the weights? + Univariate model Of y + Ay (z)/ By (z) ey (t) y(t) 35 W: unit root test 36 Two time series: W and resdm W 800 400 0 -400 -800 75 80 85 90 95 00 05 10 RE...
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