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Given the following data sets from 1976 to 2014: X1 = Inflation Rate (%_ x3 = GDP Deflator (%) x2 = Consumer Price Index x4 = Real Interest Rates (%) GDP Deflator (%) Real Interest Rates (%) Inflation Rate (%) x1 Year Consumer Price Index x2 4.55 x3 X4 1976 9.2 8.3 3.41 1977 9.9 5 8.27 3.44 1978 7.33 5.37 9.33 2.44 1979 17.53 6.31 14.84 -0.73 1980 18.2 7.46 14.25 -0.22 1981 13.08 8.44 11.7 3.25 1982 10.22 9.3 8.7 8.66 1983 10.03 10.23 14.22 4.39 1984 50.34 15.39 53.34 -16.4 1985 23.1 18.94 17.63 9.33 1986 1.15 19.16 2.95 14.16 1987 4.07 19.94 7.5 5.43 1988 13.86 22.7 9.65 5.72 1989 12.24 25.48 9.03 9.39 1990 12.18 28.58 12.97 9.87 1991 19.26 34.09 16.53 5.62 1992 8.65 37.04 7.93 10.7 1993 6.72 39.53 6.83 7.35 1994 10.39 43.63 9.99 4.61 1995 6.83 46.61 7.55 6.63 1996 7.48 50.1 7.66 6.67 1997 5.59 52.9 6.22 9.46 1998 9.23 57.78 22.38 -4.58 1999 5.94 61.22 6.59 4.87 2000 3.98 63.65 5.71 4.92 2001 5.35 67.05 5.55 6.49 2002 2.72 68.88 4.16 4.78 2003 2.29 70.46 3.2 6.08 2004 4.83 73.86 5.52 4.32 2005 6.52 78.67 5.83 4.12 2006 5.49 82.99 4.95 4.6 2007 2.9 85.39 3.09 5.43 2008 8.26 92.45 7.55 1.12 2009 4.22 96.35 2.77 5.64 2010 3.79 100 4.22 3.31 2011 4.65 104.65 4.02 2.54 2012 3.17 107.97 1.97 3.64 2013 3 111.2 2.09 3.6 2014 4.1 115.77 3.21 2.25 || 2. Fit the regression model Y= a + B1X1 + B2 X2 + B3 X3 + I on your data set and fill-up the following table (round-off coefficients to 2 decimal places) using the STATA command: State your Regression Model v Coef Std. Err. ) ta P> It No. of obs = x14 ) R= Adjusted R2 x2) 11 Prob > E X3 ) cons Root MSE = Given the following data sets from 1976 to 2014: X1 = Inflation Rate (%_ x3 = GDP Deflator (%) x2 = Consumer Price Index x4 = Real Interest Rates (%) GDP Deflator (%) Real Interest Rates (%) Inflation Rate (%) x1 Year Consumer Price Index x2 4.55 x3 X4 1976 9.2 8.3 3.41 1977 9.9 5 8.27 3.44 1978 7.33 5.37 9.33 2.44 1979 17.53 6.31 14.84 -0.73 1980 18.2 7.46 14.25 -0.22 1981 13.08 8.44 11.7 3.25 1982 10.22 9.3 8.7 8.66 1983 10.03 10.23 14.22 4.39 1984 50.34 15.39 53.34 -16.4 1985 23.1 18.94 17.63 9.33 1986 1.15 19.16 2.95 14.16 1987 4.07 19.94 7.5 5.43 1988 13.86 22.7 9.65 5.72 1989 12.24 25.48 9.03 9.39 1990 12.18 28.58 12.97 9.87 1991 19.26 34.09 16.53 5.62 1992 8.65 37.04 7.93 10.7 1993 6.72 39.53 6.83 7.35 1994 10.39 43.63 9.99 4.61 1995 6.83 46.61 7.55 6.63 1996 7.48 50.1 7.66 6.67 1997 5.59 52.9 6.22 9.46 1998 9.23 57.78 22.38 -4.58 1999 5.94 61.22 6.59 4.87 2000 3.98 63.65 5.71 4.92 2001 5.35 67.05 5.55 6.49 2002 2.72 68.88 4.16 4.78 2003 2.29 70.46 3.2 6.08 2004 4.83 73.86 5.52 4.32 2005 6.52 78.67 5.83 4.12 2006 5.49 82.99 4.95 4.6 2007 2.9 85.39 3.09 5.43 2008 8.26 92.45 7.55 1.12 2009 4.22 96.35 2.77 5.64 2010 3.79 100 4.22 3.31 2011 4.65 104.65 4.02 2.54 2012 3.17 107.97 1.97 3.64 2013 3 111.2 2.09 3.6 2014 4.1 115.77 3.21 2.25 || 2. Fit the regression model Y= a + B1X1 + B2 X2 + B3 X3 + I on your data set and fill-up the following table (round-off coefficients to 2 decimal places) using the STATA command: State your Regression Model v Coef Std. Err. ) ta P> It No. of obs = x14 ) R= Adjusted R2 x2) 11 Prob > E X3 ) cons Root MSE =