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Data X:
2293.41 10430.35 9374.63 -18.2 -11 3.3 -0.8 2443.27 2513.17 2466.92 2502.66 2070.83 9691.12 8679.75 -22.8 -17 3.47 -1.7 2293.41 2443.27 2513.17 2466.92 2029.6 9810.31 8593 -23.6 -18 3.72 -1.1 2070.83 2293.41 2443.27 2513.17 2052.02 9304.43 8398.37 -27.6 -19 3.67 -0.4 2029.6 2070.83 2293.41 2443.27 1864.44 8767.96 7992.12 -29.4 -22 3.82 0.6 2052.02 2029.6 2070.83 2293.41 1670.07 7764.58 7235.47 -31.8 -24 3.85 0.6 1864.44 2052.02 2029.6 2070.83 1810.99 7694.78 7690.5 -31.4 -24 3.9 1.9 1670.07 1864.44 2052.02 2029.6 1905.41 8331.49 8396.2 -27.6 -20 3.99 2.3 1810.99 1670.07 1864.44 2052.02 1862.83 8460.94 8595.56 -28.8 -25 4.35 2.6 1905.41 1810.99 1670.07 1864.44 2014.45 8531.45 8614.55 -21.9 -22 4.98 3.1 1862.83 1905.41 1810.99 1670.07 2197.82 9117.03 9181.73 -13.9 -17 5.46 4.7 2014.45 1862.83 1905.41 1810.99 2962.34 12123.53 11114.08 -8 -9 5.19 5.5 2197.82 2014.45 1862.83 1905.41 3047.03 12989.35 11530.75 -2.8 -11 5.03 5.4 2962.34 2197.82 2014.45 1862.83 3032.6 13168.91 11322.38 -3.3 -13 5.38 5.9 3047.03 2962.34 2197.82 2014.45 3504.37 14084.6 12056.67 -1.3 -11 5.37 5.8 3032.6 3047.03 2962.34 2197.82 3801.06 13995.33 12812.48 0.5 -9 4.87 5.2 3504.37 3032.6 3047.03 2962.34 3857.62 13357.7 12656.63 -1.9 -7 4.7 4.2 3801.06 3504.37 3032.6 3047.03 3674.4 12602.93 12193.88 2 -3 4.4 4.4 3857.62 3801.06 3504.37 3032.6 3720.98 13547.84 12419.57 1.7 -3 4.37 3.6 3674.4 3857.62 3801.06 3504.37 3844.49 13731.31 12538.12 1.9 -6 4.54 3.5 3720.98 3674.4 3857.62 3801.06 4116.68 15532.18 13406.97 0.1 -4 4.8 3.1 3844.49 3720.98 3674.4 3857.62 4105.18 15543.76 13200.58 2.4 -8 4.56 2.9 4116.68 3844.49 3720.98 3674.4 4435.23 16903.36 13901.28 2.3 -1 4.61 2.2 4105.18 4116.68 3844.49 3720.98 4296.49 16235.39 13557.69 4.7 -2 4.58 1.5 4435.23 4105.18 4116.68 3844.49 4202.52 16460.95 13239.71 5 -2 4.61 1.1 4296.49 4435.23 4105.18 4116.68 4562.84 17974.77 13673.28 7.2 -1 4.77 1.4 4202.52 4296.49 4435.23 4105.18 4621.4 18001.37 13480.21 8.5 1 4.76 1.3 4562.84 4202.52 4296.49 4435.23 4696.96 17611.14 13407.75 6.8 2 4.5 1.3 4621.4 4562.84 4202.52 4296.49 4591.27 17460.53 12754.8 5.8 2 4.37 1.8 4696.96 4621.4 4562.84 4202.52 4356.98 17128.37 12268.53 3.7 -1 4.15 1.8 4591.27 4696.96 4621.4 4562.84 4502.64 17741.23 12631.48 4.8 1 4.24 1.8 4356.98 4591.27 4696.96 4621.4 4443.91 17286.32 12512.89 6.1 -1 4.22 1.7 4502.64 4356.98 4591.27 4696.96 4290.89 16775.08 12377.62 6.9 -8 4.01 1.6 4443.91 4502.64 4356.98 4591.27 4199.75 16101.07 12185.15 5.7 1 3.93 1.5 4290.89 4443.91 4502.64 4356.98 4138.52 16519.44 11963.12 6.9 2 3.97 1.2 4199.75 4290.89 4443.91 4502.64 3970.1 15934.09 11533.59 5.5 -2 3.92 1.2 4138.52 4199.75 4290.89 4443.91 3862.27 15786.78 11257.35 6.5 -2 3.99 1.6 3970.1 4138.52 4199.75 4290.89 3701.61 15147.55 11036.89 7.7 -2 4.1 1.6 3862.27 3970.1 4138.52 4199.75 3570.12 14990.31 10997.97 6.3 -2 4.04 1.9 3701.61 3862.27 3970.1 4138.52 3801.06 16397.83 11333.88 5.5 -6 3.97 2.2 3570.12 3701.61 3862.27 3970.1 3895.51 17232.97 11234.68 5.3 -4 3.9 2 3801.06 3570.12 3701.61 3862.27 3917.96 16311.54 11145.65 3.3 -5 3.66 1.7 3895.51 3801.06 3570.12 3701.61 3813.06 16187.64 10971.19 2.2 -2 3.44 2.4 3917.96 3895.51 3801.06 3570.12 3667.03 16102.64 10872.48 0.6 -1 3.27 2.6 3813.06 3917.96 3895.51 3801.06 3494.17 15650.83 10827.81 0.2 -5 3.24 2.9 3667.03 3813.06 3917.96 3895.51 3363.99 14368.05 10695.25 -0.7 -9 3.27 2.6 3494.17 3667.03 3813.06 3917.96 3295.32 13392.79 10324.31 -1.7 -8 2.99 2.5 3363.99 3494.17 3667.03 3813.06 3277.01 12986.62 10532.54 -3.7 -14 2.77 3.2 3295.32 3363.99 3494.17 3667.03 3257.16 12204.98 10554.27 -7.6 -10 2.9 3.1 3277.01 3295.32 3363.99 3494.17 3161.69 11716.87 10545.38 -8.2 -11 2.87 3.1 3257.16 3277.01 3295.32 3363.99 3097.31 11402.75 10486.64 -7.5 -11 2.84 2.9 3161.69 3257.16 3277.01 3295.32 3061.26 11082.38 10377.18 -8 -11 3.02 2.5 3097.31 3161.69 3257.16 3277.01 3119.31 11395.64 10283.19 -6.9 -5 3.19 2.8 3061.26 3097.31 3161.69 3257.16 3106.22 11809.38 10682.06 -4.2 -2 3.39 3.1 3119.31 3061.26 3097.31 3161.69 3080.58 11545.71 10723.78 -3.6 -3 3.28 2.6 3106.22 3119.31 3061.26 3097.31 2981.85 11394.84 10539.51 -1.8 -6 3.28 2.3 3080.58 3106.22 3119.31 3061.26 2921.44 11068.05 10673.38 -3.2 -6 3.33 2.3 2981.85 3080.58 3106.22 3119.31 2849.27 10973 10411.75 -1.3 -7 3.51 2.6 2921.44 2981.85 3080.58 3106.22 2756.76 11028.93 10001.6 0.6 -6 3.65 2.9 2849.27 2921.44 2981.85 3080.58 2645.64 11079.42 10204.59 1.2 -2 3.76 2 2756.76 2849.27 2921.44 2981.85 2497.84 10989.34 10032.8 0.4 -2 3.67 2.2 2645.64 2756.76 2849.27 2921.44 2448.05 11383.89 10152.09 3 -4 3.87 2.4 2497.84 2645.64 2756.76 2849.27 2454.62 11527.72 10364.91 -0.4 0 3.99 2.3 2448.05 2497.84 2645.64 2756.76 2407.6 11037.54 10092.96 0 -6 3.9 2.6 2454.62 2448.05 2497.84 2645.64 2472.81 11950.95 10418.4 -1.3 -4 3.74 1.9 2407.6 2454.62 2448.05 2497.84 2408.64 11441.08 10323.73 -3.1 -3 3.55 1.1 2472.81 2407.6 2454.62 2448.05 2440.25 10631.92 10601.61 -4 -1 3.67 1.3 2408.64 2472.81 2407.6 2454.62 2350.44 10892.76 10540.05 -4.9 -3 3.6 1.6 2440.25 2408.64 2472.81 2407.6 2196.72 10295.98 10124.63 -4.6 -6 3.82 1.7 2350.44 2440.25 2408.64 2472.81 2174.56 10205.29 9762.12 -5.4 -6 3.91 1.9 2196.72 2350.44 2440.25 2408.64 2120.88 10717.13 9682.35 -8.1 -15 3.79 1.6 2174.56 2196.72 2350.44 2440.25 2093.48 10637.44 9492.49 -9.4 -5 3.73 1.8 2120.88 2174.56 2196.72 2350.44 2061.41 9884.59 9284.73 -12.6 -11 3.77 1.8 2093.48 2120.88 2174.56 2196.72 1969.6 9676.31 9154.34 -15.7 -13 3.47 1.5 2061.41 2093.48 2120.88 2174.56 1959.67 8895.71 9098.03 -17.3 -10 3.18 1.6 1969.6 2061.41 2093.48 2120.88 1910.43 8145.82 8623.36 -14.4 -9 3.44 1 1959.67 1969.6 2061.41 2093.48 1833.42 7905.84 8334.59 -16.2 -11 3.81 1.5 1910.43 1959.67 1969.6 2061.41 1635.25 8169.75 7977.64 -14.9 -18 3.6 1.8 1833.42 1910.43 1959.67 1969.6 1765.9 8538.47 7916.13 -11 -13 3.42 1.7 1635.25 1833.42 1910.43 1959.67 1946.81 8570.73 8474.21 -11.5 -9 3.73 1.2 1765.9 1635.25 1833.42 1910.43 1995.37 8692.94 8526.63 -9.6 -8 4.04 1.4 1946.81 1765.9 1635.25 1833.42 2042 8721.14 8641.21 -8.8 -4 4.22 1.1 1995.37 1946.81 1765.9 1635.25 1940.49 8792.5 8048.1 -9.7 -3 4.3 1.3 2042 1995.37 1946.81 1765.9 2065.81 9354.01 8160.67 -8.4 -3 4.28 1.3 1940.49 2042 1995.37 1946.81 2214.95 9751.2 8685.4 -8.4 -3 4.56 1.3 2065.81 1940.49 2042 1995.37 2304.98 10352.27 8616.49 -6.8 -1 4.79 1.3 2214.95 2065.81 1940.49 2042 2555.28 10965.88 9492.44 -5.3 0 4.93 0.9 2304.98 2214.95 2065.81 1940.49 2799.43 11717.46 10080.48 -5.1 1 5.12 1.3 2555.28 2304.98 2214.95 2065.81 2811.7 11384.49 10179.35 -6.5 0 5.13 1.8 2799.43 2555.28 2304.98 2214.95 2735.7 11448.79 10500.98 -7.3 2 5.15 2.7 2811.7 2799.43 2555.28 2304.98 2745.88 9981.65 9892.56 -10.8 1 4.92 2.6 2735.7 2811.7 2799.43 2555.28 2720.25 10300.79 9923.81 -10.9 -1 4.79 2.9 2745.88 2735.7 2811.7 2799.43 2638.53 10496.2 9978.53 -13.4 -8 4.68 2.2 2720.25 2745.88 2735.7 2811.7 2659.81 10511.22 9721.84 -15.5 -18 4.42 2.1 2638.53 2720.25 2745.88 2735.7 2641.65 10438.9 9220.75 -15.4 -14 4.53 2.3 2659.81 2638.53 2720.25 2745.88 2604.42 9996.83 9042.56 -11.9 -4 4.71 2.3 2641.65 2659.81 2638.53 2720.25 2892.63 11576.21 10314.68 -8 0 4.83 2.7 2604.42 2641.65 2659.81 2638.53 2915.02 12151.11 10444.5 -7.7 4 5.04 2.6 2892.63 2604.42 2641.65 2659.81 2845.26 12974.89 10767.2 -6.4 4 5.06 2.9 2915.02 2892.63 2604.42 2641.65 2794.83 13975.55 10546.82 -5.6 3 5.14 3.1 2845.26 2915.02 2892.63 2604.42 2848.96 13411.84 10213.97 -5.7 3 5.06 2.8 2794.83 2845.26 2915.02 2892.63 2833.18 12708.47 10052.6 -0.1 7 5.04 2.1 2848.96 2794.83 2845.26 2915.02 2995.55 13266.27 10777.22 1.9 8 5.19 2.3 2833.18 2848.96 2794.83 2845.26 2987.1 13720.95 10682.74 3.6 13 5.22 2.2 2995.55 2833.18 2848.96 2794.83 3013.24 14452.93 10666.71 5 15 5.4 2.5 2987.1 2995.55 2833.18 2848.96 3110.52 14760.87 10665.78 4.7 14 5.7 3.1 3013.24 2987.1 2995.55 2833.18 3045.78 15311.7 10433.56 5.1 14 5.61 3 3110.52 3013.24 2987.1 2995.55 3032.93 16153.34 10967.87 6.6 10 5.66 3.4 3045.78 3110.52 3013.24 2987.1 3142.95 16329.89 11014.51 6 16 5.65 2.9 3032.93 3045.78 3110.52 3013.24 3012.61 16973.38 10654.41 6.2 13 5.63 2.8 3142.95 3032.93 3045.78 3110.52 2897.06 16969.28 10582.92 8.6 15 5.5 2.7 3012.61 3142.95 3032.93 3045.78 2863.36 17039.97 10580.27 7.4 13 5.61 2.2 2897.06 3012.61 3142.95 3032.93 2882.6 19598.93 10947.43 8.6 12 5.3 2.1 2863.36 2897.06 3012.61 3142.95 2767.63 19834.71 10483.39 9.2 13 5.38 2.2 2882.6 2863.36 2897.06 3012.61 2803.47 19685.53 10539.68 7.7 11 5.5 1.9 2767.63 2882.6 2863.36 2897.06 3030.29 18941.6 11281.26 6.4 9 5.35 1.8 2803.47 2767.63 2882.6 2863.36 3210.52 18409.96 11251.2 8.6 8 4.99 1.9 3030.29 2803.47 2767.63 2882.6 3249.57 18470.97 10817.9 6.4 8 4.93 1.5 3210.52 3030.29 2803.47 2767.63 2999.93 17677.9 10394.48 6 5 5.16 1.3 3249.57 3210.52 3030.29 2803.47 3181.96 17544.22 10714.03 2.6 3 4.87 1.2 2999.93 3249.57 3210.52 3030.29 3053.05 17671 10935.47 0.1 -2 4.73 0.9 3181.96 2999.93 3249.57 3210.52 3092.71 18033.25 11052.23 0 0 4.4 0.7 3053.05 3181.96 2999.93 3249.57 3165.26 17135.96 10704.02 -0.9 -8 3.99 0.7 3092.71 3053.05 3181.96 2999.93 3173.95 16505.21 10853.87 -0.1 2 3.67 0.8 3165.26 3092.71 3053.05 3181.96 3280.37 16666.97 10443.5 -1.4 2 3.65 1.2 3173.95 3165.26 3092.71 3053.05 3288.18 15418.03 9753.63 -7.1 2 3.75 1.2 3280.37 3173.95 3165.26 3092.71 3411.13 14153.22 9327.78 -6.2 3 3.67 1 3288.18 3280.37 3173.95 3165.26 3484.74 13830.14 9349.44 -5.6 6 3.68 1 3411.13 3288.18 3280.37 3173.95 3361.13 14295.79 9018.68 -7.6 1 3.85 0.6 3484.74 3411.13 3288.18 3280.37 3230.66 14525.87 9005.73 -7.3 1 4.02 0.6 3361.13 3484.74 3411.13 3288.18 3006.84 13486.9 8164.47 -7.8 -4 3.99 0.9 3230.66 3361.13 3484.74 3411.13 3149.9 14144.81 7895.51 -3.7 1 4.12 0.8 3006.84 3230.66 3361.13 3484.74 3403.13 15243.98 8478.52 -0.3 2 4.47 0.4 3149.9 3006.84 3230.66 3361.13 3564.95 16370.17 9097.14 2 3 4.69 1 3403.13 3149.9 3006.84 3230.66 3327.7 15231.29 8872.96 2 5 4.77 1.6 3564.95 3403.13 3149.9 3006.84 3141.12 15514.27 9081.69 3.1 5 4.92 1.9 3327.7 3564.95 3403.13 3149.9 3064.42 15941.29 9037.44 2.7 3 4.84 1.5 3141.12 3327.7 3564.95 3403.13 2880.4 16840.31 8709.47 2.4 2 4.77 1 3064.42 3141.12 3327.7 3564.95 2661.39 16797.69 8323.61 2 3 4.88 0.7 2880.4 3064.42 3141.12 3327.7 2504.67 15929.69 7808.33 4.1 -1 5 0.4 2661.39 2880.4 3064.42 3141.12 2450.41 15917.07 7909.82 5.2 -9 5.3 1.1 2504.67 2661.39 2880.4 3064.42 2354.32 16135.96 7683.23 6 -5 5.5 1.4 2450.41 2504.67 2661.39 2880.4 2401.33 17274.75 7875.82 5.1 -1 5.44 1.3 2354.32 2450.41 2504.67 2661.39 2394.36 18233.45 7855.04 3.6 -9 5.36 1.6 2401.33 2354.32 2450.41 2504.67 2409.36 19081.79 7948.43 0.8 -8 5.32 1.8 2394.36 2401.33 2354.32 2450.41 2525.56 20152.53 7990.65 1.8 -12 5.26 1.9 2409.36 2394.36 2401.33 2354.32 2346.9 20482.48 7603.88 0.1 -13 5.42 1.7 2525.56 2409.36 2394.36 2401.33 2250.27 20021.79 7242.41 -2 -16 5.37 1.6 2346.9 2525.56 2409.36 2394.36 2152.18 18200.34 6657.9 -4.1 -21 5.32 1.3 2250.27 2346.9 2525.56 2409.36 2154.87 18255.96 6901.12 -3 -21 5.11 1.5 2152.18 2250.27 2346.9 2525.56 2097.76 18555.87 6921.03 -3.1 -16 4.82 2 2154.87 2152.18 2250.27 2346.9 1989.31 18223.28 6707.03 -3.9 -15 4.89 2.3 2097.76 2154.87 2152.18 2250.27 1877.1 20090.77 6435.87 -4.8 -8 5.05 2.5 1989.31 2097.76 2154.87 2152.18 1852.13 21021.37 6323.43 -5.1 -8 5.23 2.4 1877.1 1989.31 2097.76 2154.87 1795.65 21108.13 5996.21 -6.2 -9 5.3 2.5 1852.13 1877.1 1989.31 2097.76 1751.01 20824.57 5804.8 -6.6 -9 5.69 2 1795.65 1852.13 1877.1 1989.31 1745.74 20870.31 5685.5 -7.8 -11 5.86 1.9 1751.01 1795.65 1852.13 1877.1 1703.45 21597.85 5496.26 -10.5 -12 5.96 1.9 1745.74 1751.01 1795.65 1852.13 1748.09 22204.88 5671.51 -10.8 -13 6.09 1.8 1703.45 1745.74 1751.01 1795.65 1734.1 21761.31 5600.81 -12.3 -13 6.11 1.9 1748.09 1703.45 1745.74 1751.01 1711.74 21837.95 5580.18 -13 -12 6.37 2 1734.1 1748.09 1703.45 1745.74 1690.6 20394.71 5610.95 -12.8 -15 6.45 2 1711.74 1734.1 1748.09 1703.45 1665.5 20675.4 5518.24 -15.1 -18 6.26 1.9 1690.6 1711.74 1734.1 1748.09 1631.59 20407.27 5174.59 -14 -16 6.07 2 1665.5 1690.6 1711.74 1734.1 1538.09 19417.95 5136.72 -12.4 -16 6.4 1.5 1631.59 1665.5 1690.6 1711.74 1452.46 18111.66 4935.8 -11.8 -20 6.72 1.5 1538.09 1631.59 1665.5 1690.6 1429.12 17961.87 4761.28 -9.9 -16 6.99 1.2 1452.46 1538.09 1631.59 1665.5 1471.16 18128.65 4744.66 -9.9 -12 6.94 1.2 1429.12 1452.46 1538.09 1631.59 1475.57 17410.71 4637.58 -9.4 -12 7.14 1.3 1471.16 1429.12 1452.46 1538.09 1464.65 16188.69 4684.76 -9.2 -6 7.35 1.2 1475.57 1471.16 1429.12 1452.46 1433.75 15039.44 4510.76 -7 -5 7.48 1.3 1464.65 1475.57 1471.16 1429.12 1451.04 16373.21 4392.16 -5.1 -7 7.74 1.4 1433.75 1464.65 1475.57 1471.16 1365.41 16322.08 4230.64 -2.2 -4 8.1 1.7 1451.04 1433.75 1464.65 1475.57 1299.88 16433.75 4064.33 0.4 -7 8.29 1.7 1365.41 1451.04 1433.75 1464.65 1349.03 18065.03 3953.66 4.3 -8 8.26 1.8 1299.88 1365.41 1451.04 1433.75 1368.43 19036.23 3872.33 3.7 -7 8.41 1.9 1349.03 1299.88 1365.41 1451.04
Names of X columns:
BEL_20 Nikkei DJ_Indust Conjunct_Seizoenzuiver Cons_vertrouw Rend_oblig_EUR Alg_consumptie_index_BE Y1 Y2 Y3 Y4
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Include Monthly Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
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