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Data X:
7.5 0 1 0 -9 -20 1 13 12 21 0.708333333 0.120805369 6 0 1 1 -9 8 14 8 8 22 0.557142857 0.223021583 6.5 0 1 0 -9 -8 6 14 11 22 0.363636364 0.263513514 1 0 1 1 -9 18 22 16 13 18 0.543859649 0.291139241 1 0 1 1 -9 -4 5 14 11 23 0.47826087 0.2421875 5.5 0 1 1 -5 4 16 13 10 12 0.295454545 0.299107143 8.5 0 1 0 -6 3 23 15 7 20 0.543859649 0.220125786 6.5 0 1 1 -6 -4 4 13 10 22 0.636363636 0.495238095 4.5 0 1 1 -9 33 33 20 15 21 0.690909091 0.48427673 2 0 1 1 -9 17 -3 17 12 19 0.259493671 0.221556886 5 0 1 1 -8 0 17 15 12 22 0.413793103 0.193939394 0.5 0 1 1 -9 22 17 16 10 15 0.348066298 0.226415094 5 0 1 1 -6 5 28 12 10 20 0.38961039 0.319327731 5 0 1 0 -9 11 24 17 14 19 0.553191489 0.392045455 2.5 0 1 0 -9 22 26 11 6 18 0.542857143 0.388888889 5 0 0 0 -3 13 22 16 12 15 0.3875 0.285714286 5.5 0 1 1 -9 -4 17 16 14 20 0.434210526 0.331288344 3.5 0 1 0 -9 8 21 15 11 21 0.360824742 0.290322581 3 0 0 1 -8 9 12 13 8 21 0.424242424 0.306569343 4 0 1 0 -7 23 30 14 12 15 0.535714286 0.190082645 0.5 0 1 1 -9 15 20 19 15 16 0.308823529 0.222222222 6.5 0 1 1 -7 14 20 16 13 23 0.247524752 0.756756757 4.5 0 1 0 -9 10 20 17 11 21 0.411214953 0.158371041 7.5 0 1 1 -9 9 22 10 12 18 0.784090909 0.25 5.5 0 1 1 -7 14 20 15 7 25 0.482142857 0.315436242 4 0 1 1 -9 5 21 14 11 9 0.432748538 0.151639344 7.5 0 0 1 -8 7 18 14 7 30 0.583941606 0.736486486 7 0 0 0 -5 2 12 16 12 20 0.545454545 0.260869565 4 0 1 1 -9 14 18 15 12 23 0.924242424 0.133333333 5.5 0 1 0 -9 5 25 17 13 16 0.440860215 0.14379085 2.5 0 1 0 -9 -16 21 14 9 16 0.438095238 0.244680851 5.5 0 1 0 -6 6 13 16 11 19 0.297709924 0.205128205 3.5 0 1 1 -9 9 23 15 12 25 0.333333333 0.227272727 2.5 0 1 1 -8 -5 19 16 15 18 0.316770186 0.571428571 4.5 0 1 1 -6 3 21 16 12 23 0.35 0.40952381 4.5 0 1 1 -9 7 16 10 6 21 0.244094488 0.567010309 4.5 0 1 0 -9 8 20 8 5 10 0.506493506 0.105960265 6 0 0 1 -5 8 15 17 13 14 0.185185185 0.374045802 2.5 0 1 1 -7 13 10 14 11 22 0.576470588 0.427710843 5 0 1 0 -9 1 27 10 6 26 0.31547619 0.27388535 0 0 1 1 -9 -7 3 14 12 23 0.645833333 0.261261261 5 0 1 1 -9 2 10 12 10 23 0.256578947 0.386206897 6.5 0 1 1 -9 4 18 16 6 24 0.72 0.283950617 5 0 1 1 -9 -11 26 16 12 24 0.457943925 0.116564417 6 0 0 1 -8 -16 15 16 11 18 0.548387097 0.389830508 4.5 0 1 0 -9 20 19 8 6 23 0.380165289 0.315508021 5.5 0 1 1 -9 1 12 16 12 15 0.443548387 0.275229358 1 0 0 1 -9 12 20 15 12 19 0.583333333 0.677777778 7.5 0 1 0 -9 19 26 8 8 16 1.25 0.066666667 6 0 0 1 -9 1 13 13 10 25 0.224137931 0.457831325 5 0 0 1 -9 9 26 14 11 23 0.381443299 0.275862069 1 0 0 1 -6 3 15 13 7 17 0.284090909 0.380952381 5 0 1 1 -9 -5 10 16 12 19 0.238095238 0.128378378 6.5 0 0 1 -9 18 23 19 13 21 0.269230769 0.141935484 7 0 1 1 -9 15 15 19 14 18 0.304054054 0.384 4.5 0 1 1 -9 11 20 14 12 27 0.239726027 0.198275862 0 0 0 0 -7 -5 25 15 6 21 0.35 0.203125 8.5 0 1 1 -3 6 17 13 14 13 0.422680412 0.239130435 3.5 0 0 0 -9 -2 14 10 10 8 0.24 0.183673469 7.5 0 0 1 -9 1 14 16 12 29 0.454545455 0.25 3.5 0 1 1 -9 15 18 15 11 28 0.618644068 0.207317073 6 0 1 0 -9 10 23 11 10 23 0.293103448 0.296296296 1.5 0 1 0 -3 -8 26 9 7 21 0.634920635 0.181818182 9 0 1 1 -9 -5 11 16 12 19 0.460431655 0.212871287 3.5 0 1 0 -7 5 10 12 7 19 0.74 0.177419355 3.5 0 0 1 -5 -3 11 12 12 20 0.416666667 0.424242424 4 0 1 0 -9 16 24 14 12 18 0.427631579 0.387978142 6.5 0 1 1 -9 9 13 14 10 19 0.704225352 0.121495327 7.5 0 1 1 -9 0 17 13 10 17 0.29787234 0.356382979 6 0 0 0 -9 -11 16 15 12 19 0.53030303 0.326923077 5 0 1 0 -6 2 12 17 12 25 0.440944882 0.451977401 5.5 0 1 0 -9 4 17 14 12 19 0.432835821 0.23015873 3.5 0 0 0 -6 8 13 11 8 22 0.477777778 0.210526316 7.5 0 0 1 -9 17 9 9 10 23 0.786666667 0.595959596 6.5 0 1 0 -9 -9 28 7 5 14 0.390625 0.230215827 NA 0 1 1 -9 10 7 13 10 28 0.073170732 0.602564103 6.5 0 1 0 -9 -4 18 15 10 16 0.404109589 0.265432099 6.5 0 0 1 -8 -8 9 12 12 24 0.391304348 0.351851852 7 0 1 0 -9 19 18 15 11 20 0.327956989 0.182389937 3.5 0 0 0 -9 -12 20 14 9 12 0.345679012 0.486486486 1.5 0 1 1 -9 3 21 16 12 24 0.6 0.290909091 4 0 0 0 -9 1 15 14 11 22 0.648148148 0.364583333 7.5 0 0 0 -9 7 22 13 10 12 0.630434783 0.181034483 4.5 0 0 0 -9 4 22 16 12 22 0.452830189 0.333333333 0 0 0 1 -8 10 20 13 10 20 0.735294118 0.12371134 3.5 0 0 0 -4 -6 10 16 9 10 0.733333333 0.291338583 5.5 0 0 1 -7 10 16 16 11 23 0.673684211 0.349056604 5 0 0 1 -9 13 17 16 12 17 0.561403509 0.5875 4.5 0 0 0 -9 15 27 10 7 22 0.322580645 0.689189189 2.5 0 0 0 -7 9 18 12 11 24 0.777777778 0.351648352 7.5 0 0 0 -9 6 22 12 12 18 0.607142857 0.157894737 7 0 0 1 -6 3 13 12 6 21 0.574074074 0.175675676 0 0 0 1 -5 -12 15 12 9 20 0.40625 0.122807018 4.5 0 0 1 -7 22 19 19 15 20 0.763157895 -0.014285714 3 0 0 0 -1 -14 16 14 10 22 0.234693878 0.210526316 1.5 0 0 1 -8 -3 11 13 11 19 0.238636364 0.244897959 3.5 0 0 0 -9 17 22 16 12 20 0.6 0.090909091 2.5 0 0 1 -7 10 16 15 12 26 0.323529412 0.182539683 5.5 0 0 1 -5 10 19 12 12 23 0.262295082 0.244897959 8 0 0 1 -9 -4 15 8 11 24 0.25 0.147368421 1 0 0 1 -9 16 23 10 9 21 0.755102041 0.472727273 5 0 0 1 -9 9 20 16 11 21 0.448717949 0.214285714 4.5 0 0 0 -9 17 26 16 12 19 0.366666667 0.225490196 3 0 0 1 -8 -19 18 10 12 8 0.6 0.220930233 3 0 0 1 -9 14 22 18 14 17 0.745454545 0.269230769 8 0 0 1 -9 6 17 12 8 20 0.416666667 0.25 2.5 0 0 0 -7 3 17 16 10 11 0.813953488 0.382352941 7 0 0 0 -7 7 10 10 9 8 0.538461538 0.29 0 0 0 0 -9 0 22 14 10 15 0.533333333 0.138297872 1 0 0 0 -8 10 17 12 9 18 0.407407407 0.153846154 3.5 0 0 0 -9 9 13 11 10 18 0.862745098 0.183673469 5.5 0 0 0 -9 14 19 15 12 19 0.529411765 0.203389831 5.5 0 0 1 -9 12 18 7 11 19 0.447368421 0.191919192 0.5 1 1 1 -8 4 4 16 9 23 0.281517615 0.470115741 7.5 1 1 1 -9 -29 5 16 11 22 0.306563927 0.328294444 9 1 1 1 -6 -1 19 16 12 21 0.201614774 0.22255 9.5 1 1 1 -9 8 20 16 12 25 0.195067998 0.205211039 8.5 1 0 0 -9 5 7 12 7 30 0.410857626 0.339166667 7 1 0 1 0 5 6 15 12 17 0.28015873 0.200147059 8 1 1 1 -9 2 5 14 12 27 0.15537269 0.26610252 10 1 1 0 -9 9 15 15 12 23 0.337037902 0.47632384 7 1 1 1 -8 12 18 16 10 23 0.420304659 0.452816212 8.5 1 1 0 -9 4 15 13 15 18 0.147950192 0.223283582 9 1 1 0 -8 -4 -2 10 10 18 0.188320015 0.194329176 9.5 1 1 1 -9 18 27 17 15 23 0.202803922 0.323490028 4 1 1 1 -9 4 14 15 10 19 0.185347222 0.400427778 6 1 1 1 -9 6 23 18 15 15 0.170566104 0.169854855 8 1 1 1 -9 -9 13 16 9 20 0.183901452 0.173468196 5.5 1 1 1 -9 6 14 20 15 16 0.193735503 1.194735974 9.5 1 0 1 -9 23 22 16 12 24 0.292711698 0.712311281 7.5 1 1 1 -8 4 16 17 13 25 0.195848765 0.355720572 7 1 1 1 -3 -2 12 16 12 25 0.222067183 0.204281189 7.5 1 1 0 -7 11 19 15 12 19 0.257031825 0.51652862 8 1 1 1 -9 4 23 13 8 19 0.234446321 0.357821637 7 1 1 1 -9 14 20 16 9 16 0.269586454 0.417707708 7 1 1 1 -9 -13 10 16 15 19 0.224988426 0.243062963 6 1 1 1 -9 16 12 16 12 19 0.206670692 0.430823171 10 1 1 1 -9 -1 12 17 12 23 0.18985138 0.1390955 2.5 1 1 1 -9 6 22 20 15 21 0.197599826 0.112057292 9 1 1 0 -7 11 21 14 11 22 0.232957958 0.187538889 8 1 1 1 -5 13 5 17 12 19 0.222431201 0.376467236 6 1 0 1 6 -14 8 6 6 20 0.285618847 0.138661972 8.5 1 1 1 -9 0 21 16 14 20 0.18190873 0.228713805 6 1 1 1 -6 21 26 15 12 3 0.416820776 0.372898629 9 1 1 1 -8 5 26 16 12 23 0.174321364 0.182050265 8 1 1 0 -9 6 19 16 12 23 0.258202948 0.239576808 9 1 1 0 -7 -7 0 14 11 20 0.337180346 0.152296935 5.5 1 1 1 -9 26 7 16 12 15 0.285104987 0.334537037 7 1 1 0 -9 7 12 16 12 16 0.286127451 0.28612895 5.5 1 1 0 -9 1 11 16 12 7 0.151865478 0.08694234 9 1 1 1 -6 4 8 14 12 24 0.147452485 0.220476421 2 1 1 0 -9 13 28 14 8 17 0.189459064 0.143357988 8.5 1 1 1 -9 10 14 16 8 24 0.398905864 0.322568226 9 1 1 1 -8 8 13 16 12 24 0.185284329 0.235974003 8.5 1 1 0 -9 -16 11 15 12 19 0.158458282 0.128325258 9 1 0 1 -9 17 11 16 11 25 0.192361111 0.219767157 7.5 1 0 1 -8 17 10 16 10 20 0.313528864 0.021276529 10 1 1 1 -9 22 13 18 11 28 0.328360768 0.259023619 9 1 1 0 -9 9 21 15 12 23 0.211738304 0.272291052 7.5 1 0 0 -8 16 8 16 13 27 0.242804719 0.253572464 6 1 0 0 -9 -6 16 16 12 18 0.284674013 0.57795584 10.5 1 0 0 -8 0 13 16 12 28 0.528304947 0.210649718 8.5 1 0 1 -9 9 19 17 10 21 0.13641439 0.046395275 8 1 1 0 -9 -4 9 14 10 19 0.389907997 0.181021195 10 1 1 1 -9 12 13 18 11 23 0.24276331 -1.909027778 10.5 1 0 0 -6 4 1 9 8 27 0.404116576 0.409492455 6.5 1 0 1 2 0 17 15 12 22 0.312767094 1.251371882 9.5 1 0 0 -8 4 22 14 9 28 0.27719071 0.261001821 8.5 1 0 1 -8 6 17 15 12 25 0.216284722 0.327100694 7.5 1 0 0 -9 18 12 13 9 21 0.180801377 0.392530556 5 1 0 0 -6 14 18 16 11 22 0.297684211 0.233421409 8 1 0 1 -9 29 25 20 15 28 0.225438733 0.307091667 10 1 0 0 -4 3 13 14 8 20 0.351530651 0.30981401 7 1 0 1 -8 8 21 12 8 29 0.442759104 0.300772262 7.5 1 1 1 -6 9 12 15 11 25 0.128578579 0.125040404 7.5 1 1 1 -6 9 13 15 11 25 0.128578579 0.125040404 9.5 1 0 1 -8 4 24 15 11 20 0.348787561 0.226181818 6 1 1 1 -7 1 7 16 13 20 0.329419192 0.26742467 10 1 1 0 -9 -20 12 11 7 16 0.172281348 0.162681083 7 1 0 1 -9 0 13 16 12 20 0.407706667 0.191856925 3 1 1 0 -5 -3 19 7 8 20 0.167021858 0.648078231 6 1 0 0 -5 3 7 11 8 23 0.425509259 0.455885802 7 1 0 0 -8 -7 10 9 4 18 0.266293168 0.241000918 10 1 1 1 -9 6 17 15 11 25 0.222480738 0.212322581 7 1 0 0 -9 10 21 16 10 18 0.322611434 0.16110844 3.5 1 0 1 -1 -13 14 14 7 19 0.356934524 0.088493953 8 1 0 0 -7 9 13 15 12 25 0.327597553 0.293555556 10 1 0 0 -8 11 16 13 11 25 0.215343137 0.275020576 5.5 1 0 0 -7 13 20 13 9 25 0.346842243 0.302790069 6 1 0 0 -8 14 17 12 10 24 0.833472222 0.514925121 6.5 1 0 1 -8 6 23 16 8 19 0.272172262 0.211948998 6.5 1 0 1 -9 -1 12 14 8 26 0.283309942 0.386046296 8.5 1 0 1 -7 20 25 16 11 10 0.433042735 0.095461264 4 1 0 1 -9 12 7 14 12 17 0.349158951 0.075743464 9.5 1 0 0 -9 14 18 15 10 13 0.274121355 0.281776777 8 1 0 0 -9 -9 4 10 10 17 0.290506391 0.250952381 8.5 1 0 1 -1 2 -2 16 12 30 0.211063916 0.434223744 5.5 1 1 0 -9 -1 23 14 8 25 0.2117216 0.201896615 7 1 0 0 -5 0 17 16 11 4 0.248230159 0.282506242 9 1 0 0 -9 1 15 12 8 16 0.30501443 0.621481481 8 1 0 0 -9 17 15 16 10 21 0.350213333 0.316108586 10 1 1 1 9 -3 -27 16 14 23 0.211086057 0.305825758 8 1 0 1 -9 14 2 15 9 22 0.248098098 0.234538462 6 1 1 0 -9 9 20 14 9 17 0.271167929 0.159231678 8 1 0 0 -9 -3 20 16 10 20 0.185701685 0.152412156 5 1 1 1 -9 2 28 11 13 20 0.176020531 0.273148907 9 1 0 0 -9 -6 21 15 12 22 0.297584064 0.736468254 4.5 1 1 1 -9 8 20 18 13 16 0.23311566 0.541755051 8.5 1 0 1 -3 -7 2 13 8 23 0.286542838 0.264845085 9.5 1 0 0 -9 -3 25 7 3 0 0.292520886 0.090402884 8.5 1 0 1 -9 2 23 7 8 18 0.308204898 0.378388339 7.5 1 0 1 -8 7 15 17 12 25 0.7439 0.280033069 7.5 1 1 1 -9 10 22 18 11 23 0.23348528 0.186456506 5 1 1 0 -9 11 25 15 9 12 0.278736682 0.381747144 7 1 0 0 -9 7 27 8 12 18 0.110443767 0.160609668 8 1 1 0 -9 10 26 13 12 24 0.247397451 0.201980519 5.5 1 1 1 -6 3 3 13 12 11 0.22951952 0.323875405 8.5 1 0 1 -7 -9 11 15 10 18 0.320006083 0.358130787 9.5 1 1 1 -9 13 23 18 13 23 0.188667894 0.121768254 7 1 0 1 -6 0 20 16 9 24 0.396329365 0.346325536 8 1 0 0 -9 4 18 14 12 29 0.226511799 0.395868056 8.5 1 1 0 -9 10 29 15 11 18 0.144659307 0.36510101 3.5 1 0 0 -3 15 28 19 14 15 0.227397343 0.137556689 6.5 1 1 1 -9 5 10 16 11 29 0.156464052 0.266444444 6.5 1 1 1 -7 1 23 12 9 16 0.18280303 0.082317642 10.5 1 1 0 -8 3 22 16 12 19 0.215632062 0.158123752 8.5 1 0 0 1 0 15 11 8 22 0.228228512 0.421488095 8 1 1 0 -9 11 15 16 15 16 0.13849177 0.052406732 10 1 0 1 -8 13 22 15 12 23 0.249225865 0.700910853 10 1 1 1 -9 14 24 19 14 23 0.371907492 0.106134068 9.5 1 1 0 -8 4 18 15 12 19 0.210805416 0.136771066 9 1 1 0 -8 15 16 14 9 4 0.192051091 0.31140377 10 1 1 0 -7 4 13 14 9 20 0.307276179 0.202003968 7.5 1 0 1 -9 19 31 17 13 24 0.287479314 0.287638889 4.5 1 1 1 -9 2 24 16 13 20 0.217689282 0.232794312 4.5 1 1 1 -9 8 21 20 15 4 0.262741402 0.096179078 0.5 1 1 1 -8 9 12 16 11 24 0.223307494 0.36832244 6.5 1 0 0 -6 7 24 9 7 22 0.218808244 0.27353588 4.5 1 1 1 -7 13 24 13 10 16 0.20257007 0.414980843 5.5 1 1 1 -9 -17 -13 15 11 3 0.27072189 0.29 5 1 0 1 -8 15 21 19 14 15 0.341920732 0.398771242 6 1 1 0 -9 21 20 16 14 24 0.244994632 0.128055556 4 1 0 0 -9 2 33 17 13 17 0.224562198 0.263346561 8 1 0 1 -9 8 14 16 12 20 0.370937082 0.442181373 10.5 1 0 0 -2 0 12 9 8 27 0.404116576 0.409492455 6.5 1 0 1 -7 1 9 11 13 26 0.217865169 0.561430879 8 1 0 1 -9 9 27 14 9 23 0.226876543 0.255677388 8.5 1 1 0 -9 -6 10 19 12 17 0.308522035 -0.014952575 5.5 1 1 1 -2 -6 10 13 13 20 0.238308824 0.43192577 7 1 1 0 -9 14 19 14 11 22 0.276251885 0.012610229 5 1 1 1 -9 16 19 15 11 19 0.176395399 0.178722643 3.5 1 1 1 -9 7 23 15 13 24 0.14962149 0.280357981 5 1 1 0 -9 6 20 14 12 19 0.275199214 0.235927041 9 1 0 1 -8 1 17 16 12 23 0.249200708 0.199343972 8.5 1 0 0 1 -25 10 17 10 15 0.391740506 0.202880658 5 1 1 1 0 -5 16 12 9 27 0.262342995 0.11832162 9.5 1 0 0 -9 10 16 15 10 26 0.262959685 0.364840909 3 1 0 1 -9 8 24 17 13 22 0.554728535 0.422387153 1.5 1 1 0 -6 16 30 15 13 22 0.13420068 0.272724014 6 1 0 0 -6 6 24 10 9 18 0.647001339 0.468197115 0.5 1 0 1 -9 -5 7 16 11 15 0.491585795 0.375216931 6.5 1 0 1 -1 4 0 15 12 22 0.312767094 1.251371882 7.5 1 0 0 -9 6 24 11 8 27 0.343632275 0.220176768 4.5 1 0 1 0 -6 4 16 12 10 0.264130747 0.708067251 8 1 0 1 -3 13 14 16 12 20 0.370937082 0.442181373 9 1 0 0 -9 4 16 16 12 17 0.216410147 0.304893378 7.5 1 0 1 -9 3 8 14 9 23 0.255399419 0.125643739 8.5 1 0 0 -9 -12 1 14 12 19 0.161785209 0.256436404 7 1 0 0 -9 25 29 16 12 13 0.413511111 0.476383792 9.5 1 0 1 -9 18 18 16 11 27 0.360395702 0.189252137 6.5 1 0 1 -8 13 20 18 12 23 0.528591954 0.30247076 9.5 1 0 0 -9 11 24 14 6 16 0.225422222 0.271944444 6 1 0 1 -9 1 24 20 7 25 0.299106607 0.46738204 8 1 0 0 -8 8 22 15 10 2 0.299478979 0.240915751 9.5 1 0 0 -9 9 20 16 12 26 0.232872117 0.274058642 8 1 0 1 -9 11 15 16 10 20 0.388952502 0.237603175 8 1 1 0 -8 -3 6 16 12 23 0.217751799 0.32208452 9 1 0 0 -6 9 22 12 9 22 0.247850057 0.21695845 5 1 0 1 -4 -1 16 8 3 24 0.208009259 0.420555556
Names of X columns:
Ex year_bin group_bin gender_bin AMS.A AMS.I AMS.E CONFSTATTOT CONFSOFTTOT tot_numeracy CH/B PRH/LFM
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) 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, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) 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,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) 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,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) 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,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) 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,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) 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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