A.2 指標選択についての付論 · A.2 指標選択についての討論 －236－ 表 A.2.3:...
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A.2 指標選択についての討論 －232－ A.2 指標選択についての付論 以下，本文の 1.3.3 のおける指標選択の実験のうち， 関数を用いた場合の 結果を，一部の推定結果とともに示しておく．解釈も，本文中に記したものと ほとんど同様であるので，ここでは繰返さないことにする． (1) 目的変数をカレントDI，説明変数を(Δ ሺଷሻ ∗ )，(k ＝ 1) としたモデル （全11系列を採用したモデル） **************Explanatory Variable ＝ 1 2 3 4 5 6 7 8 9 10 11 ************** Percent Correct Predictions(U P) ＝ 98.95288 Percent Correct Predictions(DOWN) ＝ 97.01493 Percent Correct Predictions( ALL) ＝ 98.15385 Deviance Residuals: Min 1Q Median 3Q Max -2.401e＋00 -5.072e-06 8.005e-07 5.547e-04 1.706e＋00 Coefficients: Estimate Std. Error z value Pr(＞|z|) (Intercept) -1.9874 1.3405 -1.483 0.13818 mat.name[, var.list[1]] 11.8674 4.9603 2.392 0.01674 * mat.name[, var.list[2]] 4.0118 2.8427 1.411 0.15817 mat.name[, var.list[3]] 7.1506 2.7610 2.590 0.00960 ** mat.name[, var.list[4]] 0.3740 2.2058 0.170 0.86535 mat.name[, var.list[5]] 11.1086 3.9084 2.842 0.00448 ** mat.name[, var.list[6]] 3.2643 1.8261 1.788 0.07384 . mat.name[, var.list[7]] 4.6468 2.0146 2.307 0.02108 * mat.name[, var.list[8]] 5.4606 1.7935 3.045 0.00233 ** mat.name[, var.list[9]] 4.3205 1.8967 2.278 0.02273 * mat.name[, var.list[10]] 7.2713 3.0117 2.414 0.01577 * mat.name[, var.list[11]] -0.6224 1.3362 -0.466 0.64134 --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 440.497 on 324 degrees of freedom Residual deviance: 26.481 on 313 degrees of freedom AIC: 50.481 Number of Fisher Scoring iterations: 10
Transcript of A.2 指標選択についての付論 · A.2 指標選択についての討論 －236－ 表 A.2.3:...
A.2 指標選択についての討論
－232－
A.2 指標選択についての付論
以下，本文の 1.3.3 のおける指標選択の実験のうち， 関数を用いた場合の
結果を，一部の推定結果とともに示しておく．解釈も，本文中に記したものと
ほとんど同様であるので，ここでは繰返さないことにする．
(1) 目的変数をカレントDI，説明変数を (Δ∗ )，(k ＝ 1) としたモデル
（全11系列を採用したモデル）
**************Explanatory Variable ＝ 1 2 3 4 5 6 7 8 9 10 11 ************** Percent Correct Predictions(U P) ＝ 98.95288 Percent Correct Predictions(DOWN) ＝ 97.01493 Percent Correct Predictions( ALL) ＝ 98.15385 Deviance Residuals:
Min 1Q Median 3Q Max -2.401e＋00 -5.072e-06 8.005e-07 5.547e-04 1.706e＋00 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) -1.9874 1.3405 -1.483 0.13818 mat.name[, var.list[1]] 11.8674 4.9603 2.392 0.01674 * mat.name[, var.list[2]] 4.0118 2.8427 1.411 0.15817 mat.name[, var.list[3]] 7.1506 2.7610 2.590 0.00960 ** mat.name[, var.list[4]] 0.3740 2.2058 0.170 0.86535 mat.name[, var.list[5]] 11.1086 3.9084 2.842 0.00448 ** mat.name[, var.list[6]] 3.2643 1.8261 1.788 0.07384 . mat.name[, var.list[7]] 4.6468 2.0146 2.307 0.02108 * mat.name[, var.list[8]] 5.4606 1.7935 3.045 0.00233 ** mat.name[, var.list[9]] 4.3205 1.8967 2.278 0.02273 * mat.name[, var.list[10]] 7.2713 3.0117 2.414 0.01577 * mat.name[, var.list[11]] -0.6224 1.3362 -0.466 0.64134 --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 440.497 on 324 degrees of freedom Residual deviance: 26.481 on 313 degrees of freedom AIC: 50.481 Number of Fisher Scoring iterations: 10
A 補 論
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(稼働率指数，投資財出荷指数，
有効求人倍率を除いた8系列を採用したモデル)
**************Explanatory Variable ＝ 1 2 3 5 7 8 9 10 ************** Percent Correct Predictions(U P) ＝ 99.47644 Percent Correct Predictions(DOWN) ＝ 97.7612 Percent Correct Predictions( ALL) ＝ 98.76923 Call: glm(formula ＝ DI
～ mat.name[, var.list[1]]＋mat.name[, var.list[2]]＋
mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋ mat.name[, var.list[8]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.184e＋00 -2.539e-04 1.166e-06 1.142e-03 2.043e＋00 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) -0.9299 0.7016 -1.325 0.18503 mat.name[, var.list[1]] 10.6566 3.6767 2.898 0.00375 ** mat.name[, var.list[2]] 1.8283 1.6785 1.089 0.27604 mat.name[, var.list[3]] 4.6428 1.6630 2.792 0.00524 ** mat.name[, var.list[4]] 8.3000 2.6796 3.097 0.00195 ** mat.name[, var.list[5]] 2.9885 1.2107 2.468 0.01357 * mat.name[, var.list[6]] 5.0224 1.6242 3.092 0.00199 ** mat.name[, var.list[7]] 2.9786 1.2518 2.379 0.01734 * mat.name[, var.list[8]] 4.2626 1.8709 2.278 0.02270 * --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 440.497 on 324 degrees of freedom Residual deviance: 30.983 on 316 degrees of freedom AIC: 48.983 Number of Fisher Scoring iterations: 10
A.2 指標選択についての討論
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(稼働率指数，投資財出荷指数を除いた9系列を採用したモデル)
**************Explanatory Variable ＝ 1 2 3 5 7 8 9 10 11 ************** Percent Correct Predictions(U P) ＝ 99.47644 Percent Correct Predictions(DOWN) ＝ 97.7612 Percent Correct Predictions( ALL) ＝ 98.76923 Call: glm(formula ＝ DI
～ mat.name[, var.list[1]]＋mat.name[, var.list[2]]＋
mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋ mat.name[, var.list[8]]＋mat.name[, var.list[9]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.203e＋00 -2.515e-04 1.026e-06 1.107e-03 2.088e＋00 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) -0.9535 0.7360 -1.296 0.19514 mat.name[, var.list[1]] 10.7728 3.8429 2.803 0.00506 ** mat.name[, var.list[2]] 1.8765 1.7382 1.080 0.28032 mat.name[, var.list[3]] 4.6787 1.6939 2.762 0.00574 ** mat.name[, var.list[4]] 8.3732 2.7642 3.029 0.00245 ** mat.name[, var.list[5]] 3.0368 1.2922 2.350 0.01877 * mat.name[, var.list[6]] 5.0484 1.6447 3.069 0.00214 ** mat.name[, var.list[7]] 3.0050 1.2723 2.362 0.01818 * mat.name[, var.list[8]] 4.2523 1.8698 2.274 0.02295 * mat.name[, var.list[9]] -0.1203 1.0712 -0.112 0.91058 --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 440.50 on 324 degrees of freedom Residual deviance: 30.97 on 315 degrees of freedom AIC: 50.97 Number of Fisher Scoring iterations: 10
A 補 論
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表 A.2.1: 1系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 AIC R2 PCPU PCPD PCP
110 24511 96387
186.5285 215.6850 198.8605 230.1704 250.6201 274.3432 334.3376 313.1032 316.7856 361.8112 437.4549
0.64160.59040.61230.54820.51750.48190.33220.36210.34230.23880.0216
91.099592.670291.623089.005289.528889.528886.911084.293284.293284.293285.3403
85.074682.089679.850879.104577.611970.895570.895568.656764.179161.940322.3881
88.6154 88.3077 86.7692 84.9231 84.6154 81.8462 80.3077 77.8462 76.0000 75.0769 59.3846
表 A.2.2: 2系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 AIC R2 PCPU PCPD PCP
1512221414815322
5 10 8 11 5 6 3 10 11 11 10 7 11 10 10 9
134.9453126.4718143.3391149.3543159.5074174.5827141.9385159.2999136.7245159.9858179.2996170.3006203.7621177.2801157.4629171.6667
0.76050.76510.73450.73140.71170.66320.71570.69720.75340.70370.66790.67660.64030.66470.69740.6672
93.193793.717393.193792.670292.670293.717392.670291.623092.670292.146691.623092.670292.146691.623092.146691.0995
92.537391.791091.044889.552288.806087.313488.059788.806087.313487.313487.313485.074685.820985.820984.328485.8209
92.9231 92.9231 92.3077 91.3846 91.0769 91.0769 90.7692 90.4615 90.4615 90.1539 89.8462 89.5385 89.5385 89.2308 88.9231 88.9231
A.2 指標選択についての討論
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表 A.2.3: 3系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 3 AIC R2 PCPU PCPD PCP
5 1 1 1 3 1 1 1 5 4 2 4 1 2 1
8583525575510 255
10 8 11 8 10 8 10 11 10 10 8 11 11 10 9
97.700589.1161
108.7138106.4211103.8041122.9695113.1074115.9330120.0730112.7494127.8764121.6941123.2515112.9019118.3628
0.82740.84880.81040.79920.81710.78570.80060.80610.78780.79950.78010.78220.78080.79530.7890
96.335195.288095.288095.288094.764495.288093.717395.288094.764493.717394.240894.764494.764494.240894.2408
94.029994.029992.537392.537392.537391.791094.029991.044891.791093.283692.537391.791091.791091.791091.7910
95.3846 94.7692 94.1539 94.1539 93.8462 93.8462 93.8462 93.5385 93.5385 93.5385 93.5385 93.5385 93.5385 93.2308 93.2308
表 A.2.4: 4系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 3 4 AIC R2 PCPU PCPD PCP
1 1 1 1 1 1 3 1 3 5 5 1 1 4 4
3 5 5 5 2 5 5 2 5 7 7 5 5 5 5
5 8 7 7 8 8 7 5 8 8 10 8 6 8 7
8 9 10 8 11 11 10 8 10 10 11 10 8 10 10
68.659682.719299.635082.407496.550180.204598.917885.987579.210898.5083111.629479.199490.673583.8468
101.0071
0.89530.86560.84430.86550.84140.86670.83320.86620.85950.83030.81000.87020.84960.85520.8361
97.905895.811596.335196.335197.382296.335196.335195.811596.335196.335196.335195.811595.288095.288095.8115
94.776196.268795.522495.522494.029994.776194.776195.522494.029994.029994.029994.029994.776194.776194.0299
96.6154 96.0000 96.0000 96.0000 96.0000 95.6923 95.6923 95.6923 95.3846 95.3846 95.3846 95.0769 95.0769 95.0769 95.0769
A 補 論
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表 A.2.5: 5系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 3 4 5 AIC R2 PCPU PCPD PCP
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
5 2 3 3 3 3 3 5 3 5 5 3 2 5 3 2
7 3 5 5 5 5 4 7 5 8 7 5 3 6 7 5
10 5 8 8 7 6 5 8 8 10 8 7 8 8 8 6
11 8 10 11 8 8 8 11 9 11 10 10 11 9 11 8
91.796669.043760.728568.007862.051170.524770.478376.053863.971172.221875.979981.170684.268284.368485.125986.8594
0.86280.90290.90520.89320.90910.89530.89650.88330.90480.88180.87810.87560.86490.86750.86320.8671
97.382297.905897.905897.905897.905897.905897.905897.905896.858696.858696.858696.858697.382296.335196.858696.8586
97.014996.268795.522495.522495.522495.522495.522494.776195.522495.522495.522495.522494.776196.268795.522495.5224
97.2308 97.2308 96.9231 96.9231 96.9231 96.9231 96.9231 96.6154 96.3077 96.3077 96.3077 96.3077 96.3077 96.3077 96.3077 96.3077
表 A.2.6: 6系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 AIC R2 PCPU PCPD PCP
1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
2 3 2 2 3 3 2 2 3 3 3 3 3 3 3 4
3433543354555545
5 5 5 4 6 5 5 5 7 5 6 8 8 6 5 7
6 8 8 5 7 7 7 8 8 8 8 9 9 8 6 8
8 11 11 8 8 8 8 9 9 10 10 11 10 11 8 10
70.635169.537168.923771.040363.718662.914461.522163.875855.153062.070762.425764.620768.325469.982072.385273.7949
0.90330.89580.89900.90300.90900.91370.91300.91250.92000.90770.90490.90420.89520.89340.89630.8900
98.429398.429398.429397.905897.905897.905897.905897.905898.429397.382297.905897.382296.858697.905897.905896.8586
96.268795.522495.522496.268796.268796.268796.268796.268795.522496.268795.522496.268797.014995.522495.522497.0149
97.5385 97.2308 97.2308 97.2308 97.2308 97.2308 97.2308 97.2308 97.2308 96.9231 96.9231 96.9231 96.9231 96.9231 96.9231 96.9231
A.2 指標選択についての討論
－238－
表 A.2.7: 7系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 7 AIC R2 PCPU PCPD PCP
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3 3 2 2 3 3 2 2 4 2 3 3 3 2 2
5 5 3 3 4 4 3 3 5 3 4 5 5 3 3
765585447557655
8 7 7 7 9 6 5 5 8 6 7 8 8 6 8
9 8 8 8 10 7 7 6 10 8 8 10 9 7 9
10 10 10 9 11 8 8 8 11 9 10 11 10 8 11
48.298855.375155.572753.827279.782264.665063.214572.630068.150565.595554.645855.744058.265062.578165.0266
0.93590.92320.92280.92850.88370.91340.91520.90320.90140.91280.92960.92110.91570.91390.9099
98.952998.429399.476498.952997.905897.905897.905898.429397.905898.429398.429397.905898.429397.905897.9058
97.7612 97.0149 95.5224 96.2687 97.0149 97.0149 97.0149 96.2687 97.0149 96.2687 95.5224 96.2687 95.5224 96.2687 96.2687
98.4615 97.8462 97.8462 97.8462 97.5385 97.5385 97.5385 97.5385 97.5385 97.5385 97.2308 97.2308 97.2308 97.2308 97.2308
表 A.2.8: 8系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 7 8 AIC R2 PCPU PCPD PCP
1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2
2 3 3 3 2 2 2 2 3 2 3 3 3 2 2 3
3 5 5 4 3 3 3 3 4 3 4 5 5 3 4 5
5675455555566456
7 7 8 7 5 7 6 7 6 6 7 7 7 5 7 8
8 8 9 8 7 8 7 8 7 7 8 8 8 6 8 9
9 9 10 9 8 10 8 9 8 8 10 10 9 7 9 10
10101110911101110911111081011
48.982948.169950.269449.157655.825857.113355.549055.797355.556655.163655.326456.550360.216064.408465.260065.9213
0.93820.94080.93600.93810.92850.92220.92640.92940.93110.92900.93050.92370.91950.91540.91330.9057
99.476498.952998.952998.952998.952998.952998.952998.952998.429398.429398.429397.905897.382297.905898.952997.3822
97.7612 97.7612 97.7612 97.0149 96.2687 96.2687 96.2687 96.2687 97.0149 97.0149 96.2687 97.0149 97.7612 97.0149 95.5224 97.7612
98.7692 98.4615 98.4615 98.1539 97.8462 97.8462 97.8462 97.8462 97.8462 97.8462 97.5385 97.5385 97.5385 97.5385 97.5385 97.5385
A 補 論
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表 A.2.9: 9系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 7 8 9 AIC R2 PCPU PCPD PCP
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2 3 3 2 2 3 2 2 2 2 2 3 2 2 2
3 4 5 3 3 4 3 4 3 3 3 4 3 3 3
5 5 6 4 4 5 5 5 4 4 5 5 5 4 4
7 6 7 5 7 7 6 7 5 5 6 6 6 5 5
8 7 8 7 8 8 7 8 7 6 7 7 7 6 7
9 8 9 8 9 9 8 9 8 7 8 8 8 7 8
109
109
10109
109 8 9
10108
10
11101110111110111191111111011
50.970348.849250.158050.694274.621851.023746.820565.188757.784657.145557.009456.118457.160256.909357.3250
0.93840.94250.94090.93860.90460.93820.94650.91720.92960.92910.93110.93320.92600.93060.9306
99.476498.952998.952998.952998.429398.952998.952998.952998.952998.429398.429398.429398.952998.429398.4293
97.7612 97.7612 97.7612 97.0149 97.7612 97.0149 97.0149 97.0149 96.2687 97.0149 97.0149 97.0149 96.2687 96.2687 96.2687
98.769298.461598.461598.153998.153998.153998.153998.153997.846297.846297.846297.846297.846297.538597.5385
表 A.2.10: 10系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 7 8 9 10 AIC R2 PCPU PCPD PCP
1 1 1 1 1 1 1 1 1 2 1
3 2 2 2 2 2 2 2 2 3 2
4 3 3 4 3 3 3 3 3 4 3
5 4 5 5 4 4 4 4 4 5 4
6 5 6 6 5 6 5 5 5 6 5
7 6 7 7 7 7 6 6 6 7 6
8 7 8 8 8 8 7 7 8 8 7
9 8 9 9 9 9 8 8 9 9 9
109
10101010109
101010
1110111111111111111111
50.821048.696748.509766.555352.683975.977357.969358.902061.419460.283868.8217
0.94260.94610.94800.91900.93850.90490.93230.93210.91820.92260.8990
98.952998.952998.952998.952998.952997.905898.429398.429397.905897.905896.3351
97.7612 97.7612 97.0149 97.0149 97.0149 97.7612 97.0149 97.0149 96.2687 96.2687 95.5224
98.461598.461598.153998.153998.153997.846297.846297.846297.230897.230896.0000
表 A.2.11: 11系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
AIC R2 PCPU PCPD PCP
全11系列 50.77769 0.9477803 98.95288 97.01493 98.15385
A.2 指標選択についての討論
－240－
(ii) 説明変数を (Δ∗ )，(k ＝ 1) としたモデル
（全11系列を採用したモデル）
**************Explanatory Variable ＝ 1 2 3 4 5 6 7 8 9 10 11 ************** Percent Correct Predictions(U P) ＝ 93.82022 Percent Correct Predictions(DOWN) ＝ 84.44444 Percent Correct Predictions( ALL) ＝ 89.77636 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[,
var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋mat.name[, var.list[8]]＋mat.name[, var.list[9]]＋ mat.name[, var.list[10]]＋mat.name[, var.list[11]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.3861 -0.2210 0.1013 0.3629 2.8077 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.62059 0.28857 2.151 0.031511 * mat.name[, var.list[1]] -2.02433 0.89419 -2.264 0.023582 * mat.name[, var.list[2]] 0.67093 0.72272 0.928 0.353231 mat.name[, var.list[3]] 1.01553 0.40039 2.536 0.011201 * mat.name[, var.list[4]] 1.49553 0.79530 1.880 0.060044 . mat.name[, var.list[5]] 1.69857 0.47803 3.553 0.000380 *** mat.name[, var.list[6]] 0.87998 0.44876 1.961 0.049890 * mat.name[, var.list[7]] 0.42760 0.33921 1.261 0.207455 mat.name[, var.list[8]] 0.06654 0.39003 0.171 0.864536 mat.name[, var.list[9]] 0.77589 0.37320 2.079 0.037614 * mat.name[, var.list[10]] 0.54328 0.68835 0.789 0.429965 mat.name[, var.list[11]] 2.18281 0.50642 4.310 1.63e-05 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 167.73 on 301 degrees of freedom AIC: 191.73 Number of Fisher Scoring iterations: 5
A 補 論
－241－
(生産指数，原材料消費指数，稼働率指数，
百貨店販売額，中小企業売上高を除いた6系列を採用したモデル)
**************Explanatory Variable ＝ 3 5 6 8 9 11 ************** Percent Correct Predictions(U P) ＝ 93.25843 Percent Correct Predictions(DOWN) ＝ 87.4074 Percent Correct Predictions( ALL) ＝ 90.73482 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[,
var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.5154 -0.2603 0.1207 0.4160 2.7368 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.2105 0.2294 0.918 0.35885 mat.name[, var.list[1]] 1.1802 0.3602 3.276 0.00105 ** mat.name[, var.list[2]] 1.9399 0.4145 4.680 2.86e-06 *** mat.name[, var.list[3]] 0.6883 0.3763 1.829 0.06741 . mat.name[, var.list[4]] 0.3043 0.3653 0.833 0.40474 mat.name[, var.list[5]] 0.7309 0.3529 2.071 0.03832 * mat.name[, var.list[6]] 2.0308 0.4632 4.384 1.17e-05 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 176.49 on 306 degrees of freedom AIC: 190.49 Number of Fisher Scoring iterations: 5
A.2 指標選択についての討論
－242－
(生産指数，原材料消費指数，投資財出荷指数，
中小企業売上高を除いた7系列を採用したモデル)
**************Explanatory Variable ＝ 3 4 5 7 8 9 11 ************** Percent Correct Predictions(U P) ＝ 92.69663 Percent Correct Predictions(DOWN) ＝ 88.14815 Percent Correct Predictions( ALL) ＝ 90.73482 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[,
var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.3462 -0.3006 0.1199 0.3849 2.7724 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.4767 0.2152 2.217 0.02664 * mat.name[, var.list[1]] 0.9929 0.3726 2.665 0.00770 ** mat.name[, var.list[2]] 0.9393 0.4785 1.963 0.04966 * mat.name[, var.list[3]] 1.6128 0.4580 3.521 0.00043 *** mat.name[, var.list[4]] 0.3835 0.3239 1.184 0.23643 mat.name[, var.list[5]] 0.2542 0.3752 0.677 0.49819 mat.name[, var.list[6]] 0.6949 0.3536 1.965 0.04938 * mat.name[, var.list[7]] 2.1726 0.4554 4.771 1.84e-06 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 175.31 on 305 degrees of freedom AIC: 191.31 Number of Fisher Scoring iterations: 5
A 補 論
－243－
(生産指数，原材料消費指数，
中小企業売上高を除いた8系列を採用したモデル)
**************Explanatory Variable ＝ 3 4 5 6 7 8 9 11 ************** Percent Correct Predictions(U P) ＝ 93.25843 Percent Correct Predictions(DOWN) ＝ 87.4074 Percent Correct Predictions( ALL) ＝ 90.73482 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[,
var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋mat.name[, var.list[8]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.4010 -0.2859 0.1252 0.4078 2.8357 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.3190 0.2482 1.285 0.198687 mat.name[, var.list[1]] 1.0471 0.3800 2.756 0.005859 ** mat.name[, var.list[2]] 0.7010 0.5149 1.361 0.173417 mat.name[, var.list[3]] 1.6797 0.4634 3.625 0.000289 *** mat.name[, var.list[4]] 0.5211 0.4102 1.270 0.203948 mat.name[, var.list[5]] 0.3914 0.3245 1.206 0.227718 mat.name[, var.list[6]] 0.1508 0.3899 0.387 0.698891 mat.name[, var.list[7]] 0.6958 0.3606 1.929 0.053674 . mat.name[, var.list[8]] 2.0248 0.4634 4.370 1.24e-05 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 173.70 on 304 degrees of freedom AIC: 191.70 Number of Fisher Scoring iterations: 5
A.2 指標選択についての討論
－244－
(生産指数，投資財出荷指数，
中小企業売上高を除いた8系列を採用したモデル)
**************Explanatory Variable ＝ 2 3 4 5 7 8 9 11 ************** Percent Correct Predictions(U P) ＝ 92.69663 Percent Correct Predictions(DOWN) ＝ 88.14815 Percent Correct Predictions( ALL) ＝ 90.73482 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[,
var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋mat.name[, var.list[8]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.3250 -0.3032 0.1189 0.3936 2.7872 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.4650 0.2186 2.127 0.033418 * mat.name[, var.list[1]] 0.1945 0.6652 0.292 0.770007 mat.name[, var.list[2]] 0.9654 0.3843 2.512 0.012012 * mat.name[, var.list[3]] 0.8147 0.6414 1.270 0.204049 mat.name[, var.list[4]] 1.5784 0.4718 3.345 0.000822 *** mat.name[, var.list[5]] 0.4051 0.3330 1.216 0.223823 mat.name[, var.list[6]] 0.2422 0.3768 0.643 0.520321 mat.name[, var.list[7]] 0.6945 0.3535 1.964 0.049479 * mat.name[, var.list[8]] 2.1562 0.4586 4.702 2.58e-06 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 175.23 on 304 degrees of freedom AIC: 193.23 Number of Fisher Scoring iterations: 5
A 補 論
－245－
(生産指数，稼働率指数，中小企業売上高を除いた8系列を採用したモデル)
**************Explanatory Variable ＝ 2 3 5 6 7 8 9 11 ************** Percent Correct Predictions(U P) ＝ 93.25843 Percent Correct Predictions(DOWN) ＝ 87.4074 Percent Correct Predictions( ALL) ＝ 90.73482 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[,
var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋mat.name[, var.list[8]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.5726 -0.2850 0.1171 0.3919 2.8436 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.2341 0.2337 1.002 0.316481 mat.name[, var.list[1]] 0.5793 0.5080 1.140 0.254176 mat.name[, var.list[2]] 1.0468 0.3891 2.691 0.007133 ** mat.name[, var.list[3]] 1.7112 0.4671 3.664 0.000249 *** mat.name[, var.list[4]] 0.6219 0.3904 1.593 0.111149 mat.name[, var.list[5]] 0.4145 0.3334 1.243 0.213787 mat.name[, var.list[6]] 0.1482 0.3867 0.383 0.701570 mat.name[, var.list[7]] 0.7171 0.3576 2.005 0.044925 * mat.name[, var.list[8]] 1.9692 0.4613 4.269 1.97e-05 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 174.27 on 304 degrees of freedom AIC: 192.27 Number of Fisher Scoring iterations: 5
A.2 指標選択についての討論
－246－
(原材料消費指数，稼働率指数，営業利益を除いた8系列を採用したモデル)
**************Explanatory Variable ＝ 1 3 5 6 7 8 10 11 ************** Percent Correct Predictions(U P) ＝ 94.38202 Percent Correct Predictions(DOWN) ＝ 85.92593 Percent Correct Predictions( ALL) ＝ 90.73482 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[,
var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋mat.name[, var.list[8]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.6776 -0.2989 0.1250 0.4230 2.5580 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.3339 0.2274 1.468 0.14210 mat.name[, var.list[1]] -0.4328 0.6406 -0.676 0.49922 mat.name[, var.list[2]] 1.1726 0.3752 3.125 0.00178 ** mat.name[, var.list[3]] 2.1265 0.4449 4.779 1.76e-06 *** mat.name[, var.list[4]] 0.7939 0.4256 1.865 0.06212 . mat.name[, var.list[5]] 0.2737 0.3176 0.862 0.38876 mat.name[, var.list[6]] 0.3992 0.3584 1.114 0.26529 mat.name[, var.list[7]] 0.5111 0.6401 0.798 0.42459 mat.name[, var.list[8]] 2.0916 0.4676 4.473 7.71e-06 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 179.34 on 304 degrees of freedom AIC: 197.34 Number of Fisher Scoring iterations: 5
A 補 論
－247－
(生産指数，中小企業売上高を除いた9系列を採用したモデル)
**************Explanatory Variable ＝ 2 3 4 5 6 7 8 9 11 ************** Percent Correct Predictions(U P) ＝ 93.25843 Percent Correct Predictions(DOWN) ＝ 87.4074 Percent Correct Predictions( ALL) ＝ 90.73482 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[, var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋mat.name[, var.list[8]]＋mat.name[, var.list[9]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.4156 -0.2859 0.1231 0.3880 2.8514 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.3056 0.2516 1.214 0.22461 mat.name[, var.list[1]] 0.2142 0.6734 0.318 0.75045 mat.name[, var.list[2]] 1.0188 0.3905 2.609 0.00908 ** mat.name[, var.list[3]] 0.5602 0.6804 0.823 0.41030 mat.name[, var.list[4]] 1.6430 0.4762 3.450 0.00056 *** mat.name[, var.list[5]] 0.5236 0.4103 1.276 0.20185 mat.name[, var.list[6]] 0.4154 0.3339 1.244 0.21352 mat.name[, var.list[7]] 0.1388 0.3908 0.355 0.72236 mat.name[, var.list[8]] 0.6946 0.3608 1.925 0.05418 . mat.name[, var.list[9]] 2.0078 0.4659 4.310 1.64e-05 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 173.60 on 303 degrees of freedom AIC: 193.60 Number of Fisher Scoring iterations: 5
A.2 指標選択についての討論
－248－
(生産指数，投資財出荷指数を除いた9系列を採用したモデル)
**************Explanatory Variable ＝ 2 3 4 5 7 8 9 10 11 ************** Percent Correct Predictions(U P) ＝ 92.13483 Percent Correct Predictions(DOWN) ＝ 88.88889 Percent Correct Predictions( ALL) ＝ 90.73482 Call: glm(formula ＝ DI.type
～ mat.name[, var.list[1]]＋mat.name[,
var.list[2]]＋mat.name[, var.list[3]]＋mat.name[, var.list[4]]＋ mat.name[, var.list[5]]＋mat.name[, var.list[6]]＋mat.name[, var.list[7]]＋mat.name[, var.list[8]]＋mat.name[, var.list[9]], family ＝ binomial(link ＝ logit))
Deviance Residuals:
Min 1Q Median 3Q Max -2.3302 -0.2953 0.1176 0.4008 2.7850 Coefficients:
Estimate Std. Error z value Pr(＞|z|) (Intercept) 0.4872 0.2333 2.089 0.036753 * mat.name[, var.list[1]] 0.2510 0.6960 0.361 0.718332 mat.name[, var.list[2]] 0.9742 0.3861 2.523 0.011631 * mat.name[, var.list[3]] 0.8434 0.6494 1.299 0.194039 mat.name[, var.list[4]] 1.5715 0.4724 3.327 0.000878 *** mat.name[, var.list[5]] 0.4035 0.3329 1.212 0.225460 mat.name[, var.list[6]] 0.2508 0.3777 0.664 0.506604 mat.name[, var.list[7]] 0.7148 0.3605 1.983 0.047399 * mat.name[, var.list[8]] -0.1450 0.5334 -0.272 0.785763 mat.name[, var.list[9]] 2.1991 0.4872 4.514 6.36e-06 *** --- Signif. codes: 0‘***’0.001‘**’0.01‘*’0.05‘.’0.1‘ ’1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 427.98 on 312 degrees of freedom Residual deviance: 175.15 on 303 degrees of freedom AIC: 195.15 Number of Fisher Scoring iterations: 5
A 補 論
－249－
表 A.2.12: 1系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 AIC R2 PCPU PCPD PCP
5111014296387
274.8878 252.8861 310.0168 308.1275 300.8198 286.1192 345.2091 330.6981 323.4322 385.2481 423.1173
0.43850.48030.35610.36050.36870.40530.27340.29120.30730.14890.0278
85.955188.202384.831585.393383.707984.831583.707980.898982.022580.898980.8989
76.296368.888971.111169.629671.851969.629670.370466.666762.963057.777825.9259
81.7891 79.8722 78.9137 78.5943 78.5943 78.2748 77.9553 74.7604 73.8019 70.9265 57.1885
表 A.2.13: 2系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 AIC R2 PCPU PCPD PCP
4 1 5 2
103 5 9 3 5 6 2 5 8 1 2
11 11 11 11 11 11 9 11 5
10 11 9 8 11 5 5
212.5174230.1079209.8691214.0055234.2602228.3165248.8758236.4923237.2123242.9339242.3271267.9343258.2001244.5774253.8995246.6512
0.59280.55110.59030.58840.53270.53240.50300.52810.53320.52240.51300.45460.48290.50710.49510.5088
89.325889.325890.449489.887689.325889.325888.202388.202387.078786.516988.202387.640587.078785.955184.831585.3933
84.444482.963080.740780.740779.259378.518579.259379.259380.000079.259377.037077.037077.777878.518578.518577.7778
87.2205 86.5815 86.2620 85.9425 84.9840 84.6645 84.3451 84.3451 84.0256 83.3866 83.3866 83.0671 83.0671 82.7476 82.1086 82.1086
A.2 指標選択についての討論
－250－
表 A.2.14: 3系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 3 AIC R2 PCPU PCPD PCP
6 3 1 4 1 1 1 7 3 1 4 2 6 3 2
774766386263766
8 8 6 8 10 7 6 9 8 10 7 6 9 7 10
320.3694310.2373281.8711287.2436293.8454292.2540264.5244334.4195274.4594279.1736273.8078249.5821296.9449276.3150267.7267
0.33590.35600.42000.40640.39970.40070.46300.31450.43530.43370.43590.49400.39250.42600.4547
81.460783.146182.584383.146184.269784.831582.022583.146183.146185.393385.393383.707987.078784.269784.8315
69.629670.370472.592673.333371.851971.111174.814873.333373.333371.851971.851974.074169.629673.333373.3333
76.3578 77.6358 78.2748 78.9137 78.9137 78.9137 78.9137 78.9137 78.9137 79.5527 79.5527 79.5527 79.5527 79.5527 79.8722
表 A.2.15: 4系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 3 4 AIC R2 PCPU PCPD PCP
3 1 1 6 1 1 1 1 4 4 2 1 1 1 1
6 2 4 7 2 2 4 2 6 7 6 6 4 6 4
7 6 6 8 6 3 6 6 7 8 7 7 6 7 7
8 8 8 10 10 6 7 7 8 9 8 8 9 10 10
274.8446266.4937277.1038289.0405267.2470251.4992274.9468262.5796272.3706271.8472260.3602288.6463266.1171287.2767271.5091
0.44000.46240.44320.42940.45750.49360.44060.47070.44830.46010.47860.42570.47730.42440.4592
82.584383.707983.146183.707983.146184.269784.269784.831584.269782.584385.393385.393383.707985.393382.5843
74.814873.333374.074173.333375.555674.074174.074173.333374.074176.296372.592672.592675.555673.333377.0370
79.2332 79.2332 79.2332 79.2332 79.8722 79.8722 79.8722 79.8722 79.8722 79.8722 79.8722 79.8722 80.1917 80.1917 80.1917
A 補 論
－251－
表 A.2.16: 5系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 3 4 5 AIC R2 PCPU PCPD PCP
3 3 2 3 2 3 2 2 3 3 3 3 3 3 1
5 4 3 5 3 5 3 3 5 4 5 5 4 4 3
6 5 5 6 5 6 5 5 7 5 8 6 5 5 5
8 6 6 9 7 10 9 10 10 9 10 7 7 8 6
11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
192.7787192.3982193.4591189.1794195.2349194.6323191.5553197.1283197.1555189.7231195.6851193.4576192.6276192.4780194.7747
0.64500.64960.64510.65470.64550.64210.64910.63910.64060.65810.64230.64530.65450.64990.6383
93.258493.820292.696692.134892.696693.258492.134892.696692.696691.573092.696692.696692.134891.573092.6966
85.925985.185286.666786.666785.925985.185286.666785.925985.925987.407485.925985.925985.925986.666785.1852
90.0959 90.0959 90.0959 89.7764 89.7764 89.7764 89.7764 89.7764 89.7764 89.7764 89.7764 89.7764 89.4569 89.4569 89.4569
表 A.2.17: 6系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 AIC R2 PCPU PCPD PCP
3 3 2 3 3 3 2 3 2 3 3 3 3 2 3
5 5 3 4 4 5 3 5 3 4 4 5 4 3 4
665556565556545
8 7 6 6 7 8 6 7 6 6 7 9 6 5 9
9 9 7 7 9 10 10 8 9 10 8 10 9 9 10
11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
190.4909189.8974193.1937192.2691189.7673194.6868195.4515194.0171190.4025194.3862193.1462191.1547189.6967191.7151191.6964
0.65600.66020.65480.65820.66580.64670.64500.64880.65720.64930.65630.65440.66130.65810.6581
93.258493.258493.258493.258493.258493.258492.696692.696692.696693.820292.134892.134891.573091.573091.5730
87.407486.666786.666785.925985.925985.925986.666786.666786.666785.185286.666786.666787.407487.407487.4074
90.7348 90.4153 90.4153 90.0959 90.0959 90.0959 90.0959 90.0959 90.0959 90.0959 89.7764 89.7764 89.7764 89.7764 89.7764
A.2 指標選択についての討論
－252－
表 A.2.18: 7系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 7 AIC R2 PCPU PCPD PCP
3 3 3 3 1 3 1 3 1 2 2 2 2 2 3
4 4 5 5 3 5 3 5 3 3 3 3 3 3 4
5 5 6 6 4 6 5 6 5 5 5 4 4 5 5
768757676665566
8 7 9 8 7 9 8 8 7 7 7 7 6 7 7
9 8 10 10 10 10 10 9 10 8 10 9 7 9 9
11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
191.3131193.3968192.4458195.8002192.9334191.8974196.0875191.5756196.5729194.2838195.1910191.6374194.0878190.4138189.8445
0.66580.65920.65550.65200.65950.66020.64590.66040.64790.65540.65470.66610.65910.66620.6695
92.696693.258492.696693.258493.258493.258494.382093.258493.820293.258493.258492.696693.258492.696692.6966
88.1482 86.6667 87.4074 86.6667 86.6667 86.6667 85.1852 86.6667 85.9259 86.6667 86.6667 87.4074 86.6667 87.4074 86.6667
90.7348 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.0959
表 A.2.19: 8系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 7 8 AIC R2 PCPU PCPD PCP
3 2 2 1 1 2 2 2 1 3 1 1 1 3 3
4 3 3 3 3 3 3 3 2 4 3 2 3 5 4
5 4 5 5 4 4 5 4 3 5 4 3 4 6 5
656655655754576
7 7 7 7 6 6 7 7 6 8 6 5 7 8 7
8 8 8 8 7 7 9 9 7 9 7 7 8 9 8
9 9 9 10 9 9 10 10 9 10 10 10 10 10 10
111111111111111111111111111111
191.6965193.2277192.2684197.3415187.5409191.7205192.0436193.5909189.5303193.2838191.1416194.5938193.8525193.5726195.3817
0.66910.66590.66570.65090.67210.66970.66630.66600.66580.66550.66200.66020.66020.66020.6587
93.258492.696693.258494.382094.382093.258492.696692.696694.382092.134894.382092.696693.258493.258493.2584
87.4074 88.1482 87.4074 85.9259 85.1852 86.6667 87.4074 87.4074 85.1852 88.1482 85.1852 87.4074 86.6667 86.6667 86.6667
90.7348 90.7348 90.7348 90.7348 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153 90.4153
A 補 論
－253－
表 A.2.20: 9系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 7 8 9 AIC R2 PCPU PCPD PCP
2 2 1 1 2 2 2 1 1 1 1 1 2 3 1
3 3 2 3 3 3 3 3 3 2 2 3 3 4 2
4 4 3 4 5 4 4 4 5 3 3 4 4 5 3
5 5 4 5 6 5 5 5 6 5 5 5 5 6 4
6 7 5 6 7 6 6 6 7 6 6 6 6 7 5
7 8 7 7 8 7 7 7 8 7 7 7 7 8 7
8 9 8 8 9 8 8 9 9 8 8 8 9 9 9
9 10109
10109
10109
1010101010
111111111111101111111111111111
193.5955195.1538195.6003189.4333193.8747197.2236214.8210188.6452195.0145191.4156196.0238192.6688193.2776193.4016191.9047
0.66930.66550.66030.67120.66530.65880.62180.67290.65830.66470.65360.66190.66990.66850.6701
93.258492.134893.258494.382092.696692.696692.696694.382093.258493.820292.696693.820291.573091.573093.2584
87.4074 88.8889 86.6667 85.1852 87.4074 87.4074 86.6667 84.4444 85.9259 85.1852 85.9259 84.4444 87.4074 87.4074 85.1852
90.734890.734890.415390.415390.415390.415390.095990.095990.095990.095989.776489.776489.776489.776489.7764
表 A.2.21: 10系列，目的変数にヒストリカルDIを採用したモデル( k ＝ 1)
1 2 3 4 5 6 7 8 9 10 AIC R2 PCPU PCPD PCP
1 1 1 1 1 1 2 1 1 1 1
3 2 2 2 2 2 3 2 2 2 2
4 3 3 3 3 3 4 3 4 3 3
5 4 4 4 5 4 5 4 5 4 4
6 5 5 5 6 5 6 5 6 5 6
7 7 6 6 7 6 7 6 7 6 7
8 8 7 7 8 7 8 8 8 7 8
9 9 8 9 9 8 9 9 9 8 9
10109
1010101010109
10
1111111111111111111011
190.5914193.5992190.3430189.7546193.2740194.0254195.1308191.3376196.3435211.9251203.3776
0.67240.66920.67370.67460.66450.66230.66900.66510.65970.62760.6369
94.382093.258493.820293.820292.696693.258491.573092.696693.258492.134892.1348
84.4444 85.9259 84.4444 84.4444 85.9259 84.4444 86.6667 84.4444 83.7037 83.7037 82.9630
90.095990.095989.776489.776489.776489.456989.456989.137489.137488.498488.1789
表 A.2.22: 11系列，目的変数にカレントDIを採用したモデル( k ＝ 1)
AIC R2 PCPU PCPD PCP
全11系列 191.7258 0.6741 93.8202 84.4444 89.7764
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