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VECM 2002 1 2007 12 VECM 55 VECM 1 2 3 3-1ECM 3-2 3-3VECM 3-4 4 1 2017 9 28 2017 10 16

2 1 1 2015, 79-85 BLUEBest Linear Unbiased Estimator 1 Vector Error-Correction Model VECM II III VECM VECM 55 15-34

2 2 2 3 2 2 60 5080 1 2017 4 1 3 6 3 2015, 166 1 2004 60 2001

1 1 1 5 5 10 10 20 20 30 90 120 180 30 120 210 240 35 180 35 150 240 270 45 90 45 60 180 240 270 330 60 65 150 180 210 240 90 120 150 45 300 45 150 360 60 https : //www.hellowork.go.jp/insurance/insurance_benefitdays.html 2017 9 15 45 60 2002 1 1960 90 20 1970 1980 2000 20052012 2005 1968 100

1 1990 2012 1976 2010 4 6 1985 15-34 55 1985 1990 2002 2008 1985 1990 1989 2012 2002 1 2007 12

2012, 263-267 2 5 6 1 VAR VECM 7 3 3-1ECM 2000 ECM ECM!#!$"%#!!#!$"%!$""'%"#'%!$"&% 1 #$' GDP Auto-Regressive Distributed Lag : AD 1970 Partial!#!$"%#"$'%""%!%""&!#!$"%!$ Adjustment 2 general 1###!#!$"%#!!#!$"%!$""'%"&% 3 1!##!#!$"%#"'%"#'%!$"&% 4 2 Distributed Lag

1"####!#!$"%#!!#!$"%!$"&% 5 AR AD Adaptive Expectation general AD VAR AD contemporaneous VAR AD VAR AD VAR 1 AD!#$"!%,"#!$##!!$!"!%!#!$"%!$!!#!$"%#!$!'%"!%!#!$!"'"%!$"&% 6 ECM AD ECM 2 1 ECM AD ECM!#!$"!#!$"#"' y 7 t

"!' "!'#!%!&!$("' 8 "!'#"#!!%!&"' #$!(' #%!%!&!$("'!$ #%$#"!'#!%!&!$("'Error Correction Term ECM ECM 2 1 AD 3!%!&"'!$,('('!$!,",# 6 ECM!('!%!&!$("'!$ 2 #$,#% ECM 6#$,#%,$$"!' $ ECM $#$ k ECM 3-2 15 34 55

15-34 55 8 3 2012 DF-GLS ADFAugmented Dickey-Fuller P-PPhillips-Perron 9 2 1 Eviews 9.0 2 2002 1 2007 12 1 DF-GLS ADF P-P DF-GLS ADF P-P 2.20011 1.39811 2.446 3.95911*** 4.07111** 8.304*** 3.3130** 4.20711*** 3.894** 1.13611 7.25610*** 11.509*** 2.1372 4.0831** 3.175* 8.5041*** 9.2601*** 14.141*** 2.6382 5.3751*** 4.357*** 9.1311*** 9.2941*** 14.142*** 0.84111 1.71911 2.022 3.27311** 1.34211 4.727*** 4.4010*** 4.7960*** 4.747*** 9.6931*** 10.0531*** 13.786*** 15-34 4.1430*** 4.9221*** 4.015** 5.9640*** 6.5363*** 11.591*** 15-34 4.9320*** 5.0910*** 5.057*** 8.2131*** 8.4351*** 14.034*** 55 5.4380*** 5.4040*** 5.172*** 7.8230*** 8.4301*** 17.761*** 55 4.9310*** 5.0230*** 4.849*** 8.2470*** 7.1412*** 29.366*** *p0.1, **p0.05, ***p0.01 t DF-GLS ADF Schwarz Phillips-Perron Spectral estimation method Bandwidth Automatic selection, Newey-West Bandwidth

5 2012, 306 15 34 55 ECM 3 4 1 5 3 5 3 P P P 0 270.529 103.847 0.000 233.977 95.754 0.000 529.925 117.708 0.000 1 144.030 76.973 0.000 108.586 69.819 0.000 223.563 88.804 0.000 2 69.608 54.079 0.001 54.050 47.856 0.012 103.018 63.876 0.000 3 40.103 35.193 0.014 27.456 29.797 0.091 48.621 42.915 0.012 4 21.067 20.262 0.039 9.698 15.495 0.305 27.429 25.872 0.032 5 8.679 9.165 0.062 0.025 3.841 0.873 9.673 12.518 0.143 4 P P P 0 298.943 103.847 0.000 279.421 95.754 0.000 370.614 117.708 0.000 1 178.046 76.973 0.000 161.307 69.819 0.000 244.712 88.804 0.000 2 111.239 54.079 0.000 94.550 47.856 0.000 139.214 63.876 0.000 3 65.137 35.193 0.000 50.601 29.797 0.000 84.250 42.915 0.000 4 29.853 20.262 0.002 15.884 15.495 0.044 46.375 25.872 0.000 5 8.020 9.165 0.082 0.054 3.841 0.816 14.679 12.518 0.021

VECM 10 AIC 8 3-3VECM 5, 6 7 8 VECM 7 8 1 Jarque-Bera VECM 2 4 5 1 2 3 1 1.000 0.000 0.000 1 0.000 1.000 0.000 15-34 1 0.000 0.000 1.000 55 1 0.744 0.326 0.437 1.081 0.187 0.309 1 16.760 1.806 4.391 2.671 5.097 2.065 1 0.327 0.472 0.268 2.184 0.318 1.724 2.880 1.282 2.3169 t 6 1 2 3 4 5 1 1.000 0.000 0.000 0.000 0.000 1 0.000 1.000 0.000 0.000 0.000 15-34 1 0.000 0.000 1.000 0.000 0.000 55 1 0.000 0.000 0.000 1.000 0.000 1 0.000 0.000 0.000 0.000 1.000 1 0.738 4.076 0.131 6.010 0.116 3.815 0.825 2.053 0.163 3.538 1.086 17.131 0.515 67.458 0.658 62.035 1.795 12.769 0.320 19.875 t

55 8 4 5 6 7 VECM 0.057 4.228 1 0.675 1.213 2 4.742 2.048 3 0.737 0.919 0.151 0.186 0.301 0.413 2.838 5.037 1.175 2.153 15-34 0.073 0.088 55 1 2 3 4 5 6 7 8 0.397 0.488 0.248 0.537 0.621 1.118 0.802 1.389 1.033 1.878 0.489 0.284 1.134 1.031 0.731 7.464 4.229 8.074 7.993 1.628 0.371 3.991 2.288 3.487 3.809 0.771 0.013 0.020 0.842 1.229 0.538 1.068 0.386 0.967 0.537 1.687 0.180 0.687 1.017 0.512 0.590 0.439 0.229 0.040 0.273 0.436 1.621 0.994 1.669 1.489 1.036 0.221 1.826 3.103 6.622 1.683 0.520 0.786 N 63 6.040 1.584 0.022 0.040 6.302 1.780 6.151 1.875 0.243 1.404 0.453 3.342 t 4.088 1.502 0.794 1.364 0.837 195.686 Jarque-Bera 3.150 1.504 0.243 0.439 0.969 1.254 0.650 1.343 0.173 0.814 0.491 2.554 3.609 Jarque-Bera p 0.990

8 VECM 1 2 3 4 5 6 7 8 1 0.305 0.589 2 2.338 0.595 3 0.274 0.114 4 0.351 0.588 5 0.663 0.194 1.053 0.668 0.868 0.884 0.185 0.316 0.070 0.504 2.371 1.537 1.983 2.167 0.627 1.007 0.194 1.676 3.200 0.947 6.344 1.922 15-34 0.265 0.017 0.117 0.007 55 0.362 0.748 N 63 0.112 0.235 3.711 1.255 0.313 0.143 0.458 1.071 0.512 0.154 0.723 0.353 0.225 0.704 3.518 0.703 1.646 0.862 0.146 0.494 3.496 0.798 1.826 1.207 0.299 1.186 3.200 0.818 1.203 1.085 0.228 0.966 3.519 1.679 0.852 1.528 0.188 1.110 2.300 3.798 4.053 3.089 3.317 2.935 0.920 0.633 0.655 1.093 1.412 1.385 2.042 2.722 1.073 0.759 0.557 1.570 0.871 3.008 t 0.0788 0.734 0.084 0.884 0.210 0.089 0.845 0.123 1.510 0.479 0.824 169.881 Jarque-Bera 14.926 Jarque-Bera p 0.122 0.326 0.246 55 3-4 VECM 2012, 239 15-34 55 2, 3 1 30 2 6

11 55 2 15-34 55

2 3 2012, 244 4 15 34 5 5 1534 2 7 1534 4

5 11 4 VECM 55 VECM 55

1534 2014, 242-244 BLUE 2015, 162-179 29 www.mhlw.go.jp/file/06-seisakujouhou.../0000159618. pdf2017 9 19 2012, 263-264 )&)& "!)&"$"for #!)&"$#%for all&!%'!)&$)&!$"$!$ $$!$$#$$$%$"""!%'!)&$)&!$"$# $$#$$%$""" "!)&"$"for #!)&"$#%for all& 2000 2000(&$)&(&$)& (&$)& I1

&##"%#"!"$ $!##&#!"!%#!! I0 1 2004 2012, 76-77 2012, 307-3112012, 76-77 VAR 2 12 1 6 HP http : //www.mhlw.go.jp/bunya/ koyou/dl/koyouhoken-santei.pdf2017 10 4 2004528, 33-48. 2012 7 131-148 2004528, 4-18. 2012 20059, 29-51. 2015 2012Eviews 2 2015 1989ECMError Correction Model 8399-147.

Beneficiary Ratio of Unemployment Insurance : A Vector Error-Correction Model (VECM) Analysis Jun Fukuda This study analyzes the beneficiary ratio of unemployment insurance by employing vector error-correction models (VECM), cumulative impulse response functions, and relative dispersion contributions. The monthly data used cover the period from January 2002 to December 2007, and are retrieved from the labor force survey by the Statistic Bureau of the Ministry of Internal Affairs and Communications and the Monthly report of employment insurance business by the Ministry of Health, Labor and Welfare. Results show that, for both males and females, the effective job openings ratio decreases the beneficiary ratio of unemployment insurance through the increase of voluntary unemployed, which are restricted to receive labor insurance. However, for females only, the ratio of unemployed aged over 55 increases the ratio of unemployment insurance in early times, but decreases it later on. This suggests that senior women have difficulty to be reemployed, and that the payment period tends to exhaust before re-employment, even when women look for a job while receiving the unemployment insurance benefits. Key words : Unemployment insurance, Ratio beneficiaries, Vector Error-Correction Models (VECM), Cumulative impulse response functions, Relative dispersion contribution rate