{"id":1508,"date":"2024-11-19T18:05:41","date_gmt":"2024-11-19T09:05:41","guid":{"rendered":"https:\/\/www.feg.co.jp\/?post_type=column&p=1508"},"modified":"2024-11-20T15:04:21","modified_gmt":"2024-11-20T06:04:21","slug":"%e3%83%9e%e3%82%af%e3%83%ad%e7%b5%8c%e6%b8%88%e6%8c%87%e6%a8%99%e3%82%92%e7%94%a8%e3%81%84%e3%81%9f%e9%8a%80%e8%a1%8c%e5%bc%95%e5%bd%93%e7%8e%87%e3%81%ae%e4%ba%88%e6%b8%ac","status":"publish","type":"column","link":"https:\/\/www.feg.co.jp\/column\/%e3%83%9e%e3%82%af%e3%83%ad%e7%b5%8c%e6%b8%88%e6%8c%87%e6%a8%99%e3%82%92%e7%94%a8%e3%81%84%e3%81%9f%e9%8a%80%e8%a1%8c%e5%bc%95%e5%bd%93%e7%8e%87%e3%81%ae%e4%ba%88%e6%b8%ac\/","title":{"rendered":"\u30de\u30af\u30ed\u7d4c\u6e08\u6307\u6a19\u3092\u7528\u3044\u305fPython\u306b\u3088\u308b\u9280\u884c\u5f15\u5f53\u7387\u306e\u4e88\u6e2c"},"content":{"rendered":"
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\"\"<\/figure><\/div>\n\n\n

\u30b5\u30de\u30ea<\/h2>\n\n\n\n

\u30fb\u5b9f\u8cea\uff27\uff24\uff30\u3001\u9271\u5de5\u696d\u751f\u7523 \u751f\u7523\u6307\u6570\u300110\u5e74\u65e5\u672c\u56fd\u50b5\u91d1\u5229\u304c\u9280\u884c\u5f15\u5f53\u7387\u306e\u5148\u884c\u6307\u6a19\u3001\u5b8c\u5168\u5931\u696d\u7387\u3001\u6d88\u8cbb\u8005\u7269\u4fa1\u7b49\u306f\u9045\u884c\u6307\u6a19\u306b
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\u5c1a\u3001\u672c\u7a3f\u3067\u306f\u7d50\u679c\u304c\u7406\u89e3\u3057\u3084\u3059\u3044\u3088\u3046\u3001\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3088\u308a\u3001\u305d\u306e\u95a2\u9023\u6027\u3092\u89e3\u304d\u660e\u304b\u305b\u308b\u306e\u304b\u8a66\u307f\u307e\u3059\u3002<\/p>\n\n\n\n

\u5229\u7528\u30c7\u30fc\u30bf<\/h2>\n\n\n\n

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\u56f3\u8868\uff11\u306eNo.1 \u5f15\u5f53\u7387\u3092\u76ee\u7684\u5909\u6570\u3001No.2\uff5e8\u307e\u3067\u3092\u8aac\u660e\u5909\u6570\u306e\u5019\u88dc\u3068\u3057\u3066\u5206\u6790\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

\u56f3\u88681\u3000\u5229\u7528\u30c7\u30fc\u30bf<\/strong><\/p>\n\n\n

\n
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\u5206\u6790\u65b9\u91dd<\/h2>\n\n\n\n

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\u5206\u6790\u624b\u9806<\/h2>\n\n\n\n

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\u2466\u5168\u3066\u306e\u504f\u56de\u5e30\u4fc2\u6570\u306ep\u5024\u304c0.05\u3092\u4e0b\u56de\u3063\u305f\u6bb5\u968e\u3067\u30e2\u30c7\u30eb\u3092FIX\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

\u30c7\u30fc\u30bf\u6e96\u5099<\/h2>\n\n\n\n

\u6700\u521d\u306b\u6a19\u6e96\u5316\u5909\u63db\u3092\u884c\u3044\u307e\u3059\u3002
Python Ver 3.8.5\u306b\u3088\u308b\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n

import pandas as pd\nfrom matplotlib.pyplot import xcorr\nfrom sklearn import preprocessing\n#\u30c7\u30fc\u30bf\u8aad\u8fbc\ndir="\/\u30de\u30af\u30ed\u7d4c\u6e08\u6307\u6a19\u3068\u9280\u884c\u5f15\u5f53\u7387\/"\nfile=dir+"hikiate.csv"\ndf=pd.read_csv(file)\n#\u6a19\u6e96\u5316\nss=preprocessing.StandardScaler()\nyx=df.drop(['fy'],axis=1)\nss.fit(yx)\nsdf=pd.DataFrame(ss.transform(yx),columns=yx.columns) # \u6a19\u6e96\u5316\n#\u5024\u304c-2\u304b\u3089+2\u30b7\u30b0\u30de\u3092\u5916\u308c\u3066\u3044\u308b\u5834\u5408\u3001-2,2\u3067\u57cb\u3081\u308b\nmdf=yx.copy()\nmdf[(sdf < -2) | (sdf > 2)] = None\n#\u518d\u5ea6\u6a19\u6e96\u5316\nssdf=pd.DataFrame(ss.transform(mdf),columns=mdf.columns) # \u6a19\u6e96\u5316\n#\u5024\u304c-2\u304b\u3089+2\u30b7\u30b0\u30de\u3092\u5916\u308c\u3066\u3044\u308b\u5834\u5408\u3001-2,2\u3067\u57cb\u3081\u308b\nssdf[sdf< -2]=-2\nssdf[sdf> 2]=2\nsdf=ssdf.copy()<\/code><\/pre><\/div>\n\n\n\n
\u76f8\u4e92\u76f8\u95a2\u5206\u6790<\/h2>\n\n\n\n
\u5404\u6307\u6a19\u3092-5\u671f\u304b\u3089+5\u671f\u305a\u3089\u3057\u306a\u304c\u3089\u5f15\u5f53\u7387\u3068\u306e\u76f8\u95a2\u4fc2\u6570\u3092\u78ba\u8a8d\u3057\u3001\u76f8\u95a2\u4fc2\u6570\u306e\u7d76\u5bfe\u5024\u304c\u6700\u5927\u3068\u306a\u308b\u671f\u3092\u30e9\u30b0\u671f\u3068\u3057\u307e\u3059\u3002
Python\u306b\u3088\u308b\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n
def xc(v1,v2,dst=sdf):\n    dd=pd.concat([dst[v1],dst[v2]],axis=1)\n    dd=dd.dropna(how='any').reset_index(drop='True')\n    lags,xcmp,flg,ax=xcorr(dd[v1],dd[v2],maxlags=5)\n    ll=pd.DataFrame(lags,columns=['lags'])\n    xx=pd.DataFrame(xcmp,columns=['xcmp'])\n    kka=pd.concat([ll, xx],axis=1)\n    print(kka)\n    return\n#gdp\nxc('hikiate','gdp',dst=sdf)\n'''\n    lags      xcmp\n0     -5 -0.183534\n1     -4 -0.155800\n2     -3 -0.160925\n3     -2 -0.171269\n4     -1 -0.175033\n5      0 -0.174922\n6      1 -0.216933 <--GDP\u304c1\u671f\u5148\u884c\u9006\u76f8\u95a2\n7      2 -0.203675\n8      3 -0.175982\n9      4 -0.183137\n10     5 -0.153773\n'''<\/code><\/pre><\/div>\n\n\n\n
\u5168\u3066\u306e\u6307\u6a19\u306b\u3064\u3044\u3066\u3001\u76f8\u4e92\u76f8\u95a2\u5206\u6790\u3092\u884c\u3063\u305f\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n
\u56f3\u88682\u3000\u76f8\u4e92\u76f8\u95a2\u5206\u6790\u7d50\u679c\u8868<\/strong><\/p>\n\n\n
\n
\"\u6307\u6a19\u3054\u3068\u306e\u5f15\u5f53\u7387\u306b\u5bfe\u3059\u308b\u76f8\u4e92\u76f8\u95a2\u5206\u6790\u7d50\u679c\"<\/figure><\/div>\n\n\n
\u203b\u672c\u8cc7\u6599\u4e2d\u306e\u30b0\u30e9\u30d5\u3001\u8868\u306f\u3001\u4e0a\u8a18\u30c7\u30fc\u30bf\u3092\u57fa\u306b\u3057\u3066FEG\u304c\u4f5c\u6210<\/p>\n\n\n\n
\u65b9\u91dd\u901a\u308a\u3001\u30c9\u30eb\u5186\u306f\u3053\u306e\u6bb5\u968e\u3067\u6295\u5165\u6307\u6a19\u304b\u3089\u9664\u5916\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u591a\u91cd\u5171\u7dda\u6027\u306e\u30c1\u30a7\u30c3\u30af<\/h2>\n\n\n\n
\u30e9\u30b0\u671f\u3092\u53cd\u6620\u3055\u305b\u305f\u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3057\u3001\u591a\u91cd\u5171\u7dda\u6027\u306e\u30c1\u30a7\u30c3\u30af\u3092\u884c\u3044\u307e\u3059\u3002
\u5c1a\u3001\u5206\u6790\u306e\u969b\u306b\u306f\u3001\u5404\u6307\u6a19\u306e\u30c7\u30fc\u30bf\u5145\u8db3\u671f\u9593\u304c\u7570\u306a\u308b\u305f\u3081\u3001\u5168\u3066\u306e\u30c7\u30fc\u30bf\u304c\u6b20\u640d\u3067\u306a\u3044\u30c7\u30fc\u30bf\u671f\u9593\u3092\u5bfe\u8c61\u3068\u3057\u307e\u3059\u3002
Python\u306b\u3088\u308b\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n
x=pd.concat([sdf.gdp.shift(1),sdf.unemply.shift(-2),sdf.infla.shift(-2),sdf.ind_pro.shift(4),sdf.call_on,sdf.rate_10y.shift(5)],axis =1)\ny=pd.DataFrame(sdf.hikiate)\nyx=pd.concat([y,x],axis=1)\n###\u30e2\u30c7\u30eb\u7528\u30c7\u30fc\u30bf\u751f\u6210\u95a2\u6570\ndef mk_dat(df=df,dst=yx,drop_v='no'):\n    fyx=dst.copy()\n    if drop_v != 'no':\n        #\u9664\u5916\n        for name in drop_v:\n            fyx=fyx.drop(name,axis=1)\n    #\u6a19\u6e96\u5316\u5f8c\u306e\u30c7\u30fc\u30bf\n    fyx=fyx.dropna(how='any')\n    #\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u4f7f\u3063\u3066\u7d50\u5408\n    yyy=pd.merge(df.fy,fyx,left_index=True,right_index=True,how='inner')\n    ffy=pd.DataFrame(yyy.fy).reset_index(drop='True')\n    fy=pd.DataFrame(fyx.hikiate).reset_index(drop='True')\n    fx=fyx.drop('hikiate',axis=1).reset_index(drop='True')\n    return ffy,fy,fx\n###\nffy,fy,fx=mk_dat(df=df,dst=yx,drop_v='no')\n#\u591a\u91cd\u5171\u7dda\u6027\u30c1\u30a7\u30c3\u30af\n#\u5404\u8aac\u660e\u5909\u6570\u306eVIF\u3092\u8a08\u7b97\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nvif=pd.DataFrame()\nvif['Features']=fx.columns\nvif['VIF']=[variance_inflation_factor(fx.values, i) for i in range(fx.shape[1])]\nprint(vif)\n\n''''\n   Features       VIF\n0  gdp        1.483935   \u5b9f\u8ceaGDP\u5e74\u5ea6\u6210\u9577\u7387\n1  unemply    2.366417   \u5b8c\u5168\u5931\u696d\u7387\n2  infla      2.013692   \u6d88\u8cbb\u8005\u7269\u4fa1\n3  ind_pro    1.318250   \u9271\u5de5\u696d\u751f\u7523\n4  call_on    2.774963   \u7121\u62c5\u4fdd\u30b3\u30fc\u30eb\u7fcc\u65e5\u7269\n5  rate_10y   1.712384   10\u5e74\u56fd\u50b5\u91d1\u5229\n'''<\/code><\/pre><\/div>\n\n\n\n
\u7d50\u679c\u3068\u3057\u3066\u306f\u3001VIF\u5024\u306f\u5168\u30665\u672a\u6e80\u3067\u591a\u91cd\u5171\u7dda\u6027\u306e\u554f\u984c\u306f\u8a8d\u3081\u3089\u308c\u306a\u304b\u3063\u305f\u305f\u3081\u3001\u91cd\u56de\u5e30\u5206\u6790\u306b\u79fb\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u91cd\u56de\u5e30\u5206\u6790<\/h2>\n\n\n\n
\u51fa\u529b\u3055\u308c\u308b\u60c5\u5831\u91cf\u306e\u591a\u3044statsmodels.api \u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u7528\u3044\u3066\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002
Python\u306b\u3088\u308b\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3068\u51fa\u529b\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n
import statsmodels.api as sm\nx_add_const=sm.add_constant(fx)\nmodel_sm=sm.OLS(fy,x_add_const).fit()\nprint(model_sm.summary())<\/code><\/pre><\/div>\n\n\n\n
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p\u5024\u306e\u6700\u3082\u9ad8\u3044\u6d88\u8cbb\u8005\u7269\u4fa1\u3092\u6295\u5165\u6307\u6a19\u304b\u3089\u9664\u5916\u3057\u3066\u3001\u518d\u5ea6\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/p>\n\n\n
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p\u5024\u306e\u6700\u3082\u9ad8\u3044\u5b9f\u8ceaGDP\u5e74\u5ea6\u6210\u9577\u7387\u3092\u6295\u5165\u6307\u6a19\u304b\u3089\u9664\u5916\u3057\u3066\u3001\u518d\u5ea6\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/p>\n\n\n
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p\u5024\u306e\u6700\u3082\u9ad8\u3044\u9271\u5de5\u696d\u751f\u7523\u3092\u6295\u5165\u6307\u6a19\u304b\u3089\u9664\u5916\u3057\u3066\u3001\u518d\u5ea6\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/p>\n\n\n
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\u5168\u3066\u306ep\u5024\u304c0.05\u3092\u4e0b\u56de\u3063\u305f\u305f\u3081\u3001\u30e2\u30c7\u30eb\u69cb\u7bc9\u306f\u7d42\u4e86\u3067\u3059\u3002<\/p>\n\n\n\n
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\u81ea\u7531\u5ea6\u8abf\u6574\u5f8c\u306e\u6c7a\u5b9a\u4fc2\u6570\u306f0.750\u3068\u6c7a\u3057\u3066\u9ad8\u304f\u306f\u306a\u3044\u3067\u3059\u304c\u3001\u3042\u308b\u7a0b\u5ea6\u306f\u8aac\u660e\u529b\u304c\u3042\u308b\u30e2\u30c7\u30eb\u306b\u306a\u3063\u3066\u3044\u308b\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/p>\n\n\n\n
\u30e2\u30c7\u30eb\u3092\u9069\u7528<\/h2>\n\n\n\n
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\u307e\u305f\u3001\u7121\u62c5\u4fdd\u30b3\u30fc\u30eb\u7fcc\u65e5\u7269\u306f\u3001\u4e00\u81f4\u6307\u6a19\u3067\u3042\u308b\u305f\u3081\u3001\u4e88\u6e2c\u5e74\u6570\u5206\u306e\u4e88\u6e2c\u5024\u304c\u5fc5\u8981\u3068\u306a\u308a\u307e\u3059\u3002
\u4eca\u56de\u306f\u30012023\u5e749\u670829\u65e5\u306b\u516c\u8868\u3055\u308c\u305f\uff1a\u4e09\u83f1UFJ\u30ea\u30b5\u30fc\u30c1&\u30b3\u30f3\u30b5\u30eb\u30c6\u30a3\u30f3\u30b0\u682a\u5f0f\u4f1a\u793e\u69d8\u306e\u4e88\u6e2c\u5024\u3092\u7528\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u56f3\u88683\u3000\u5b8c\u5168\u5931\u696d\u7387\u3068\u77ed\u671f\u91d1\u5229\u306e\u4e88\u6e2c\u5024<\/strong><\/p>\n\n\n
\n
\"2024\u5e74\u304b\u308928\u5e74\u307e\u3067\u306e\u5b8c\u5168\u5931\u696d\u7387\u3068\u77ed\u671f\u91d1\u5229\u306e\u5e74\u5ea6\u4e88\u6e2c\u5024\"<\/figure><\/div>\n\n\n
\u203b\u4e09\u83f1UFJ\u30ea\u30b5\u30fc\u30c1&\u30b3\u30f3\u30b5\u30eb\u30c6\u30a3\u30f3\u30b0\u682a\u5f0f\u4f1a\u793e\u304c2023\u5e749\u670829\u65e5\u306b\u516c\u8868\u3055\u308c\u305f\u4e88\u6e2c\u5024\u3092\u3082\u3068\u306bFEG\u793e\u304c\u8868\u3092\u4f5c\u6210<\/p>\n\n\n\n
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\u30e2\u30c7\u30eb\u3092\u9069\u7528\u3055\u305b\u305f\u7d50\u679c\u306f\u3001\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n
\u56f3\u88684\u3000\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3088\u308b\u9280\u884c\u5f15\u5f53\u7387\u306e\u4e88\u6e2c\u7d50\u679c<\/strong><\/p>\n\n\n
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\"1997\u5e74\u304b\u30892025\u5e74\u307e\u3067\u306e\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3088\u308b\u9280\u884c\u5f15\u5f53\u7387\u306e\u4e88\u6e2c\u7d50\u679c\"<\/figure><\/div>\n\n\n
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\u203b\u5206\u6790\u7d50\u679c\u3092\u3082\u3068\u306bFEG\u793e\u4f5c\u6210<\/p>\n\n\n\n
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\u5f53\u793e\u306b\u3064\u3044\u3066<\/h2>\n\n\n\n
\u79c1\u305f\u3061\u91d1\u878d\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u30fb\u30b0\u30eb\u30fc\u30d7\uff08FEG\uff09\u306f\u3001\u6700\u5148\u7aef\u306e\u5206\u6790\u6280\u8853\u3092\u7528\u3044\u305f\u4e88\u6e2cAI\u30e2\u30c7\u30eb\u69cb\u7bc9\u3001\u30c7\u30fc\u30bf\u5206\u6790\u30fb\u30b3\u30f3\u30b5\u30eb\u30c6\u30a3\u30f3\u30b0\u30b5\u30fc\u30d3\u30b9\u3092\u63d0\u4f9b\u3057\u3066\u304a\u308a\u3001\u4e0e\u4fe1\u5be9\u67fb\u5206\u91ce\u3067\u306e\u30ea\u30b9\u30af\u63a8\u5b9a\u30e2\u30c7\u30eb\u306e\u91d1\u878d\u6a5f\u95a2\u69d8\u3078\u306e\u5c0e\u5165\u306b\u304a\u3044\u3066\u56fd\u5185\u30c8\u30c3\u30d7\u30ec\u30d9\u30eb\u306e\u5b9f\u7e3e\u3092\u8a87\u308a\u307e\u3059\u3002<\/p>\n\n\n\n
\u30fb\u7121\u62c5\u4fdd\u30ed\u30fc\u30f3\u521d\u671f\u5be9\u67fb\u30e2\u30c7\u30eb<\/a>
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\u30fb\u5546\u54c1\u30ec\u30b3\u30e1\u30f3\u30c9\u30e2\u30c7\u30eb\u69cb\u7bc9<\/a>
\u3082\u3063\u3068\u898b\u308b<\/a>\u3000<\/p>\n\n\n\n
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\u30b5\u30fc\u30d3\u30b9\u7d39\u4ecb\u30da\u30fc\u30b8\u3092\u3054\u89a7\u3044\u305f\u3060\u304d\u3001\u304a\u6c17\u8efd\u306b\u304a\u554f\u3044\u5408\u308f\u305b\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"\u30b5\u30de\u30ea \u30fb\u5b9f\u8cea\uff27\uff24\uff30\u3001\u9271\u5de5\u696d\u751f\u7523 \u751f\u7523\u6307\u6570\u300110\u5e74\u65e5\u672c\u56fd\u50b5\u91d1\u5229\u304c\u9280\u884c\u5f15\u5f53\u7387\u306e\u5148\u884c\u6307\u6a19\u3001\u5b8c\u5168\u5931\u696d\u7387\u3001\u6d88 […]","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"column_cat":[49],"class_list":["post-1508","column","type-column","status-publish","hentry","column_cat-49"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.feg.co.jp\/wp-json\/wp\/v2\/column\/1508","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.feg.co.jp\/wp-json\/wp\/v2\/column"}],"about":[{"href":"https:\/\/www.feg.co.jp\/wp-json\/wp\/v2\/types\/column"}],"wp:attachment":[{"href":"https:\/\/www.feg.co.jp\/wp-json\/wp\/v2\/media?parent=1508"}],"wp:term":[{"taxonomy":"column_cat","embeddable":true,"href":"https:\/\/www.feg.co.jp\/wp-json\/wp\/v2\/column_cat?post=1508"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}