怎样用EVIEWS作Engle-Granger两步法
\u5982\u4f55\u7528Eviews\u505a\u65f6\u95f4\u5e8f\u5217\u7684granger\u56e0\u679c\u68c0\u9a8c\uff0c\u8981\u8be6\u7ec6\u6b65\u9aa4\uff01 \u8c22\u8c22\uff01\u8fd9\u4e2a\u7a0d\u5fae\u6709\u70b9\u9ebb\u70e6\uff0c\u56e0\u4e3a\u505agranger\u56e0\u679c\uff0c\u9996\u5148\u8981\u6ce8\u610f\u5e8f\u5217\u662f\u5426\u5e73\u7a33\uff0c\u4e00\u822c\u8981\u5148\u505aADF\u68c0\u9a8c\uff0c\u7ed3\u679c\u5982\u679c\u5e73\u7a33\u53ef\u4ee5\u7ee7\u7eedG\u68c0\u9a8c\uff1b\u82e5\u4e0d\u5e73\u7a33\u8981\u5bf9\u540c\u9636\u5355\u6574\u8fdb\u884c\u534f\u6574\u68c0\u9a8c\uff0c\u5982\u679c\u6709\u534f\u6574\u5173\u7cfb\u540c\u6837\u53ef\u4ee5G\u68c0\u9a8c\u3002\u5426\u5219\u505a\u51fa\u6765\u6709\u53ef\u80fd\u4f1a\u662f\u4f2a\u56de\u5f52\uff0c\u6240\u4ee5\u4e4b\u524d\u7684\u51c6\u5907\u5de5\u4f5c\u6709\u70b9\u9ebb\u70e6\u3002
\u5982\u679c\u4ec5\u4ec5\u8bf4\u505aGranger\u8fd9\u4e00\u6b65\u7684\u8bdd\uff1a
1\u3001\u5047\u5b9a\u4f60\u7684\u5de5\u4f5c\u6587\u4ef6\u5df2\u7ecf\u5efa\u7acb\uff0c\u9996\u5148\u6253\u5f00\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7ec4\u7a97\u53e3\u3002
2\u3001\u70b9\u51fbview\u952e\uff0c\u9009\u62e9Granger Causality\u3002\u3002\u3002\u529f\u80fd\u3002
3\u3001\u968f\u5373\u6253\u5f00\u4e00\u4e2a\u5bf9\u8bdd\u6846\uff0c\u9700\u8981\u9009\u62e9\u6700\u5927\u6ede\u540e\u957f\u5ea6\uff0c\u7136\u540e\u70b9\u51fbok\u952e\uff0c\u5c31\u5f97\u5230\u68c0\u9a8c\u7ed3\u679c\u3002
4\u3001\u6bd4\u8f83\u4e0bP\u548cF\u503c\uff0c\u5224\u65ad\u4e0b\u662f\u5426\u62d2\u7edd\u539f\u5047\u8bbe\uff0c\u7136\u540e\u5f97\u51fa\u7ed3\u8bba\u3002
\u5e0c\u671b\u4f60\u7684\u6570\u636e\u6027\u8d28\u597d\uff0c\u505a\u7684\u987a\u5229\uff1a\uff09
\u8bf4\u5b9e\u8bdd\uff0c\u6211\u6700\u75db\u6068\u8ba1\u91cf\u7ecf\u6d4e\u5b66\u4e86\uff0c\u4e0d\u8fc7\u6211\u4e0d\u75db\u6068eviews\u3002
\u5148\u8bf4\u675c\u5bbe\u4e24\u6b65\u6cd5\uff1a
\u7b2c\u4e00\u6b65\uff0c\u4f30\u8ba1\u6a21\u578b
ls chukou c chukou(-1) gdp gdp(-1)
\uff08\u8fd9\u6b65\uff0c\u4f60\u4f1a\u5f97\u5230\u4e00\u4e2a\u56de\u5f52\u6a21\u578b\uff0c\u56de\u5f52\u6a21\u578b\u4f60\u4f1a\u770b\u5427\uff1f\u5047\u8bbe\u4f60\u5f97\u5230\u7684\u56de\u5f52\u6a21\u578b\u4e3a\uff1achukou\uff1dc\uff0b\u03b1*chukou(-1)\uff0b\u03b2*gdp\uff0b\u03bc*gdp(-1)\uff0cc\u3001\u03b1\u3001\u03b2\u3001\u03bc\u662f\u5e38\u6570\uff0c\u7531\u56de\u5f52\u7ed3\u679c\u53ef\u4ee5\u770b\u51fa\u5b83\u4eec\u7684\u5177\u4f53\u6570\u503c\u3002\uff09
\u7b2c\u4e8c\u6b65\uff0c\u4f5c\u5dee\u5206\u53d8\u6362
genr chukou0=chukou-\u03b1*chukou(-1)
genr gdp0=gdp-\u03b1*gdp(-1)
(\u6ce8\u610f\uff0c\u8fd9\u91ccchukou(-1)\u548cgdp(-1)\u524d\u9762\u7684\u7cfb\u6570\u90fd\u662f\u03b1,\u8fd9\u91cc\u7684\u03b1\u5c31\u662f\u7b2c\u4e00\u6b65\u6240\u5f97\u7684\u03b1\u503c\u3002genr\u662f\u4e00\u4e2a\u5b9a\u4e49\u7684\u547d\u4ee4\uff0c\u6211\u4eec\u8fd9\u91cc\u5c31\u662f\u5f3a\u884c\u628achukou0\u5b9a\u4e49\u4e3achukou-\u03b1*chukou(-1)\uff0c\u628agdp0\u5b9a\u4e49\u4e3agdp0=gdp-\u03b1*gdp(-1)\uff0c\u6ce8\u610fchukou0\u548cgdp0\u4e0d\u540c\u4e8echukou\u548cgdp\uff0c\u540e\u9762\u8981\u52a0\u4e2a\u201c0\u201d\uff09
\u518d\u4f5cchukou0\u5173\u4e8egdp0\u7684\u4f30\u8ba1\uff1a
ls chukou0 c gdp0
\uff08\u8fd9\u6b65\uff0c\u4f60\u53c8\u4f1a\u5f97\u5230\u4e00\u4e2a\u56de\u5f52\u6a21\u578b\uff0c\u5047\u8bbe\u4f60\u5f97\u5230\u7684\u56de\u5f52\u6a21\u578b\u4e3a\uff1achukou0\uff1d\u03bb\uff0b\u03b4*gdp0\uff0c\u03bb\u3001\u03b4\u662f\u5e38\u6570\uff0c\u7531\u56de\u5f52\u7ed3\u679c\u53ef\u4ee5\u770b\u51fa\u5b83\u4eec\u7684\u5177\u4f53\u6570\u503c\u3002\uff09
\u8ba1\u7b97\u03b20\uff1a
\u03b20\uff1d\u03bb/(1-\u03b1)
(\u8fd9\u4e2a\u5f0f\u5b50\u9700\u8981\u81ea\u5df1\u7528\u7b14\u6216\u8ba1\u7b97\u5668\u7b97\uff0c\u03bb\u3001\u03b1\u90fd\u662f\u524d\u9762\u5f97\u5230\u7684\u5e38\u6570\uff09
\u4e8e\u662f\u6d88\u9664\u5728\u4e00\u9636\u7ebf\u6027\u81ea\u76f8\u5173\u540e\u7684\u6a21\u578b\u4e3a\uff1a
chukou\uff1d\u03b20\uff0b\u03b4*gdp
\uff08\u8fd9\u91cc\uff0c\u03b20\u548c\u03b4\u90fd\u662f\u524d\u9762\u5f97\u5230\u7684\u5e38\u6570\uff0c\u4f60\u76f4\u63a5\u4ee3\u8fdb\u53bb\u5c31\u53ef\u4ee5\uff09
**********************************************
\u597d\u4e86\uff0c\u4e0b\u9762\u7814\u7a76\u8fed\u4ee3\u6cd5\uff0c\u5373\u79d1\u514b\u4f26\uff0d\u5965\u79d1\u7279\u8fed\u4ee3\u6cd5\uff1a
\u636e\u674e\u5b50\u5948\u7684\u300a\u8ba1\u91cf\u7ecf\u6d4e\u5b66\u300b\u548c\u5076\u4eec\u8001\u5e08\u6240\u6559\u6388\u7684\uff0c\u79d1\u514b\u4f26\uff0d\u5965\u79d1\u7279\u8fed\u4ee3\u6cd5\u76f4\u63a5\u4e00\u90e8\u5c31\u53ef\u4ee5\u55c4\uff5e\u4f60\u76f4\u63a5\u5728\u7a97\u53e3\u8f93\u5165
ls chukou c gdp ar(1\uff09
\u5f97\u5230\u7684\u56de\u5f52\u6a21\u578b\u5c31\u662f\u4fee\u6b63\u540e\u7684\u6a21\u578b\u4e86\u3002
\uff08\u5982\u679c\u662f2\u9636\u81ea\u76f8\u5173\uff0c\u5c31\u662f\u201cls chukou c gdp ar(1\uff09ar(2\uff09\u201d\uff0c\u4f9d\u6b64\u7c7b\u63a8\u3002ar(p\uff09\u5c31\u662f\u8868\u793a\u968f\u673a\u5e72\u6270\u9879\u7684p\u9636\u81ea\u56de\u5f52\uff0c\u5728\u4f30\u8ba1\u8fc7\u7a0b\u4e2d\u81ea\u52a8\u5b8c\u6210\u4e86\u03c11\uff0c\u03c12\uff0c...\uff0c\u03c1p\u7684\u8fed\u4ee3\u3002\u6240\u4ee5\u4e0d\u9700\u8981\u5177\u4f53\u518d\u8fed\u4ee3\u4e86\u3002\u4f60\u4eec\u8001\u5e08\u90fd\u544a\u8bc9\u4f60ls chukou c gdp ar(1)\u4e86\uff0c\u5c31\u8bf4\u660e\u4f60\u4eec\u8001\u5e08\u8bf4\u7684\u662f\u79d1\u514b\u4f26\uff0d\u5965\u79d1\u7279\u8fed\u4ee3\u6cd5\u3002\u5728\u8f6f\u4ef6\u64cd\u4f5c\u4e2d\u79d1\u514b\u4f26\uff0d\u5965\u79d1\u7279\u8fed\u4ee3\u6cd5\u7684\u547d\u4ee4\u53ea\u6709\u4e00\u6b65\uff0c\u4e0d\u8bba\u51e0\u9636\u90fd\u53ea\u6709\u4e00\u6b65\u3002\u6211\u4eec\u8001\u5e08\u8bf4\u4e86\uff0c\u5b9e\u9645\u4e2d\u79d1\u514b\u4f26\uff0d\u5965\u79d1\u7279\u8fed\u4ee3\u6cd5\u6bd4\u675c\u5bbe\u4e24\u6b65\u6cd5\u66f4\u5e38\u7528\u3002\u9996\u5148\uff0c\u79d1\u514b\u4f26\uff0d\u5965\u79d1\u7279\u8fed\u4ee3\u6cd5\u6bd4\u675c\u5bbe\u4e24\u6b65\u6cd5\u66f4\u51c6\u786e\u3002\u5176\u6b21\uff0c\u5b83\u7684\u8f6f\u4ef6\u64cd\u4f5c\u4e5f\u66f4\u7b80\u5355\u3002\uff09
\u5ffd\u5ffd\uff0c\u7ec8\u4e8e\u6253\u5b8c\u4e86\uff0c\u697c\u4e3b\u8981\u662f\u8fd8\u6709\u4e0d\u660e\u767d\uff0c\u6211\u53ef\u4ee5\u518d\u8865\u5145\u3002\u6ce8\u610f\uff0c\u524d\u9762\u7684\u03b1\u3001\u03b2\u3001\u03b4\u3001\u03bb\u3001\u03bc\u53ef\u90fd\u662f\u5177\u4f53\u6570\u5b57\u54e6\uff0c\u6211\u662f\u56e0\u4e3a\u6ca1\u6709\u5177\u4f53\u6570\u636e\uff0c\u6240\u4ee5\u5148\u7528\u8fd9\u4e9b\u7b26\u53f7\u4ee3\u66ff\u3002\u8fd8\u6709\uff0c\u8f93\u5165\u547d\u4ee4\u65f6\u201c*\u201d\uff0c\u522b\u6f0f\u4e86\uff5e\u518d\u8bf4\u660e\u4e00\u4e2a\uff0c\u5728\u8f6f\u4ef6\u4e2dchukou(-1)\u662f\u8868\u793a\u524d\u4e00\u671f\u7684\u51fa\u53e3\uff0cgdp(-1)\u662f\u8868\u793a\u524d\u4e00\u671f\u7684GDP\u3002
第二步，如果不同阶单整，下结论，二者不存在协整关系，分析结束。
如果是同阶单整，则进入下一步，OLS回归，计算出残差。
第三步，对残差进行单位根检验，如果残差平稳，则二者是协整关系。
这就是EG两步法。
绛旓細绗竴姝ワ紝ADF妫楠岋紝纭畾涓や釜鍙橀噺鏄惁鍚岄樁鍗曟暣銆傜浜屾锛屽鏋滀笉鍚岄樁鍗曟暣锛屼笅缁撹锛屼簩鑰呬笉瀛樺湪鍗忔暣鍏崇郴锛屽垎鏋愮粨鏉熴傚鏋滄槸鍚岄樁鍗曟暣锛屽垯杩涘叆涓嬩竴姝ワ紝OLS鍥炲綊锛岃绠楀嚭娈嬪樊銆傜涓夋锛屽娈嬪樊杩涜鍗曚綅鏍规楠岋紝濡傛灉娈嬪樊骞崇ǔ锛屽垯浜岃呮槸鍗忔暣鍏崇郴銆傝繖灏辨槸EG涓ゆ娉曘
绛旓細ARCH鏄宸」浜岄樁鐭╃殑鑷洖褰掕繃绋嬨傛仼鏍煎皵锛Engle 1982锛夐拡瀵笰RCH杩囩▼鎻愬嚭LM妫楠屾硶銆傝嚜鍥炲綊鏉′欢寮傛柟宸 (ARCH) 妫楠屻傝繖绉嶆楠屾柟娉曚笉鏄妸鍘熷洖褰掓ā鍨嬬殑闅忔満璇樊椤箂t 2 鐪嬩綔鏄痻t 鐨勫嚱鏁帮紝鑰屾槸鎶妔t 2 鐪嬩綔闅忔満璇樊骞虫柟椤箄t-12 鍙婂叾婊炲悗椤, ut-22 , 鈥︼紝 鐨勫嚱鏁般傚仛鍘熷亣璁撅細娈嬪樊搴忓垪涓煡閬揚闃跺害涓嶅瓨...
绛旓細鍙锛屽湪鍚勬按骞充笂x閮芥槸骞崇ǔ鐨勩傚洜姝わ紝鍙互鎶婂師搴忓垪x鐪嬪仛涓闃跺崟鏁淬傦紙浜岋級銆佸彧鏈変竴闃跺崟鏁寸殑搴忓垪鎵嶅彲浠杩涜鍗忔暣妫楠岋細鍒╃敤engle鍜実ranger鎻愬嚭鐨勪袱姝ユ楠屾硶锛氶鍏堝缓绔嬫ā鍨嬶細y=ax+c+e锛岀粨鏋滀负Y = 0.720902361403*X + 788.046309221 鍐嶅鏂圭▼鐨勬畫宸繘琛孉DF妫楠岋細Null Hypothesis: E has a unit root E...
绛旓細鏈 Johansen test is generally stricter than Engle-Granger cointegration test. However you can use the EG result to formulate the error correction model if the residuals are all stationary.
绛旓細瀵箈y鍏圓DF妫楠屽钩绋虫э紝缁撴灉涓嶆垚绔嬶紝鍙杔og涓闃跺樊鍒嗭紝鎺ュ彈銆傜劧鍚嶰LS锛屽娈嬪樊妫楠屽钩绋筹紝鑻ュ钩绋筹紝浜岃呭瓨鍦ㄥ崗鏁村叧绯 Dependent Variable: X Method: Least Squares Date: 04/04/11 Time: 21:17 Sample (adjusted): 2001 2009 Included observations: 9 after adjustments Variable Coefficient Std. Error ...
绛旓細鍦‥Views涓瑼RCH妯″瀷鏄湪璇樊鏄潯浠舵鎬佸垎甯冪殑鍋囧畾涓嬶紝閫氳繃鏋佸ぇ浼肩劧鍑芥暟鏂规硶浼拌鐨勩備緥濡傦紝瀵逛簬GARCH(1,1)锛宼 鏃舵湡鐨勫鏁颁技鐒跺嚱鏁颁负锛(5.1.7)鍏朵腑 (5.1.8)杩欎釜璇存槑閫氬父鍙互鍦ㄩ噾铻嶉鍩熷緱鍒拌В閲婏紝鍥犱负浠ｇ悊鍟嗘垨璐告槗鍟嗗彲浠ラ氳繃寤虹珛闀挎湡鍧囧肩殑鍔犳潈骞冲潎锛堝父鏁帮級锛屼笂鏈熺殑棰勬湡鏂瑰樊锛圙ARCH椤癸級鍜屽湪浠ュ墠鍚勬湡涓...
绛旓細鈥, 鐨勫嚱鏁.ARCH鏄宸」浜岄樁鐭╃殑鑷洖褰掕繃绋.鎭╂牸灏旓紙Engle 1982锛夐拡瀵笰RCH杩囩▼鎻愬嚭LM妫楠屾硶.涓轰簡鍙互姣旇緝瀹规槗鐨勮В閲,鎴戜滑鍏堣涓涓嬫粸鍚庢晥搴.鍥犲彉閲忓彈鍒拌嚜韬垨鍙︿竴瑙ｉ噴鍙橀噺鐨勫墠鍑犳湡鍊煎奖鍝嶇殑鐜拌薄绉颁负婊炲悗鏁堝簲.琛ㄧず鍓嶅嚑鏈熷肩殑鍙橀噺绉颁负婊炲悗鍙橀噺.婊炲悗鏈熷氨鏄,浜嬩欢鍙戠敓鍚庡鍚庨潰瑕佸彂鐢熶簨浠舵寔缁奖鍝嶇殑鏃堕棿.
绛旓細鈥, 鐨勫嚱鏁.ARCH鏄宸」浜岄樁鐭╃殑鑷洖褰掕繃绋.鎭╂牸灏旓紙Engle 1982锛夐拡瀵笰RCH杩囩▼鎻愬嚭LM妫楠屾硶.涓轰簡鍙互姣旇緝瀹规槗鐨勮В閲,鎴戜滑鍏堣涓涓嬫粸鍚庢晥搴.鍥犲彉閲忓彈鍒拌嚜韬垨鍙︿竴瑙ｉ噴鍙橀噺鐨勫墠鍑犳湡鍊煎奖鍝嶇殑鐜拌薄绉颁负婊炲悗鏁堝簲.琛ㄧず鍓嶅嚑鏈熷肩殑鍙橀噺绉颁负婊炲悗鍙橀噺.婊炲悗鏈熷氨鏄,浜嬩欢鍙戠敓鍚庡鍚庨潰瑕佸彂鐢熶簨浠舵寔缁奖鍝嶇殑鏃堕棿.
绛旓細鍦杩涜鍗忔暣鍒嗘瀽涔嬪墠,椤诲鍙橀噺鐨勫钩绋虫у強鍏舵粸鍚庢湡鏁拌繘琛屾楠.灏嗘嫙鐢ㄥ彉閲忓彇瀵规暟鍚,鍒╃敤EVIEWS3.0缁忔祹搴旂敤杞欢,閲囩敤ADF鏂规硶妫楠屽崟浣嶆牴,妫楠岀粨鏋滃涓:LV0锝濱(C,1,2)**,LV1锝濱(C,1,1)**,LV2锝濱(C,1,1)**,LCR锝濱(C,1,1)***,LL锝濱(C,1,1)**,LFE锝濱(C,1,1)*,LEM锝濱(C,1,1)**鍏朵腑,鍦...
绛旓細绗竴姝ワ紝ADF妫楠岋紝纭畾涓や釜鍙橀噺鏄惁鍚岄樁鍗曟暣銆傜浜屾锛屽鏋滀笉鍚岄樁鍗曟暣锛屼笅缁撹锛屼簩鑰呬笉瀛樺湪鍗忔暣鍏崇郴锛屽垎鏋愮粨鏉熴傚鏋滄槸鍚岄樁鍗曟暣锛屽垯杩涘叆涓嬩竴姝ワ紝OLS鍥炲綊锛岃绠楀嚭娈嬪樊銆傜涓夋锛屽娈嬪樊杩涜鍗曚綅鏍规楠岋紝濡傛灉娈嬪樊骞崇ǔ锛屽垯浜岃呮槸鍗忔暣鍏崇郴銆傝繖灏辨槸EG涓ゆ娉曘
