计量经济学导论(伍德里奇第三版)课后习题答案 CHAPTER 1

1、肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅

2、膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆

3、膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆

4、螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄

5、袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅

6、袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅

7、羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃

8、羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄

9、羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅

10、肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃

11、肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃

12、膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄

13、膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂

14、膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈节莁蚂膀肅蚀蚁袀芀薆蚀羂肃薂虿膄莈蒈蚈袄膁莄蚇羆莇蚂蚇聿膀薈蚆膁莅蒄螅袁膈莀螄羃莃芆螃膅膆蚅螂

15、袅蒂薁螁羇芄蒇螁肀蒀莃螀膂芃蚁蝿袁肆薇袈羄芁蒃袇肆肄荿袆螆艿莅袅羈肂蚄袅肀莈薀袄膃膀蒆袃袂莆莂袂羅腿蚁羁肇莄薇羀腿膇蒃罿衿莂葿薆肁芅莄薅膄蒁蚃薄袃芄蕿薃羆葿蒅薃肈 chapter 1 solutions to problems 1.1 (i) ideally, we could randomly assign students to classes of different sizes. that is, each student is assigned a different class size without regard to any student characteristics s

16、uch as ability and family background. for reasons we will see in chapter 2, we would like substantial variation in class sizes (subject, of course, to ethical considerations and resource constraints).(ii) a negative correlation means that larger class size is associated with lower performance. we mi

17、ght find a negative correlation because larger class size actually hurts performance.however, with observational data, there are other reasons we might find a negative relationship. for example, children from more affluent families might be more likely to attend schools with smaller class sizes, and

18、 affluent children generally score better on standardized tests. another possibility is that, within a school, a principal might assign the better students to smaller classes. or, some parents might insist their children are in the smaller classes, and these same parents tend to be more involved in

19、their childrens education. (iii) given the potential for confounding factors some of which are listed in (ii) finding a negative correlation would not be strong evidence that smaller class sizes actually lead to better performance. some way of controlling for the confounding factors is needed, and t

20、his is the subject of multiple regression analysis. 1.2 (i) here is one way to pose the question: if two firms, say a and b, are identical in allrespects except that firm a supplies job training one hour per worker more than firm b, by how much would firm as output differ from firm bs? (ii) firms ar

21、e likely to choose job training depending on the characteristics of workers. some observed characteristics are years of schooling, years in the workforce, and experience in a particular job. firms might even discriminate based on age, gender, or race. perhaps firms choose to offer training to more o

22、r less able workers, where ability might be difficult toquantify but where a manager has some idea about the relative abilities of different employees. moreover, different kinds of workers might be attracted to firms that offer more job training on average, and this might not be evident to employers

23、. (iii) the amount of capital and technology available to workers would also affect output. so, two firms with exactly the same kinds of employees would generally have different outputs if they use different amounts of capital or technology. the quality of managers would also have an effect. (iv) no

24、, unless the amount of training is randomly assigned. the many factors listed in parts (ii) and (iii) can contribute to finding a positive correlation between output and training even if job training does not improve worker productivity. 1.3 it does not make sense to pose the question in terms of ca

25、usality. economists would assume that students choose a mix of studying and working (and other activities, such as attending class,1leisure, and sleeping) based on rational behavior, such as maximizing utility subject to the constraint that there are only 168 hours in a week. we can then use statist

26、ical methods to measure the association between studying and working, including regression analysis that we cover starting in chapter 2. but we would not be claiming that one variable causes the other. they are both choice variables of the student. chapter 2solutions to problems 2.1 (i) income, age,

27、 and family background (such as number of siblings) are just a fewpossibilities. it seems that each of these could be correlated with years of education. (income and education are probably positively correlated; age and education may be negatively correlated because women in more recent cohorts have

28、, on average, more education; and number of siblings and education are probably negatively correlated.) (ii) not if the factors we listed in part (i) are correlated with educ. because we would like to hold these factors fixed, they are part of the error term. but if u is correlated with educ then e(

29、u|educ) 0, and so slr.4 fails. 2.2 in the equation y = b0 + b1x + u, add and subtract a0 from the right hand side to get y = (a0 + b0) + b1x + (u - a0). call the new error e = u - a0, so that e(e) = 0. the new intercept is a0 + b0, but the slope is still b1. 2.3 (i) let yi = gpai, xi = acti, and n =

30、 8. then = 25.875, = 3.2125, (xi )(yi ) = i=1n= 5.8125, and (xi )2 = 56.875. from equation (2.9), we obtain the slope as b1i=1n = 5.8125/56.875 .1022, rounded to four places after the decimal. from (2.17), b0 3.2125 (.1022)25.875 .5681. so we can write b1 = .5681 + .1022 act gpan = 8. the intercept

31、does not have a useful interpretation because act is not close to zero for the increases by .1022(5) = .511. population of interest. if act is 5 points higher, gpa (ii) the fitted values and residuals rounded to four decimal places are given along with the observation number i and gpa in the followi

32、ng table: 2 you can verify that the residuals, as reported in the table, sum to -.0002, which is pretty close to zero given the inherent rounding error. = .5681 + .1022(20) 2.61. (iii) when act = 20, gpa i2, is about .4347 (rounded to four decimal places), (iv) the sum of squared residuals, ui=1nnan

33、d the total sum of squares, (yi )2, is about 1.0288. so the r-squared from thei=1regression is r2 = 1 ssr/sst 1 (.4347/1.0288) .577. therefore, about 57.7% of the variation in gpa is explained by act in this small sample of students. 2.4 (i) when cigs = 0, predicted birth weight is 119.77 ounces. wh

34、en cigs = 20, bwght = 109.49.this is about an 8.6% drop. (ii) not necessarily. there are many other factors that can affect birth weight, particularly overall health of the mother and quality of prenatal care. these could be correlated withcigarette smoking during birth. also, something such as caff

35、eine consumption can affect birth weight, and might also be correlated with cigarette smoking. (iii) if we want a predicted bwght of 125, then cigs = (125 119.77)/( .524) 10.18, or about 10 cigarettes! this is nonsense, of course, and it shows what happens when we are trying to predict something as

36、complicated as birth weight with only a single explanatory variable. the largest predicted birth weight is necessarily 119.77. yet almost 700 of the births in the sample had a birth weight higher than 119.77. 3 (iv) 1,176 out of 1,388 women did not smoke while pregnant, or about 84.7%. because we ar

37、e using only cigs to explain birth weight, we have only one predicted birth weight at cigs = 0. the predicted birth weight is necessarily roughly in the middle of the observed birth weights at cigs = 0, and so we will under predict high birth rates. 2.5 (i) the intercept implies that when inc = 0, c

38、ons is predicted to be negative $124.84. this, of course, cannot be true, and reflects that fact that this consumption function might be a poor predictor of consumption at very low-income levels. on the other hand, on an annual basis, $124.84 is not so far from zero. = 124.84 + .853(30,000) = 25,465

39、.16 dollars. (ii) just plug 30,000 into the equation: cons (iii) the mpc and the apc are shown in the following graph. even though the intercept is negative, the smallest apc in the sample is positive. the graph starts at an annual income levelincreases housing prices. (ii) if the city chose to loca

40、te the incinerator in an area away from more expensiveneighborhoods, then log(dist) is positively correlated with housing quality. this would violate slr.4, and ols estimation is biased. (iii) size of the house, number of bathrooms, size of the lot, age of the home, and quality of the neighborhood (

41、including school quality), are just a handful of factors. as mentioned in part (ii), these could certainly be correlated with dist and log(dist).4 2.7 (i) when we condition on ince(u|ince|inc) = e(e|inc0 because e(e|inc) = e(e) = 0. (ii) again, when we condition on incvar(u|ince|inc2var(e|inc) = se2

42、inc because var(e|inc) = se2. (iii) families with low incomes do not have much discretion about spending; typically, a low-income family must spend on food, clothing, housing, and other necessities. higher income people have more discretion, and some might choose more consumption while others more s

43、aving. this discretion suggests wider variability in saving among higher income families. 2.8 (i) from equation (2.66), nn2%b1 = xiyi / xi. i=1i=1 plugging in yi = b0 + b1xi + ui gives nn2%b1 = xi(b0+b1xi+ui)/ xi. i=1i=1 after standard algebra, the numerator can be written as b0xi+b1x+xiui. 2nnni=1i

44、=1ii=1% as putting this over the denominator shows we can write b1 nn2nn2%b1 = b0xi/ xi + b1 + xiui/ xi. i=1i=1i=1i=1 conditional on the xi, we have nn2%e(b1) = b0xi/ xi + b1 i=1i=1% is given by the first term in this equation. because e(ui) = 0 for all i. therefore, the bias in b1this bias is obvio

45、usly zero when b0 = 0. it is also zero when xi = 0, which is the same asi=1n= 0. in the latter case, regression through the origin is identical to regression with an intercept.5 %in part (i) we have, conditional on the xi, (ii) from the last expression for b1%) var(b1n2nn2n2= xivarxiui = xixivar(ui)

46、 i=1i=1i=1i=1-2-2-2 nn22n22= xisxi = s/ xi2. i=1i=1i=1nnn222(iii) from (2.57), var(b1) = s/(xi-). from the hint, xi (xi-)2, and soi=1i=1i=1nn%) var(b). a more direct way to see this is to write (x-)2 = x2-n()2, which var(bii11i=1i=1is less than xi2 unless = 0.i=1n % increases as increases (holding t

47、he sum of the (iv) for a given sample size, the bias in b1increases relative to var(b%). the bias in b% x2 fixed). but as increases, the variance of bi111% or b on a mean squared error is also small when b0 is small. therefore, whether we prefer b11basis depends on the sizes of b0, , and n (in addit

48、ion to the size of xi2).i=1n 2.9 (i) we follow the hint, noting that c1y = c1 (the sample average of c1yi is c1 times the sample average of yi) and c2x = c2. when we regress c1yi on c2xi (including an intercept) we use equation (2.19) to obtain the slope:%=b1(cx-cx)(c-c)cc(x-)(y-)2in21i112niii=1nn=i

49、=1(cx-c)2i2i=1nn2c(x-)22ii=12 c1=i=1c2(xi-)(yi-)=2i(x-)i=1c1b1.c2% = (c1) b%(c2) = (c1) (c1/c2)b(c2) = from (2.17), we obtain the intercept as b011) because the intercept from regressing yi on xi is ( b) = c1b). c1( b1 1 (ii) we use the same approach from part (i) along with the fact that (c1+y) = c

50、1 + and(c2+x) = c2 + . therefore, (c1+yi)-(c1+y) = (c1 + yi) (c1 + ) = yi and (c2 + xi) 6(c2+x) = xi . so c1 and c2 entirely drop out of the slope formula for the regression of (c1 + yi)% = (c+y) b% = b. the intercept is b%(c+x) = (c1 + ) b(c2 + ) = on (c2 + xi), and b1101121 + c1 c2b) + c1 c2b = b,

51、 which is what we wanted to show. (-b0111 (iii) we can simply apply part (ii) because log(c1yi)=log(c1)+log(yi). in other words, replace c1 with log(c1), yi with log(yi), and set c2 = 0. (iv) again, we can apply part (ii) with c1 = 0 and replacing c2 with log(c2) and xi with log(xi). and b are the o

52、riginal intercept and slope, then b%=b and b%=b-log(c)b. if b01110021 2.10 (i) this derivation is essentially done in equation (2.52), once (1/sstx) is brought inside the summation (which is valid because sstx does not depend on i). then, just definewi=di/sstx. ,)=e(b-b) , we show that the latter is

53、 zero. but, from part (i), (ii) because cov(b111n=nwe(u). because the u are pairwise uncorrelated -b) =ee(bwui11ii=1iii=1i(they are independent), e(ui)=e(ui2/n)=s2/n (because e(uiuh)=0, ih). therefore, ()i=1wie(ui)=i=1wi(s2/n)=(s2/n)i=1wi=0. nnn=-b and, plugging in =b+b+ (iii) the formula for the ol

54、s intercept isb010=(b+b+)-b=b+-(b-b) gives b0011011 and are uncorrelated, (iv) because b1)=var()+var(b)2=s2/n+(s2/sst)2=s2/n+s22/sst, var(b01xxwhich is what we wanted to show. )=s2(sst/n)+2/sst (v) using the hint and substitution gives var(b0xx-12222-12=s2nx-+/sst=snx/sstx. xi=1ii=1i 2.11 (i) we wou

55、ld want to randomly assign the number of hours in the preparation course so that hours is independent of other factors that affect performance on the sat. then, we would collect information on sat score for each student in the experiment, yielding a data set (sati,hoursi):i=1,.,n, where n is the num

56、ber of students we can afford to have in the study. from equation (2.7), we should try to get as much variation in hoursi as is feasible.nn()()7(ii) here are three factors: innate ability, family income, and general health on the day of the exam. if we think students with higher native intelligence think they do not need to prepare for the sat, then ability and hours will be negatively correlated. family income would probably be positively correlated with hours, because higher income families can more easily affordpreparation courses. ruling out chron