stata课堂命令讲解

第六周课 多元统计分析

Manova检验

H0：各个总体的均值相同

均类分析 （平均法）

. clusteraveragelinkage price mpg weight length cluster name: _clus_1 . edit - preserve

. cluster list _clus_1

_clus_1 (type: hierarchical, method: average, dissimilarity: L2) vars: _clus_1_id (id variable)

_clus_1_ord (order variable) _clus_1_hgt (height variable)

other: cmd: cluster averagelinkage price mpg weight length varlist: price mpg weight length range: 0 .

. list _clus_1_id _clus_1_ord _clus_1_hgt 用分析谱系图（不超过50个样本）来分析 clusterdendrogram _clus_1

cluster dendrogram _clus_1 ,horizontal【水平版本】 另一种结构，产生差异结果的话说明数据本身不稳健。 . clusterwardslinkage price mpg weight length cluster name: _clus_2

. cluster wardslinkage price mpg weight length, name(class1)#自己命名生成的变量class#

. clusterdendrogram class1

生成相似矩阵ma1，希望在此基础上进行矩阵分析 . matrix dissimilarity ma1=price mpg weight length 构造简单链接singlelinkage . clear

.clustermatsinglelinkage ma1 obs was 0, now 74 cluster name: _clus_1

. clusterdendrogram _clus_1

在谱系图的基础上，分成k()类命名为class2等选项，研究样本个体具体的属类情况。

clusterkmeans price mpg weight length, k(4) name (class2)

. list class2

+--------+ | class2 | |--------| 1. | 1 | 2. | 4 | 3. | 1 | 4. | 4 | 5. | 3 | |--------| 6. | 4 | 7. | 1 | 8. | 4 | 9. | 2 | 10. | 1 | |--------| 11. | 2 | 12. | 2 | 13. | 2 | 14. | 1 | 15. | 4 | |--------| 16. | 1 | 17. | 4 | 18. | 1 | 19. | 1 | 20. | 1 | |--------| 21. | 1 | 22. | 4 | 23. | 4 | 24. | 1 | 25. | 1 | |--------| 26. | 2 | 27. | 2 | 28. | 2 | 29. | 1 | 30. | 4 |

|--------| 31. | 4 | 32. | 1 | 33. | 4 | 34. | 1 | 35. | 3 | |--------| 36. | 4 | 37. | 4 | 38. | 4 | 39. | 1 | 40. | 1 | |--------| 41. | 2 | 42. | 1 | 43. | 1 | 44. | 1 | 45. | 4 | |--------| 46. | 1 | 47. | 4 | 48. | 4 | 49. | 4 | 50. | 4 | |--------| 51. | 1 | 52. | 1 | 53. | 3 | 54. | 4 | 55. | 3 | |--------| 56. | 4 | 57. | 1 | 58. | 1 | 59. | 3 | 60. | 1 | |--------| 61. | 4 | 62. | 1 | 63. | 1 | 64. | 2 | 65. | 1 | |--------| 66. | 1 |

67. | 4 | 68. | 1 | 69. | 4 | 70. | 3 | |--------| 71. | 4 | 72. | 1 | 73. | 3 | 74. | 2 | +--------+

. list price lass2

variable lass2 not found r(111);

. list price class2

+-----------------+ | price class2 | |-----------------| 1. | 4,099 1 | 2. | 4,749 4 | 3. | 3,799 1 | 4. | 4,816 4 | 5. | 7,827 3 | |-----------------| 6. | 5,788 4 | 7. | 4,453 1 | 8. | 5,189 4 | 9. | 10,372 2 | 10. | 4,082 1 | |-----------------| 11. | 11,385 2 | 12. | 14,500 2 | 13. | 15,906 2 | 14. | 3,299 1 | 15. | 5,705 4 | |-----------------| 16. | 4,504 1 | 17. | 5,104 4 | 18. | 3,667 1 | 19. | 3,955 1 | 20. | 3,984 1 | |-----------------|

21. | 4,010 1 | 22. | 5,886 4 | 23. | 6,342 4 | 24. | 4,389 1 | 25. | 4,187 1 | |-----------------| 26. | 11,497 2 | 27. | 13,594 2 | 28. | 13,466 2 | 29. | 3,829 1 | 30. | 5,379 4 | |-----------------| 31. | 6,165 4 | 32. | 4,516 1 | 33. | 6,303 4 | 34. | 3,291 1 | 35. | 8,814 3 | |-----------------| 36. | 5,172 4 | 37. | 4,733 4 | 38. | 4,890 4 | 39. | 4,181 1 | 40. | 4,195 1 | |-----------------| 41. | 10,371 2 | 42. | 4,647 1 | 43. | 4,425 1 | 44. | 4,482 1 | 45. | 6,486 4 | |-----------------| 46. | 4,060 1 | 47. | 5,798 4 | 48. | 4,934 4 | 49. | 5,222 4 | 50. | 4,723 4 | |-----------------| 51. | 4,424 1 | 52. | 4,172 1 | 53. | 9,690 3 | 54. | 6,295 4 | 55. | 9,735 3 | |-----------------| 56. | 6,229 4 | 57. | 4,589 1 |

58. | 5,079 1 | 59. | 8,129 3 | 60. | 4,296 1 | |-----------------| 61. | 5,799 4 | 62. | 4,499 1 | 63. | 3,995 1 | 64. | 12,990 2 | 65. | 3,895 1 | |-----------------| 66. | 3,798 1 | 67. | 5,899 4 | 68. | 3,748 1 | 69. | 5,719 4 | 70. | 7,140 3 | |-----------------| 71. | 5,397 4 | 72. | 4,697 1 | 73. | 6,850 3 | 74. | 11,995 2 | +-----------------+

. tabstat price mpg weight length, by(class2)

Summary statistics: mean by categories of: class2

class2 | price mpg weight length ---------+----------------------------------------

第 1类 | 4163.938 24.5625 2581.25 174.7813 第2类 | 12607.6 15 4041 209.1 第3类 | 8312.143 21 2931.429 188.5714 4 | 5548.88 19.72 3196.4 196.12 ---------+----------------------------------------

Total | 6165.257 21.2973 3019.459 187.9324 -------------------------------------------------- .

反过来做多元方差分析，检验分类是否有效。看均值差异情况。P显著小于0，分类成功。

. manova price mpg weight length=class2

Number of obs = 74

W = Wilks' lambda L = Lawley-Hotelling trace

P = Pillai's trace R = Roy's largest root

Source | Statisticdf F(df1, df2) = F Prob>F -----------+--------------------------------------------------

class2 | W 0.0549 3 12.0 177.6 29.52 0.0000 a

| P 1.2125 12.0 207.0 11.70 0.0000 a

| L 12.6572 12.0 197.0 69.26 0.0000 a

| R 12.3147 4.0 69.0 212.43 0.0000 u

|-------------------------------------------------- Residual | 70

-----------+-------------------------------------------------- Total | 73

--------------------------------------------------------------

e = exact, a = approximate, u = upper bound on F 使用中位数kmedians重整，分类情况发生改变

. clusterkmedians price mpg weight length, k(4) name(class3)

. list class2 class3

+-----------------+ | class2 class3 | |-----------------|

1. | 1 2 | 2. | 4 2 | 3. | 1 3 | 4. | 4 2 | 5. | 3 1 | |-----------------|

6. | 4 1 | 7. | 1 3 | 8. | 4 2 | 9. | 2 4 | 10. | 1 2 | |-----------------|

11. | 2 4 | 12. | 2 4 | 13. | 2 4 | 14. | 1 3 |

15. | 4 2 | |-----------------|

16. | 1 2 | 17. | 4 2 | 18. | 1 3 | 19. | 1 2 | 20. | 1 3 | |-----------------|

21. | 1 2 | 22. | 4 23. | 4 24. | 1 25. | 1 |-----------------|

26. | 2 27. | 2 28. | 2 29. | 1 30. | 4 |-----------------|

31. | 4 32. | 1 33. | 4 34. | 1 35. | 3 |-----------------|

36. | 4 37. | 4 38. | 4 39. | 1 40. | 1 |-----------------|

41. | 2 42. | 1 43. | 1 44. | 1 45. | 4 |-----------------|

46. | 1 47. | 4 48. | 4 49. | 4 50. | 4 |-----------------|

1 | 1 | 3 | 3 | 4 | 4 | 4 | 3 | 2 | 1 | 2 | 1 | 3 | 4 | 2 | 2 | 2 | 2 | 3 | 4 | 2 | 3 | 3 | 1 | 2 | 1 | 2 | 2 | 2 | 51. | 1 2 | 52. | 1 3 | 53. | 3 4 | 54. | 4 1 | 55. | 3 4 | |-----------------|

56. | 4 1 | 57. | 1 3 | 58. | 1 3 | 59. | 3 1 | 60. | 1 3 | |-----------------|

61. | 4 1 | 62. | 1 3 | 63. | 1 3 | 64. | 2 4 | 65. | 1 3 | |-----------------|

66. | 1 3 | 67. | 4 1 | 68. | 1 3 | 69. | 4 1 | 70. | 3 1 | |-----------------|

71. | 4 1 | 72. | 1 3 | 73. | 3 1 | 74. | 2 4 | +-----------------+ .

停止K聚类分析，根据设定的规则。 Pseudo F统计量calinaki

. cluster stop class3, rule(calinski)

+---------------------------+

| | Calinski/ | | Number of | Harabasz | | clusters | pseudo-F | |-------------+-------------|

| 4 | 151.37 | +---------------------------+

在分类图中的个体赋予类别界定线

. clusteraveragelinkage price mpg weight length cluster name: _clus_1

. cluster generate clus5= cut(3500), name( _clus_1)

. list clus5

+-------+ | clus5 | |-------| 1. | 1 | 2. | 1 | 3. | 1 | 4. | 1 | 5. | 2 | |-------| 6. | 1 | 7. | 1 | 8. | 1 | 9. | 2 | 10. | 1 | |-------| 11. | 2 | 12. | 3 | 13. | 3 | 14. | 1 | 15. | 1 | |-------| 16. | 1 | 17. | 1 | 18. | 1 | 19. | 1 | 20. | 1 | |-------| 21. | 1 | 22. | 1 | 23. | 1 | 24. | 1 | 25. | 1 | |-------| 26. | 2 | 27. | 3 | 28. | 3 | 29. | 1 |

30. | 1 | |-------| 31. | 1 | 32. | 1 | 33. | 1 | 34. | 1 | 35. | 2 | |-------| 36. | 1 | 37. | 1 | 38. | 1 | 39. | 1 | 40. | 1 | |-------| 41. | 2 | 42. | 1 | 43. | 1 | 44. | 1 | 45. | 1 | |-------| 46. | 1 | 47. | 1 | 48. | 1 | 49. | 1 | 50. | 1 | |-------| 51. | 1 | 52. | 1 | 53. | 2 | 54. | 1 | 55. | 2 | |-------| 56. | 1 | 57. | 1 | 58. | 1 | 59. | 2 | 60. | 1 | |-------| 61. | 1 | 62. | 1 | 63. | 1 | 64. | 3 | 65. | 1 | |-------|

66. | 1 | 67. | 1 | 68. | 1 | 69. | 1 | 70. | 1 | |-------| 71. | 1 | 72. | 1 | 73. | 1 | 74. | 2 | +-------+ .

聚类分析到此为止，接下来继续讲判定分析 线性+非线性+其他

先看线性判定分析discrimlda（线性的）

. discrimlda price mpg weight length,group(foreign)

Linear discriminant analysis

Resubstitution classification summary

+---------+ | Key | |---------| | Number | | Percent | +---------+

| Classified True foreign | Domestic Foreign | Total -------------+--------------------+---------

Domestic | 43 9 | 52

| 82.69 domestic判别正确率 17.31 | | | Foreign | 0 22 | 22

| 0.00 100.00 foreign判别正确率 | -------------+--------------------+---------

Total | 43 31 | 74 | 58.11 41.89 | 100.00 | | Priors | 0.5000 0.5000 |

.根据分析结果进行下一步检验estat . estatclasstable

100.00 100.00

Resubstitution classification table

+---------+ | Key | |---------| | Number | | Percent | +---------+

| Classified True foreign | Domestic Foreign | Total -------------+--------------------+---------

Domestic | 43 9 | 52 | 82.69 17.31 | 100.00 | | Foreign | 0 22 | 22 | 0.00 100.00 | 100.00 -------------+--------------------+---------

Total | 43 31 | 74 | 58.11 41.89 | 100.00 | | Priors | 0.5000 0.5000 |

. eatatcorr

unrecognized command: eatat r(199);

. estatcorr

Pooled within-group correlation matrix

| price mpg weight length -------------+---------------------------------------- price | 1.00000 mpg | -0.53117 1.00000 weight | 0.70551 -0.77521 1.00000 length | 0.56014 -0.75664 0.91898 1.00000

. estat covariance

Pooled within-group covariance matrix

| price mpg weight length -------------+--------------------------------------------

price | 8799417 mpg | -8438.941 28.6848 weight | 1318950 -2616.617 397186.1 length | 30603.94 -74.63974 10667.35 339.2432

. estaterrorrate错判率

Error rate estimated by error count

| foreign

| Domestic Foreign | Total

-------------+----------------------+----------

Error rate | .1730769 0 | .0865385 -------------+----------------------+----------

Priors | .5 .5 | .

. estatgrsum四个变量的分组描述性统计差异情况

Estimation sample discrimlda Summarized by foreign

| foreign

Mean | Domestic Foreign | Total -------------+----------------------+---------- price | 6072.423 6384.682 | 6165.257 mpg | 19.82692 24.77273 | 21.2973 weight | 3317.115 2315.909 | 3019.459 length | 196.1346 168.5455 | 187.9324 -------------+----------------------+----------

N | 52 22 | 74

每个变量进行方差分析. . estatanova

Univariate ANOVA summaries

| Adj.

Variable | Model MS Resid MS Total MS R-sq R-sq Pr> F

-------------+-------------------------------------------------------------

price | 1507382.7 6.336e+08 6.249e+08 .0024 -.0115 .1713 0.6802 mpg | 378.15352 2065.3059 2042.1943 .1548 .143 13.18 0.0005

F weight | 15496779 28597399 28417939 .3514 .3424 39.02 0.0000 length | 11767.15 24425.512 24252.11 .3251 .3158 34.69 0.0000 ---------------------------------------------------------------------------

Number of obs = 74 Model df = 1 Residual df = 72 生成的判别函数？——典型判别函数法canontest . estatcanontest

Canonical linear discriminant analysis

| | Like- | Canon. Eigen- Variance | lihood

Fcn | Corr.value Prop. Cumul. | Ratio F df1 df2 Prob>F ----+---------------------------------+------------------------------------

1 | 0.7494 1.28083 1.0000 1.0000 | 0.4384 22.094 4 0.0000 e

---------------------------------------------------------------------------

Ho: this and smaller canon. corr. are zero; e = exact F 调用多元函数形式 . estat loadings

Standardized canonical discriminant function coefficients

| function1 -------------+----------- price | -1.084153 mpg | .3115969 weight | 2.04874 length | -.4264069

显示分类函数 statclassfunction

Classification functions

| foreign | Domestic Foreign

-------------+---------------------- price | .0013868 .0022795 mpg | 4.577349 4.435253 weight | -.0341788 -.0421185 length | 2.534884 2.591428

_cons | -241.4898 -231.8288 -------------+----------------------

Priors | .5 .5

69

接下来

说明非线性判别

. discrimlda price mpg weight length,group(clus5)

Linear discriminant analysis

Resubstitution classification summary

+---------+ | Key | |---------| | Number | | Percent | +---------+

| Classified True clus5 | 1 2 3 | Total -------------+------------------------+-------

1 | 59 0 0 | 59 | 100.00 0.00 0.00 | 100.00 | | 2 | 0 10 0 | 10 | 0.00 100.00 0.00 | 100.00 | | 3 | 0 0 5 | 5 | 0.00 0.00 100.00 | 100.00 -------------+------------------------+-------

Total | 59 10 5 | 74 | 79.73 13.51 6.76 | 100.00

| | Priors | 0.3333 0.3333 0.3333 | 三类有两个判别函数 前文皆在此成立 . estat loadings

Standardized canonical discriminant function coefficients

| function1 function2 -------------+---------------------- price | .9994096 .0619517 mpg | .1147418 .3214297 weight | .7125425 -2.05314 length | -.6690039 2.838476

多元方程分析 . estat loadings

Standardized canonical discriminant function coefficients

| function1 function2 -------------+---------------------- price | .9994096 .0619517 mpg | .1147418 .3214297 weight | .7125425 -2.05314 length | -.6690039 2.838476

. estatgrsum

Estimation sample discrimlda Summarized by clus5

| clus5 Mean | 1 2 3 | Total -------------+---------------------------------+----------

price | 4846.746 9981.5 14091.2 | 6165.257 mpg | 22.49153 17.4 15 | 21.2973 weight | 2824.746 3662 4032 | 3019.459 length | 183.4576 205.2 206.2 | 187.9324 -------------+---------------------------------+----------

N | 59 10 5 | 74

. estatmanova

Number of obs = 74

W = Wilks' lambda L = Lawley-Hotelling trace

P = Pillai's trace R = Roy's largest root

Source | Statisticdf F(df1, df2) = F Prob>F -----------+--------------------------------------------------

clus5 | W 0.1043 2 8.0 136.0 35.63 0.0000 e

| P 0.9304 8.0 138.0 15.00 0.0000 a

| L 8.2507 8.0 134.0 69.10 0.0000 a

| R 8.2102 4.0 69.0 141.63 0.0000 u

|--------------------------------------------------

Residual | 71

-----------+-------------------------------------------------- Total | 73

--------------------------------------------------------------

e = exact, a = approximate, u = upper bound on F .

两次判别qta提高判别率

. discrimqda price mpg weight length, group(foreign)

Quadratic discriminant analysis

Resubstitution classification summary

+---------+ | Key | |---------| | Number | | Percent | +---------+

| Classified True foreign | Domestic Foreign | Total -------------+--------------------+---------

Domestic | 45 7 | 52

| 86.54（提高啦！！！！） | | Foreign | 0 22 | 22 | 0.00 100.00 | 100.00 -------------+--------------------+---------

Total | 45 29 | 74 | 60.81 39.19 | 100.00 | | Priors | 0.5000 0.5000 | 原来的——判别之后的类别及概率 .. estat list

+------------------------------------------------+

| | Classification | Probabilities |

| | | | Obs.| True Class. | Domestic Foreign | |-----+----------------------+-------------------|

| 1 | Domestic Domestic | 1.0000 0.0000 | | 2 | Domestic Domestic | 1.0000 0.0000 | | 3 | Domestic Domestic | 0.9935 0.0065 | | 4 | Domestic Domestic | 1.0000 0.0000 |

13.46 | | 100.00 | 5 | Domestic Domestic | 1.0000 0.0000 | |-----+----------------------+-------------------|

| 6 | Domestic Domestic | 1.0000 0.0000 | | 7 | Domestic Foreign * | 0.2235 0.7765 | | 8 | Domestic Domestic | 1.0000 0.0000 | | 9 | Domestic Domestic | 1.0000 0.0000 | | 10 | Domestic Domestic | 1.0000 0.0000 | |-----+----------------------+-------------------|

| 11 | Domestic Domestic | 1.0000 0.0000 | | 12 | Domestic Domestic | 0.8931 0.1069 | | 13 | Domestic Domestic | 1.0000 0.0000 | | 14 | Domestic Foreign * | 0.2789 0.7211 | | 15 | Domestic Domestic | 1.0000 0.0000 |

总体错判率 . estaterrorrate

Error rate estimated by error count

| foreign

| Domestic Foreign | Total

-------------+----------------------+----------

Error rate | .1346154 （降低了！！！） 0 | .0673077 -------------+----------------------+----------

Priors | .5 .5 |【实际上不一样】 . sum foreign

Variable | Obs Mean Std. Dev. Min -------------+--------------------------------------------------------

foreign | 74 .29729730.3 .4601885 0 改变线性概率再检验

. discrimlda price mpg weight length, group(foreign) priors(0.7,0.3)

Linear discriminant analysis

Resubstitution classification summary

+---------+ | Key | |---------| | Number | | Percent | +---------+

| Classified True foreign | Domestic Foreign | Total

Max 1 -------------+--------------------+---------

Domestic | 44 8 | 52 | 84.62 15.38 | 100.00 | | Foreign | 1 21 | 22 | 4.55 95.45 | 100.00 -------------+--------------------+---------

Total | 45 29 | 74 | 60.81 39.19 | 100.00 | | Priors | 0.7000 0.3000 |

主成份分析

对数据进行降维处理PCA选几个变量出来几个主成份 . pca price mpg weight length turn

Principal components/correlation Number of obs = 74

Number of comp. = 5

Trace = 5

Rotation: (unrotated = principal) Rho = 1.0000

--------------------------------------------------------------------------

Component | Eigenvalue Difference Proportion Cumulative

-------------+------------------------------------------------------------

Comp1 | 3.77621 3.01112 0.7552 0.7552

Comp2 | .76509 .488796 0.1530【两个占总体的90%以上，故选这两个】 0.9083

Comp3 | .276294 .139261 0.0553 0.9635

Comp4 | .137033 .0916582 0.0274 0.9909

Comp5 | .0453749 . 0.0091 1.0000 --------------------------------------------------------------------------

Principal components (eigenvectors)

------------------------------------------------------------------------------

Variable | Comp1 Comp2 Comp3 Comp4 Comp5 | Unexplained

-------------+--------------------------------------------------+-------------

price | 0.3014 0.9183 0.1662 -0.1572 0.1160 | 0 mpg | -0.4558 0.0192 0.8754 0.1600 -0.0053 | 0 weight | 0.5000 -0.0386 0.1883 0.3737 -0.7573 | 0 length | 0.4908 -0.1795 0.1649 0.5386 0.6400 | 0 turn | 0.4589 -0.3501 0.3788 -0.7210 0.0591 | 0 ------------------------------------------------------------------------------ 进一步分析，各个主成份的载荷矩阵 . estat loadings

Principal component loadings (unrotated)

component normalization: sum of squares(column) = 1

----------------------------------------------------------------

| Comp1 Comp2 Comp3 Comp4 Comp5

-------------+--------------------------------------------------

price | .3014 .9183 .1662 -.1572 .116 mpg | -.4558 .01923 .8754 .16 -.005314 weight | .5 -.03859 .1883 .3737 -.7573 length | .4908 -.1795 .1649 .5386 .64 turn | .4589 -.3501 .3788 -.721 .05914 ---------------------------------------------------------------- 因素分析

.. factor price mpg weight length turn (obs=74)

Factor analysis/correlation Number of obs = 74

Method: principal factors Retained factors = 2

Rotation: (unrotated) Number of params = 9

--------------------------------------------------------------------------

Factor | Eigenvalue Difference Proportion Cumulative -------------+------------------------------------------------------------

Factor1 | 3.57602 3.31462 0.9655【第一个因子较大】 0.9655

Factor2 | 0.26140 0.28064 0.0706 1.0361 Factor3 |-0.01923 0.00881 -0.0052 1.0309

Factor4 | -0.02804 0.05822 -0.0076 1.0233

Factor5 | -0.08627【出现负数，数据有问题】 . -0.0233 1.0000

--------------------------------------------------------------------------

LR test: independent vs. saturated: chi2(10) = 379.35 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

-------------------------------------------------

Variable | Factor1 Factor2 | Uniqueness -------------+--------------------+-------------- price | 0.5056 0.4222 | 0.5662 mpg | -0.8303 -0.0729 | 0.3052 weight | 0.9793 0.0517 | 0.0383 length | 0.9591 -0.1122 | 0.0676 turn | 0.8672 -0.2502 | 0.1853 -------------------------------------------------

作球形演变，不拒绝H0，故数据不适合做因素分析。做主成份分析 . estatkmo

Kaiser-Meyer-Olkin measure of sampling adequacy

-----------------------

Variable | kmo -------------+--------- price | 0.6831 mpg | 0.9609 weight | 0.7476 length | 0.7924 turn | 0.8932

-------------+--------- Overall | 0.8179

-----------------------

继续主成份分析，并做碎石图。

. pca price mpg weight length

Principal components/correlation Number of obs 74

Number of comp. = 4

Trace 4

Rotation: (unrotated = principal) Rho 1.0000

--------------------------------------------------------------------------

= = = Component | Eigenvalue Difference Proportion Cumulative

-------------+------------------------------------------------------------

Comp1 | 3.04003 2.37073 0.7600 0.7600

Comp2 | .669304 .42455 0.1673 0.9273

Comp3 | .244754 .198846 0.0612 0.9885

Comp4 | .0459085 . 0.0115 1.0000 --------------------------------------------------------------------------

Principal components (eigenvectors)

--------------------------------------------------------------------

Variable | Comp1 Comp2 Comp3 Comp4 | Unexplained

-------------+----------------------------------------+-------------

price | 0.3775 0.9196 0.0240 0.1057 | 0 mpg | -0.5145 0.1901 0.8361 -0.0063 | 0 weight | 0.5521 -0.1521 0.3688 -0.7321 | 0 length | 0.5366 -0.3082 0.4053 0.6729 | 0 --------------------------------------------------------------------

. screeplot

构造两个主成份，根据得分画图scoreplot . predict sc1 sc2 (score assumed)

(2 components skipped)

Scoring coefficients

sum of squares(column-loading) = 1

------------------------------------------------------

Variable | Comp1 Comp2 Comp3 Comp4 -------------+---------------------------------------- price | 0.3775 0.9196 0.0240 0.1057 mpg | -0.5145 0.1901 0.8361 -0.0063 weight | 0.5521 -0.1521 0.3688 -0.7321 length | 0.5366 -0.3082 0.4053 0.6729 ------------------------------------------------------ .

作旋转，正交（默认）+斜交

. rotate

Principal components/correlation Number of obs = 74

Number of comp. = 4

Trace = 4

Rotation: orthogonal varimax (Kaiser off) Rho = 1.0000

--------------------------------------------------------------------------

Component | Variance Difference Proportion Cumulative

-------------+------------------------------------------------------------

Comp1 | 1.00001 9.27638e-06 0.2500 0.2500

Comp2 | 1 4.40853e-07 0.2500 0.5000

Comp3 | 1 .0000268949 0.2500 0.7500

Comp4 | .999977 . 0.2500 1.0000 --------------------------------------------------------------------------

Rotated components 旋转后结果，每个成分代表一个变量，便于成分定义。

--------------------------------------------------------------------

Variable | Comp1 Comp2 Comp3 Comp4 | Unexplained

-------------+----------------------------------------+-------------

price | -0.0000 0.0000 1.0000 -0.0000 | 0 mpg | 0.0000 1.0000 -0.0000 0.0000 | 0 weight | 1.0000 -0.0000 0.0000 -0.0000 | 0 length | 0.0000 -0.0000 0.0000 1.0000 | 0 --------------------------------------------------------------------

Component rotation matrix

------------------------------------------------------

| Comp1 Comp2 Comp3 Comp4 -------------+----------------------------------------

Comp1 | 0.5521 -0.5145 0.3775 0.5366 Comp2 | -0.1521 0.1901 0.9196 -0.3082 Comp3 | 0.3688 0.8361 0.0240 0.4053 Comp4 | -0.7321 -0.0063 0.1057 0.6729

------------------------------------------------------ .

第七周课 时间序列

.sysuse gdp96 .tsset

.ac gnp96, lags(20) .pac gnp96, lag(30) .scatter gnp96 date 验证趋势性

.wntestq gnp96，lags(40) H0原数据是portmanteau白噪声序列

另一种白噪声检验

谱密度图

. pergram gnp96

累计谱密度图 cumsp gnp96

#估计#

. arimad1.gnp96#作一阶差分#,arima(2,1,2)

Ar一阶差分 Ma 随意误差项

对于不显著的两个L2项可以去掉 . arima D1. gnp96, arima(2,0,2)

接下来引入季节差分

. arima gnp96, arima(2,1,2) sarima(0,1,1,4)#季度为4，月份为12#

在此基础上作预测（对差分后的预测）扩充了四个季度的值

再加上y以保证自由度进行预测

预测残差

建立arch 3/2模型——不收敛= =！！（或许是不具有方差性的原因）

检验序列是否平稳

不拒绝H0，表明不平稳（金融数据特征）。H0:正常平行。

一阶差分后变平稳。

一阶差分后的数据和date的图表明数据在围绕一个趋势上下波动 scatter d1.gnp96 date

菲利普斯检验——不拒绝H0

面板数据

时间序列和截面数据的结合 Xtset id year

Xtset id year,yearly Xtsumcpip

分类数据的汇总 Xttabcp 变化趋势图 Xtlinecp

.xtlinecp, overlay放在一张图中的所有曲线，便于比较关系

主要介绍静态模型 1. 混合模型 .regcpip

.xtregcpip, fe固定效应结果【假定个体不同】【看F检验结果】 predictfix_indic,u

listfix_indic #预测个体效应#

.xtregcpip, re 随机效应结果 GLS回归 对随机效应模型考虑用其他方法估计 .xtregcpip, mle最大似然估计 .xtregcpip, be 组间回归 [系数应大体稳定] 2. 总体模型 .xtregcpip,pa

验证使用固定效应or随机效应模型hausman检验 xtregcpip, re 随机效应 est store random 保存

xtregcpip, fe固定效应 est store fix 保存 hausman fix random 检验 拒绝H0 用固定效应模型

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