r - PCSE结果解读

Question

嗨，我希望在解释有关进行的回归分析的结果方面得到一些帮助：

这是可重现的示例：

# A tibble: 50 x 11
   Country Prefix  Year   GTD   HDI `Population Siz~ `Military Spend~
   <chr>    <dbl> <dbl> <dbl> <dbl>            <dbl>            <dbl>
 1 Brunei       1  1996     0 0.807            0.305             6.25
 2 Brunei       1  1997     0 0.81             0.312             7.18
 3 Brunei       1  1998     0 0.812            0.319             7.53
 4 Brunei       1  1999     0 0.818            0.326             6.13
 5 Brunei       1  2000     0 0.819            0.333             4.07
 6 Brunei       1  2001     0 0.82             0.340             3.89
 7 Brunei       1  2002     0 0.823            0.347             3.87
 8 Brunei       1  2003     0 0.828            0.353             3.71
 9 Brunei       1  2004     0 0.834            0.359             2.53
10 Brunei       1  2005     0 0.838            0.365             2.35
# ... with 40 more rows, and 4 more variables: `Voice and Accountability
#   (-2.5 to 2.5)` <dbl>, `Education Index (0 to 1)` <dbl>, `Youth
#   Unemployment (%)` <dbl>, `GDP Per Capita (In US$)` <dbl>

该数据集中有更多国家，GTD 衡量每年的恐怖事件数量。

Code for Simple Linear Model
PovertyonTerrorism = (PI_Final_Dataset_PS3257_)
PovertyonTerrorism.lm = lm(GTD ~ HDI +
              `Population Size (In Millions)` +
              `Military Spending (% of GDP)` + 
              `Voice and Accountability (-2.5 to 2.5)` + 
              `Youth Unemployment (%)`, data = POT)
Summary(PovertyonTerrorism)
Call:
lm(formula = GTD ~ HDI + `Population Size (In Millions)` + `Military Spending (% of GDP)` + 
    `Voice and Accountability (-2.5 to 2.5)` + `Youth Unemployment (%)`, 
    data = POT)

Residuals:
    Min      1Q  Median      3Q     Max 
-120.61  -47.41  -28.11   22.41  608.11 

Coefficients:
                                         Estimate Std. Error t value
(Intercept)                              -55.2402    46.3994  -1.191
HDI                                      276.9661    75.1568   3.685
`Population Size (In Millions)`            0.4785     0.1164   4.113
`Military Spending (% of GDP)`           -13.8050     6.0859  -2.268
`Voice and Accountability (-2.5 to 2.5)`  29.5829    11.4481   2.584
`Youth Unemployment (%)`                  -7.1644     1.4098  -5.082
                                         Pr(>|t|)    
(Intercept)                              0.235032    
HDI                                      0.000284 ***
`Population Size (In Millions)`           5.4e-05 ***
`Military Spending (% of GDP)`           0.024212 *  
`Voice and Accountability (-2.5 to 2.5)` 0.010366 *  
`Youth Unemployment (%)`                  7.6e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 105.4 on 236 degrees of freedom
Multiple R-squared:  0.1818,    Adjusted R-squared:  0.1644 
F-statistic: 10.49 on 5 and 236 DF,  p-value: 4.202e-09

---

Code for PCSE Correction
PovertyonTerrorism.pcse = pcse(PovertyonTerrorism.lm, groupN = PovertyonTerrorism $Country, groupT = PovertyonTerrorism $Year)
summary(PovertyonTerrorism.pcse)
Results: 

                                            Estimate       PCSE   t value
(Intercept)                              -55.2401838 31.5740507 -1.749544
HDI                                      276.9661000 49.1693320  5.632903
`Population Size (In Millions)`            0.4785356  0.0459366 10.417305
`Military Spending (% of GDP)`           -13.8049962  2.6827129 -5.145909
`Voice and Accountability (-2.5 to 2.5)`  29.5828518 13.5261561  2.187085
`Youth Unemployment (%)`                  -7.1644384  1.1171568 -6.413100
                                             Pr(>|t|)
(Intercept)                              8.149689e-02
HDI                                      5.022592e-08
`Population Size (In Millions)`          3.752064e-21
`Military Spending (% of GDP)`           5.601758e-07
`Voice and Accountability (-2.5 to 2.5)` 2.971846e-02
`Youth Unemployment (%)`                 7.704480e-10

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

# Valid Obs = 242; # Missing Obs = 0; Degrees of Freedom = 236.

这是否意味着虽然线性模型中有统计上显着的变量，但当进行 pcse 校正时，不再有统计上显着的变量？对于澄清 pcse 结果的解释，我将不胜感激:)

标签： rpaneldata-analysispanel-data