使用FIXEST的FEOL进行固定效果的固定效果

Question

我想采用一个固定效果模型，其中包括国家和年份固定效果以及国家与年份之间的互动效果。我尝试了以下方法：

library(fixest)
feols(y ~ x | country*year, data)
feols(y ~ x | country + year + country:year, data)
feols(y ~ x | country + year + I(country*year), data)

但是全部产生了错误：

Error in feols(y ~ x | country*: Error in res[[2]] : subscript out of bounds This error was unforeseen by the author of the function feols. If you think your call to the function is legitimate, could you report?

我该如何正确处理？

以下是我实际数据的前60行：

structure(list(NAME_1.y = c("Alibori", "Atakora", "Atlantique", 
"Borgou", "Collines", "Donga", "Kouffo", "Littoral", "Mono", 
"Oueme", "Plateau", "Zou", "Central", "Chobe", "Francistown", 
"Gaborone", "Ghanzi", "Jwaneng", "Kgalagadi", "Kgatleng", "Kweneng", 
"Lobatse", "North-East", "Selibe Phikwe", "South-East", "Southern", 
"Mosteiros", "Paúl", "Porto Novo", "Praia", "Ribeira Grande", 
"Santa Catarina", "Santa Cruz", "Sao Domingos", "Sao Filipe", 
"Sao Miguel", "Sao Vicente", "Tarrafal", "Ashanti", "Brong Ahafo", 
"Central", "Eastern", "Greater Accra", "Northern", "Upper East", 
"Upper West", "Volta", "Western", "Bomet", "Bungoma", "Garissa", 
"Isiolo", "Kajiado", "Kakamega", "Kericho", "Kiambu", "Kilifi", 
"Kirinyaga", "Kisumu", "Kitui"), country = c("Benin", "Benin", 
"Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", 
"Benin", "Benin", "Benin", "Botswana", "Botswana", "Botswana", 
"Botswana", "Botswana", "Botswana", "Botswana", "Botswana", "Botswana", 
"Botswana", "Botswana", "Botswana", "Botswana", "Botswana", "Cape Verde", 
"Cape Verde", "Cape Verde", "Cape Verde", "Cape Verde", "Cape Verde", 
"Cape Verde", "Cape Verde", "Cape Verde", "Cape Verde", "Cape Verde", 
"Cape Verde", "Ghana", "Ghana", "Ghana", "Ghana", "Ghana", "Ghana", 
"Ghana", "Ghana", "Ghana", "Ghana", "Kenya", "Kenya", "Kenya", 
"Kenya", "Kenya", "Kenya", "Kenya", "Kenya", "Kenya", "Kenya", 
"Kenya", "Kenya"), year = c(2005, 2005, 2005, 2005, 2005, 2005, 
2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005), 
    yearvalue = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 29874625.2288818, 
    0, 0, 0, 0, 0, 3119730.26764258, 0), sngq = c(40.3350714285714, 
    41.8501465773809, 32.1462559523809, 53.3881378348214, 51.3143125, 
    45.3290479910714, 30.9222321428571, 31.8178055555556, 29.6696517857143, 
    32.6099506302521, 30.4012254464286, 31.2331401098901, 48.3522208850932, 
    46.2751339285714, 51.8882346938775, 49.0520504201681, 49.9850803571429, 
    49.7569285714286, 48.6707276785714, 55.5698392857143, 49.1367147108843, 
    49.6709583333333, 47.7482008928571, 47.4158928571429, 47.1282232142857, 
    47.5937851190476, 45.5919585253456, 39.4586860119048, 34.0096227106227, 
    37.4234026227679, 35.2635037202381, 36.663, 39.3566339285714, 
    39.9424339285714, 38.4452915543576, 41.0157392857143, 46.5320122818358, 
    45.8783482142857, 45.1649102484472, 55.3813660714286, 40.4874038461538, 
    43.992073015873, 39.0866451990632, 54.0252091836735, 48.2392232142857, 
    45.3506232142857, 43.0489608516484, 36.9757994047619, 38.9841964285714, 
    36.4050892857143, 38.1211875, 46.1263392857143, 38.1707857142857, 
    30.9757380952381, 34.6969267857143, 46.7413571428571, 31.9214107142857, 
    41.9071845238095, 29.2133482142857, 41.2855535714286)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -60L), groups = structure(list(
    year = c(2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
    2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
    2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
    2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
    2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
    2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 
    2005, 2005), country = c("Benin", "Benin", "Benin", "Benin", 
    "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", "Benin", 
    "Benin", "Botswana", "Botswana", "Botswana", "Botswana", 
    "Botswana", "Botswana", "Botswana", "Botswana", "Botswana", 
    "Botswana", "Botswana", "Botswana", "Botswana", "Botswana", 
    "Cape Verde", "Cape Verde", "Cape Verde", "Cape Verde", "Cape Verde", 
    "Cape Verde", "Cape Verde", "Cape Verde", "Cape Verde", "Cape Verde", 
    "Cape Verde", "Cape Verde", "Ghana", "Ghana", "Ghana", "Ghana", 
    "Ghana", "Ghana", "Ghana", "Ghana", "Ghana", "Ghana", "Kenya", 
    "Kenya", "Kenya", "Kenya", "Kenya", "Kenya", "Kenya", "Kenya", 
    "Kenya", "Kenya", "Kenya", "Kenya"), NAME_1.y = c("Alibori", 
    "Atakora", "Atlantique", "Borgou", "Collines", "Donga", "Kouffo", 
    "Littoral", "Mono", "Oueme", "Plateau", "Zou", "Central", 
    "Chobe", "Francistown", "Gaborone", "Ghanzi", "Jwaneng", 
    "Kgalagadi", "Kgatleng", "Kweneng", "Lobatse", "North-East", 
    "Selibe Phikwe", "South-East", "Southern", "Mosteiros", "Paúl", 
    "Porto Novo", "Praia", "Ribeira Grande", "Santa Catarina", 
    "Santa Cruz", "Sao Domingos", "Sao Filipe", "Sao Miguel", 
    "Sao Vicente", "Tarrafal", "Ashanti", "Brong Ahafo", "Central", 
    "Eastern", "Greater Accra", "Northern", "Upper East", "Upper West", 
    "Volta", "Western", "Bomet", "Bungoma", "Garissa", "Isiolo", 
    "Kajiado", "Kakamega", "Kericho", "Kiambu", "Kilifi", "Kirinyaga", 
    "Kisumu", "Kitui"), .rows = structure(list(1L, 2L, 3L, 4L, 
        5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
        17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 
        28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 
        39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 
        50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -60L), .drop = TRUE))

我的回归没有国家和年份固定效果之间的相互作用

feols(sngq ~ yearvalue | country + year, df)

Answer 1

您应该查看所需的特定语法，如文档在这里 。也许您希望这样：

library(fixest)
#> Warning: package 'fixest' was built under R version 4.1.2
feols(sngq ~ yearvalue | country^year, data)
#> OLS estimation, Dep. Var.: sngq
#> Observations: 60 
#> Fixed-effects: country^year: 5
#> Standard-errors: Clustered (country^year) 
#>            Estimate Std. Error       t value  Pr(>|t|)    
#> yearvalue -2.26e-08   1.55e-23 -1.453754e+15 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 5.38506     Adj. R2: 0.375872
#>                 Within R2: 2.377e-4
feols(sngq ~ yearvalue | country + year + c(country,year), data)
#> OLS estimation, Dep. Var.: sngq
#> Observations: 60 
#> Fixed-effects: country: 5,  year: 1,  c(country, year): 6
#> Standard-errors: Clustered (country) 
#>            Estimate Std. Error      t value  Pr(>|t|)    
#> yearvalue -2.26e-08   2.17e-23 -1.04226e+15 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 5.38506     Adj. R2: 0.312185
#>                 Within R2: 2.377e-4
feols(sngq ~ yearvalue | country + year + I(c(country,year)), data)
#> OLS estimation, Dep. Var.: sngq
#> Observations: 60 
#> Fixed-effects: country: 5,  year: 1,  I(c(country, year)): 6
#> Standard-errors: Clustered (country) 
#>            Estimate Std. Error      t value  Pr(>|t|)    
#> yearvalue -2.26e-08   2.17e-23 -1.04226e+15 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 5.38506     Adj. R2: 0.312185
#>                 Within R2: 2.377e-4

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