13 Exercises IIIb
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Required packages
Session info
R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=English_United Kingdom.utf8
[2] LC_CTYPE=English_United Kingdom.utf8
[3] LC_MONETARY=English_United Kingdom.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.utf8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] SDPDmod_0.0.6 splm_1.6-5 lfe_3.1.1
[4] plm_2.6-6 viridis_0.6.5 viridisLite_0.4.2
[7] tmap_4.1 ggplot2_3.5.2 spatialreg_1.3-6
[10] Matrix_1.7-3 spdep_1.3-13 spData_2.3.4
[13] mapview_2.11.2 sf_1.0-21
loaded via a namespace (and not attached):
[1] Rdpack_2.6.4 DBI_1.2.3
[3] deldir_2.0-4 gridExtra_2.3
[5] tmaptools_3.2 s2_1.1.9
[7] logger_0.4.0 sandwich_3.1-1
[9] rlang_1.1.6 magrittr_2.0.3
[11] multcomp_1.4-28 e1071_1.7-16
[13] compiler_4.5.1 png_0.1-8
[15] vctrs_0.6.5 stringr_1.5.1
[17] pkgconfig_2.0.3 wk_0.9.4
[19] fastmap_1.2.0 lwgeom_0.2-14
[21] leafem_0.2.4 rmarkdown_2.29
[23] spacesXYZ_1.6-0 miscTools_0.6-28
[25] xfun_0.52 satellite_1.0.5
[27] jsonlite_2.0.0 collapse_2.1.2
[29] terra_1.8-54 parallel_4.5.1
[31] LearnBayes_2.15.1 R6_2.6.1
[33] stringi_1.8.7 RColorBrewer_1.1-3
[35] boot_1.3-31 lmtest_0.9-40
[37] stars_0.6-8 Rcpp_1.0.14
[39] knitr_1.50 zoo_1.8-14
[41] base64enc_0.1-3 leaflet.providers_2.0.0
[43] splines_4.5.1 tidyselect_1.2.1
[45] rstudioapi_0.17.1 dichromat_2.0-0.1
[47] abind_1.4-8 maptiles_0.10.0
[49] maxLik_1.5-2.1 codetools_0.2-20
[51] lattice_0.22-7 tibble_3.3.0
[53] leafsync_0.1.0 withr_3.0.2
[55] coda_0.19-4.1 evaluate_1.0.4
[57] survival_3.8-3 units_0.8-7
[59] proxy_0.4-27 pillar_1.10.2
[61] KernSmooth_2.23-26 stats4_4.5.1
[63] generics_0.1.4 sp_2.2-0
[65] scales_1.4.0 xtable_1.8-4
[67] class_7.3-23 glue_1.8.0
[69] tools_4.5.1 leaflegend_1.2.1
[71] data.table_1.17.6 RSpectra_0.16-2
[73] dotCall64_1.2 mvtnorm_1.3-3
[75] XML_3.99-0.18 grid_4.5.1
[77] rbibutils_2.3 crosstalk_1.2.1
[79] bdsmatrix_1.3-7 colorspace_2.1-1
[81] nlme_3.1-168 cols4all_0.8
[83] raster_3.6-32 Formula_1.2-5
[85] cli_3.6.5 spam_2.11-1
[87] dplyr_1.1.4 gtable_0.3.6
[89] digest_0.6.37 classInt_0.4-11
[91] TH.data_1.1-3 htmlwidgets_1.6.4
[93] farver_2.1.2 htmltools_0.5.8.1
[95] lifecycle_1.0.4 leaflet_2.2.2
[97] microbenchmark_1.5.0 MASS_7.3-65
13.1 Inkar data: the effect of regional characteristics on life expectancy
Below, we read and transform some characteristics of the INKAR data on the level of German counties.
load("_data/inkar2.Rdata")
Variables are
Variable | Description |
---|---|
“Kennziffer” | ID |
“Raumeinheit” | Name |
“Aggregat” | Level |
“year” | Year |
“poluation_density” | Population Density |
“median_income” | Median Household income (only for 2020) |
“gdp_in1000EUR” | Gross Domestic Product in 1000 euros |
“unemployment_rate” | Unemployment rate |
“share_longterm_unemployed” | Share of longterm unemployed (among unemployed) |
“share_working_indutry” | Share of employees in undistrial sector |
“share_foreigners” | Share of foreign nationals |
“share_college” | Share of school-finishers with college degree |
“recreational_space” | Recreational space per inhabitant |
“car_density” | Density of cars |
“life_expectancy” | Life expectancy |
13.2 County shapes
1) Please map the life expectancy across Germany
Merge data with the shape file (as with conventional data)
Create a map of life-expectancy
2) Chose some variables that could predict life expectancy. See for instance the following paper.
3) Generate a neighbours object (e.g. the 10 nearest neighbours).
4) Estimate a cross-sectional spatial model for the year 2020 and calculate the impacts.
5) Calculate the spatial lagged variables for your covariates (e.g. use create_WX(), which needs a non-spatial df as input) .
6) Can you run a spatial machine learning model? (for instance, using randomForest
)?
Esimate an FE model with SLX specification
Loop over years to generate WX
Estimate a twoways FE SLX panel model
Estimate a twoways FE SAR panel model (use
spml()
)Estimate the summary impacts.