RStudio: DOI: https://doi.org/10.1002/for.2816 DOI: 10.1002/for.2816

Suggested citation: Santos-Marquez, F. (2021). Spatial Beta-Convergence Forecasting Models: Evidence from Municipal Homicide Rates in Colombia. Journal of Forecasting. https://doi.org/10.1002/for.2816.

This work is licensed under the Creative Commons Attribution-Share Alike 4.0 International License.

This repository contains all the code and data to replicate the results of the published paper

Spatial Beta-Convergence Forecasting Models: Evidence from Municipal Homicide Rates in Colombia

The forecasting power of different methods is tested utilizing crime data for 1120 inland municipalities in Colombia. Using data from 2003 to 2018, five different forecasting methods are used: ETS, ARIMA, STAR, a classical beta convergence based model and a spatial beta convergence model. First, it is shown that overall municipal crime disparities are steadily decreasing over time. This indicates that convergence and spatial effects are pivotal for the study of the dynamics of crime in Colombian municipalities. Time series cross validation for 4-year ahead forecasts is implemented to assess the accuracy of all models. It is found that the STAR and the beta models have the lowest root mean squared errors. Therefore, as time goes by, space appears to play a more important role in the evolution of homicide rates. The paper concludes with some policy implications in terms of spatial effects and the mitigation of crime.

How to replicate the results of the paper

There are three (or more) options, they are presented in the order of how simple it is to implement the replication. the last option (binder button or online environment) sometimes takes a few minutes (even an hour) to load, depending if the virtual computer has to be created from scratch.

• Go directly to Rsudiocloud and run the project on the cloud https://rstudio.cloud/project/2773965
• Go to Rstudiocloud and use the GitHub option when creating the project and the link of this repository which is public
• Download all this code as a zip file and run it locally on Rstudio (take a look at the essionInfo() section below)
• Click on the binder button on the top left of this Readme file or follow the simple instruction in the "Online Environment" section of this readme file (just below)

Online Environments

• This notebook can also be executed online at GESIS Notebooks. Just copy the URL of this repository and paste it on the BINDER form To open a virtual R Studio session, make sure you change you click on File and change it to URL. Then, write rstudio in the field URL to open (optional). Finally, click on launch.

Replication

In order to replicate the main tables of the article tables 1 and 2, run the Rmd files in the order from a to e.

sessionInfo()

You may opt to run all the code in your PC, here it is the session info of mine

R version 4.0.4 (2021-02-15) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale: [1] LC_COLLATE=English_United States.1252 [2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

attached base packages: [1] stats graphics grDevices utils datasets [6] methods base

other attached packages: [1] forecast_8.12 REAT_2.1.1
[3] readxl_1.3.1 data.table_1.12.8 [5] broom_0.5.6 modelr_0.1.7
[7] forcats_0.5.0 stringr_1.4.0
[9] dplyr_1.0.2 purrr_0.3.4
[11] readr_1.3.1 tidyr_1.0.3
[13] tibble_3.0.1 ggplot2_3.3.0
[15] tidyverse_1.3.0 knitr_1.28
[17] ExPanDaR_0.5.3 devtools_2.3.0
[19] usethis_1.6.1

loaded via a namespace (and not attached): [1] nlme_3.1-152 fs_1.4.1
[3] xts_0.12-0 lubridate_1.7.8
[5] httr_1.4.1 rprojroot_1.3-2
[7] tools_4.0.4 backports_1.1.6
[9] R6_2.4.1 DT_0.13
[11] mgcv_1.8-33 DBI_1.1.0
[13] colorspace_1.4-1 nnet_7.3-15
[15] withr_2.2.0 tidyselect_1.1.0
[17] prettyunits_1.1.1 tictoc_1.0
[19] processx_3.4.2 curl_4.3
[21] compiler_4.0.4 cli_2.0.2
[23] rvest_0.3.5 xml2_1.3.2
[25] desc_1.2.0 labeling_0.3
[27] tseries_0.10-47 scales_1.1.1
[29] lmtest_0.9-37 fracdiff_1.5-1
[31] quadprog_1.5-8 callr_3.4.3
[33] askpass_1.1 digest_0.6.25
[35] foreign_0.8-81 rio_0.5.16
[37] pkgconfig_2.0.3 htmltools_0.4.0
[39] sessioninfo_1.1.1 dbplyr_1.4.3
[41] fastmap_1.0.1 TTR_0.23-6
[43] htmlwidgets_1.5.1 rlang_0.4.7
[45] quantmod_0.4.17 rstudioapi_0.11
[47] shiny_1.4.0.2 farver_2.0.3
[49] generics_0.0.2 zoo_1.8-8
[51] jsonlite_1.7.1 zip_2.0.4
[53] magrittr_1.5 Matrix_1.3-2
[55] Rcpp_1.0.4.6 munsell_0.5.0
[57] fansi_0.4.1 shinycssloaders_0.3 [59] lifecycle_0.2.0 stringi_1.4.6
[61] pkgbuild_1.0.7 grid_4.0.4
[63] parallel_4.0.4 promises_1.1.0
[65] crayon_1.3.4 lattice_0.20-41
[67] splines_4.0.4 haven_2.2.0
[69] hms_0.5.3 ps_1.3.2
[71] pillar_1.4.4 pkgload_1.0.2
[73] urca_1.3-0 reprex_0.3.0
[75] glue_1.4.0 remotes_2.1.1
[77] vctrs_0.3.2 httpuv_1.5.2
[79] testthat_2.3.2 cellranger_1.1.0
[81] gtable_0.3.0 openssl_1.4.1
[83] assertthat_0.2.1 xfun_0.22
[85] openxlsx_4.1.5 mime_0.9
[87] xtable_1.8-4 later_1.0.0
[89] timeDate_3043.102 memoise_1.1.0
[91] ellipsis_0.3.0