R/data.R

In estimateW: Estimation of Spatial Weight Matrices

#' Covid incidences data
#'
#' COVID-19 data set provided by Johns Hopkins University (Dong et al., 2020). The
#' database contains information on (official) daily infections for a large panel of
#' countries around the globe in the very beginning of the outbreak
#' from 17 February to 20 April 2020.
#'
#' Data is provided for countries: Australia (AUS), Bahrain (BHR), Belgium (BEL),
#' Canada (CAN), China (CHN), Finland (FIN), France (FRA), Germany (DEU), Iran (IRN), Iraq (IRQ),
#' Israel (ISR), Italy (ITA), Japan (JPN), Kuwait (KWT), Lebanon (LBN), Malaysia (MYS), Oman (OMN),
#' Republic of Korea (KOR), Russian Federation (RUS), Singapore (SGP), Spain (ESP), Sweden (SWE),
#' Thailand (THA), United Arab Emirates (ARE), United Kingdom (GBR), United States of America (USA),
#' and Viet Nam (VNM).
#'
#' The dataset includes daily data on the country specific maximum measured temperature (Temperature) and
#' precipitation levels (Precipitation) as additional covariates (source: Dark Sky API).
#' The stringency index (Stringency) put forward by Hale et al. (2020), which summarizes country-specific
#' governmental policy measures to contain the spread of the virus. We use the biweekly average of the
#' reported stringency index.
#'
#' @name covid
#' @keywords covid infections stringency
#'
#' @docType data
#'
#' @format A \code{data.frame} object.
#'
#' @references
#'   Dong, E., Du, H., and Gardner, L. (2020). An interactive web-based dashboard to track
#'   COVID-19 in real time. \emph{The Lancet Infectious Diseases}, \bold{20(5)}, 533–534.
#'   \doi{10.1016/S1473-3099(20)30120-1}.
#'
#'   Hale, T., Petherick, A., Phillips, T., and Webster, S. (2020). Variation in government
#'   responses to COVID-19. Blavatnik School of Government Working Paper, 31, 2020–2011.
#'   \doi{10.1038/s41562-021-01079-8}.
#'
#'   Krisztin, T., and Piribauer, P. (2022). A Bayesian approach for the estimation
#'   of weight matrices in spatial autoregressive models, \emph{Spatial Economic Analysis},
#'   1-20. \doi{10.1080/17421772.2022.2095426}.
#'
#'   Krisztin, T., Piribauer, P., and Wögerer, M. (2020). The spatial econometrics of the
#'   coronavirus pandemic. \emph{Letters in Spatial and Resource Sciences}, \bold{13 (3)}, 209-218.
#'   \doi{10.1007/s12076-020-00254-1}.
#'
#'   Dong, E., Du, H., and Gardner, L. (2020). An interactive web-based dashboard to track
#'   COVID-19 in real time. \emph{The Lancet Infectious Diseases}, \bold{20(5)}, 533–534.
#'   \doi{10.1016/S1473-3099(20)30120-1}.
"covid"

#' Regional growth data
#'
#' Regional growth data set contains information on annual growth rates of GVA per worker
#' (labor productivity) for 90 European NUTS-1 regions for the period 1999-2019.
#’ The data set moreover contains initial log-levels of labor productivity as well as information
#’ on the share of low- and tertiary education attainment on working age population.
#’ Data comes from the Annual Regional Database of the European Commission's Directorate General for Regional and Urban Policy (ARDECO), and the Eurostat regional database.
#’ The data set can be viewed as a reduced form of the application in Piribauer et al. (2023).
#'
#' The dataset contains annual regional data for 26 European Union countries, disaggregated at the NUTS-1 level. The countries covered are: Austria, Belgium, Bulgaria, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Lithuania, Luxembourg, Latvia, Malta, Netherlands, Poland, Portugal, Romania, Sweden, Slovenia, and Slovakia. The NUTS-1 codes identify the first-level administrative regions within these countries.
#'
#' The dataset includes the following variables: annual output per worker growth (in percent), the natural logarithm of initial gross value added (GVA) per worker, and the share of the population with low and high levels of education based on the International Standard Classification of Education (ISCED). This structure allows for analyzing regional economic performance in relation to educational attainment across the European Union.
#'
#' @name nuts1growth
#' @keywords regional growth
#'
#' @docType data
#'
#' @format A \code{data.frame} object.
#'
#' @references
#'   Piribauer, P., Glocker, C., & Krisztin, T. (2023). Beyond distance: The spatial relationships of European regional economic growth. Journal of Economic Dynamics and Control, 155, 104735.
"nuts1growth"