MyNixOS website logo
Description

Estimate Entry Models.

Tools for measuring empirically the effects of entry in concentrated markets, based in Bresnahan and Reiss (1991) <https://www.jstor.org/stable/2937655>.

entrymodels: Estimate Entry Models

CRANversion CRANdownloads Travis buildstatus

Tools for measuring empirically the effects of entry in concentrated markets, based in Bresnahan and Reiss (1991).

Installation

You can install the released version of entrymodels from CRAN with:

install.packages("entrymodels")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("gnjardim/entrymodels")

Which should return something similar to:

* installing *source* package 'entrymodels' ...
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
  converting help for package 'entrymodels'
    finding HTML links ... done
    aux_matrix                              html  
    br1                                     html  
    br2                                     html  
    em_2var                                 html  
    em_basic                                html  
    load_example_data                       html  
** building package indices
** testing if installed package can be loaded from temporary location
*** arch - i386
*** arch - x64
** testing if installed package can be loaded from final location
*** arch - i386
*** arch - x64
** testing if installed package keeps a record of temporary installation path
* DONE (entrymodels)

Please note that you should have Rtools installed.

Examples

Basic Model

This is a basic example which shows you how to estimate a basic entry model with our sample data.

library(entrymodels)

tb <- load_example_data()
(em <- em_basic(tb, "Populacao", "n_agencias"))
#> # A tibble: 5 x 4
#>   n_competitors critical_values[,1] alpha[,1] gamma[,1]
#>           <int>               <dbl>     <dbl>     <dbl>
#> 1             1               5238.     0.832      7.12
#> 2             2              18961.     0.832      8.19
#> 3             3              42038.     0.825      8.79
#> 4             4              75638.     0.819      9.20
#> 5             5             162958.     0.809      9.71

Two-Variable Model

This is a basic example which shows you how to estimate a two-variable entry model with our sample data.

library(entrymodels)

tb <- load_example_data()
(em <- em_2var(tb, "Populacao", "RendaPerCapita", "n_agencias"))
#> # A tibble: 5 x 4
#>   n_competitors critical_values[,1] alpha[,1] gamma[,1]
#>           <int>               <dbl>     <dbl>     <dbl>
#> 1             1               5127.      1.06      17.8
#> 2             2              19265.      1.06      19.2
#> 3             3              44458.      1.05      20.0
#> 4             4              79959.      1.05      20.6
#> 5             5             169059.      1.03      20.9

Citation

To cite package entrymodels in publications use:

Guilherme N. Jardim (2020). entrymodels: Estimate Entry Models. R package version 0.2.1. https://CRAN.R-project.org/package=entrymodels

A BibTeX entry for LaTeX users is:

  @Manual{entrymodels,
    title = {entrymodels: Estimate Entry Models},
    author = {Guilherme {N. Jardim}},
    year = {2020},
    note = {R package version 0.2.1},
    url = {https://CRAN.R-project.org/package=entrymodels},
  }
Metadata

Version

0.2.1

License

Unknown

Platforms (75)

    Darwin
    FreeBSD
    Genode
    GHCJS
    Linux
    MMIXware
    NetBSD
    none
    OpenBSD
    Redox
    Solaris
    WASI
    Windows
Show all
  • aarch64-darwin
  • aarch64-genode
  • aarch64-linux
  • aarch64-netbsd
  • aarch64-none
  • aarch64_be-none
  • arm-none
  • armv5tel-linux
  • armv6l-linux
  • armv6l-netbsd
  • armv6l-none
  • armv7a-darwin
  • armv7a-linux
  • armv7a-netbsd
  • armv7l-linux
  • armv7l-netbsd
  • avr-none
  • i686-cygwin
  • i686-darwin
  • i686-freebsd
  • i686-genode
  • i686-linux
  • i686-netbsd
  • i686-none
  • i686-openbsd
  • i686-windows
  • javascript-ghcjs
  • loongarch64-linux
  • m68k-linux
  • m68k-netbsd
  • m68k-none
  • microblaze-linux
  • microblaze-none
  • microblazeel-linux
  • microblazeel-none
  • mips-linux
  • mips-none
  • mips64-linux
  • mips64-none
  • mips64el-linux
  • mipsel-linux
  • mipsel-netbsd
  • mmix-mmixware
  • msp430-none
  • or1k-none
  • powerpc-netbsd
  • powerpc-none
  • powerpc64-linux
  • powerpc64le-linux
  • powerpcle-none
  • riscv32-linux
  • riscv32-netbsd
  • riscv32-none
  • riscv64-linux
  • riscv64-netbsd
  • riscv64-none
  • rx-none
  • s390-linux
  • s390-none
  • s390x-linux
  • s390x-none
  • vc4-none
  • wasm32-wasi
  • wasm64-wasi
  • x86_64-cygwin
  • x86_64-darwin
  • x86_64-freebsd
  • x86_64-genode
  • x86_64-linux
  • x86_64-netbsd
  • x86_64-none
  • x86_64-openbsd
  • x86_64-redox
  • x86_64-solaris
  • x86_64-windows