| Type: | Package |
| Title: | A Graphical User Interface for Antitrust and Trade Practitioners |
| Version: | 0.7.1 |
| Depends: | R (≥ 2.10), antitrust (≥ 0.99.11), trade (≥ 0.5.4), shiny, rhandsontable |
| Imports: | ggplot2 |
| Description: | A graphical user interface for simulating the effects of mergers, tariffs, and quotas under an assortment of different economic models. The interface is powered by the 'Shiny' web application framework from 'RStudio'. |
| URL: | https://github.com/luciu5/competitiontoolbox |
| License: | CC0 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2022-08-24 20:51:45 UTC; ctara |
| Author: | Charles Taragin [aut, cre], Kenneth Rios [aut], Paulette Wolak [aut] |
| Maintainer: | Charles Taragin <ctaragin+competitiontoolbox@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-08-25 08:22:47 UTC |
A Link to the Shiny Interface to the trade and antitrust Packages
Description
Launch a shiny interface to simulate the effects of tariffs and mergers
Usage
antitrust_shiny()
Details
antitrust_shiny calls ct_shiny, which is a shiny interface for the antitrust
and trade package. See ct_shiny for further details.
A Shiny Interface to the trade and antitrust Packages
Description
Launch a shiny interface to simulate the effects of tariffs and mergers
Usage
ct_shiny()
Details
ct_shiny launches a shiny interface for the antitrust and trade packages.
The shiny interface provides users with the ability to calibrate model parameters and simulate
tariff effects using many of the supply and demand models included in the trade package. It
also provides users with the ability to calibrate different consumer demand systems and simulate the
effects of mergers under different competitive regimes included in the antitrust package.
Author(s)
Charles Taragin, Paulette Wolak
Examples
if(interactive()){ct_shiny()}
Box Plot Statistics for "Indices" Tab
Description
A dataset containing the summary statistics necessary to make boxplots according to supply, demand, and percent of outside share for horizontal mergers. This allows for examination of the relationship between industry price changes and commonly used merger indices.
Usage
indicboxdata
Format
A data frame with 2,303 rows and 10 variables
- Cut_type
Firm Count, HHI, Delta HHI, UPP, CMCR, Harm 2nd, Party Gap
- Cut_value
axis units depending on Cut_type
- shareOutThresh
outside share threshold in percent (20–70)
- Supply
pooled, bertrand, cournot, auction
- Demand
pooled, log, logit, aids, ces, linear
- high_wisk
maximum
- low_wisk
minimum
- pct25
25th percentile boxplot line
- pct50
50th percentile boxplot line
- pct75
75th percentile boxplot line
References
Taragin, C., & Loudermilk, M. (2019). Using measures of competitive harm for optimal screening of horizontal mergers. mimeo.doi:10.13140/RG.2.2.30872.85760/1.
Number of Monte Carlo Simulations Performed in "Indices" Tab
Description
A dataset containing the information necessary to calculate the number of merger simulations used to generate the plots for the "Indices" tab of "Numerical Simulations" for Horizontal Mergers based on the index of interest.
Usage
indicboxmktCnt
Format
A data frame with 35 rows and 3 variables
- Cut_type
Firm Count, HHI, Delta HHI, UPP, CMCR, Harm 2nd, Party Gap
- Cnt
number of horizontal merger simulations (25,890 – 184,254)
- shareOutThresh
outside share threshold in percent (20–70)
References
Taragin, C., & Loudermilk, M. (2019). Using measures of competitive harm for optimal screening of horizontal mergers. mimeo.doi:10.13140/RG.2.2.30872.85760/1.
Box Plot Statistics for "Summary" Tab for Horizontal Mergers
Description
A dataset containing the summary statistics necessary to make boxplots according to supply, demand, and percent of outside share for horizontal mergers so as to examine the distribution of outcomes.
Usage
sumboxdata
Format
A data frame with 210 rows and 10 variables
- Demand
log, logit, aids, ces, linear
- Model
cournot:log, cournot: linear, bertrand:aids, bertrand:logit, bertrand:ces, auction:logit
- Outcome
post-Merger index of interest (Industry Price Change (percent), Merging Party Price Change (percent), Consumer Harm (dollars), Producer Benefit (dollars), Net Harm (dollars)
- Supply
bertrand, cournot, auction
- high_wisk
maximum
- low_wisk
minimum
- pct25
25th percentile boxplot line
- pct50
50th percentile boxplot line
- pct75
75th percentile boxplot line
- shareOutThresh
outside share threshold in percent (20–70)
References
Taragin, C., & Loudermilk, M. (2019). Using measures of competitive harm for optimal screening of horizontal mergers. mimeo.doi:10.13140/RG.2.2.30872.85760/1.
Box Plot Statistics for "Summary" Tab for Tariffs
Description
A dataset containing the summary statistics necessary to make boxplots according to supply, demand, and tariff percentage for tariffs so as to examine the distribution of outcomes.
Usage
sumboxdata_trade
Format
A data frame with 162 rows and 10 variables
- Demand
Linear, CES, Logit
- Model
Cournot:Linear, Bertrand:CES, Bertrand:Logit, Auction2nd:Logit, Bargaining:Logit, Monopolistic Competition:CES, Monopolistic Competition:Logit
- Outcome
Consumer Harm, Domestic Firm Benefit, Foreign Firm Harm, Industry Price Change, Net Domestic Harm, Net Total Harm, Domestic Firm Price Change, Foreign Firm Price Change
- Supply
Cournot, Bertrand, Auction2nd, Bargaining, Monopolistic Competition
- high_wisk
maximum
- low_wisk
minimum
- pct25
25th percentile boxplot line
- pct50
50th percentile boxplot line
- pct75
75th percentile boxplot line
- tariffThresh
tariff threshold in percent (10–30)
References
Taragin, C., & Loudermilk, M. (2019). Using measures of competitive harm for optimal screening of horizontal mergers. mimeo.doi:10.13140/RG.2.2.30872.85760/1.
Number of Monte Carlo Simulations Performed in "Summary" Tab for Horizontal Mergers
Description
A dataset containing the information necessary to calculate the number of merger simulations used to generate the plots for the Summary tab of Numerical Simulations for Horizontal Mergers.
Usage
sumboxmktCnt
Format
A data frame with 30 rows and 3 variables
- Outcome
post-Merger indice of interest (Industry Price Change (percent), Merging Party Price Change (percent), Consumer Harm (dollars), Producer Benefit (dollars), Net Harm (dollars)
- Cnt
number of horizontal merger simulations
- shareOutThresh
outside share threshold in percent (20–70)
References
Taragin, C., & Loudermilk, M. (2019). Using measures of competitive harm for optimal screening of horizontal mergers. mimeo.doi:10.13140/RG.2.2.30872.85760/1.
Number of Monte Carlo Simulations Performed in "Summary" Tab for Tariffs
Description
A dataset containing the information necessary to calculate the number of tariffs used to generate the plots for the Summary tab of Numerical Simulations for Tariffs.
Usage
sumboxmktCnt_trade
Format
A data frame with 24 rows and 3 variables
- Outcome
Consumer Harm, Domestic Firm Benefit, Foreign Firm Harm, Industry Price Change, Net Domestic Harm, Net Total Harm, Domestic Firm Price Change, Foreign Firm Price Change
- Cnt
number of tariffs simulated
- tariffThresh
tariff threshold in percent (10–30)
References
Taragin, C., & Loudermilk, M. (2019). Using measures of competitive harm for optimal screening of horizontal mergers. mimeo.doi:10.13140/RG.2.2.30872.85760/1.
A Link to the Shiny Interface to the trade and antitrust Packages
Description
Launch a shiny interface to simulate the effects of tariffs and mergers
Usage
trade_shiny()
Details
trade_shiny calls ct_shiny, which is a shiny interface for the antitrust
and trade package. See ct_shiny for further details.