plot_differences: plot_difference

In tidysynth: A Tidy Implementation of the Synthetic Control Method

plot_difference

Description

Plot the difference between the observed and synthetic control unit. The difference captures the causal quantity (i.e. the magnitude of the difference between the observed and counter-factual case).

Usage

plot_differences(data, time_window = NULL)

Arguments

data	nested data of type tbl_df.
time_window	time window of the trend plot.

Value

ggplot object of the difference between the observed and synthetic trends.

ggplot object of difference between the observed and synthetic control unit.

Examples

# Smoking example data
data(smoking)

smoking_out <-
smoking %>%

# initial the synthetic control object
synthetic_control(outcome = cigsale,
                  unit = state,
                  time = year,
                  i_unit = "California",
                  i_time = 1988,
                  generate_placebos=TRUE) %>%

# Generate the aggregate predictors used to generate the weights
  generate_predictor(time_window=1980:1988,
                     lnincome = mean(lnincome, na.rm = TRUE),
                     retprice = mean(retprice, na.rm = TRUE),
                     age15to24 = mean(age15to24, na.rm = TRUE)) %>%

  generate_predictor(time_window=1984:1988,
                     beer = mean(beer, na.rm = TRUE)) %>%

  generate_predictor(time_window=1975,
                     cigsale_1975 = cigsale) %>%

  generate_predictor(time_window=1980,
                     cigsale_1980 = cigsale) %>%

  generate_predictor(time_window=1988,
                     cigsale_1988 = cigsale) %>%


  # Generate the fitted weights for the synthetic control
  generate_weights(optimization_window =1970:1988,
                   Margin.ipop=.02,Sigf.ipop=7,Bound.ipop=6) %>%

  # Generate the synthetic control
  generate_control()

# Plot the observed and synthetic trend
smoking_out %>% plot_differences(time_window = 1970:2000)

tidysynth documentation built on May 31, 2023, 6:13 p.m.