##### Example 1: Basic Example with First Differences #####
# Attach sample data and variable names:
data(macro)
# Estimate model and present a summary:
z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)
# Set explanatory variables to their default (mean/mode) values, with
# high (80th percentile) and low (20th percentile) values:
x.high<- setx(z.out1, trade = quantile(macro$trade, 0.8))
x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2))
x.high
x.low
# Generate first differences for the effect of high versus low trade on
# GDP:
s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
# Summary of fitted statistical model
summary(z.out1)
# Summary of simualted quantities of interest
summary(s.out1)
# Plot of simulated quantities of interest
plot(s.out1)
##### Example 2: Using Dummy Variables #####
# Estimate a model with a dummy variable for each year and country.
# Note that you do not need to create dummy variables, as the program
# will automatically parse the unique values in the selected variables
# into dummy variables.
z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country),
model = "ls", data = macro)
# Set values for the explanatory variables, using the default mean/mode
# values, with country set to the United States and Japan, respectively:
x.US <- setx(z.out2, country = "United States")
x.Japan <- setx(z.out2, country = "Japan")
# Simulate quantities of interest:
s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
# Summary of fitted statistical model
summary(z.out2)
# Summary of simulated quantities of interest
summary(s.out2)
# Plot differences:
plot(s.out2)
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