Update README

kylebutts · Jun 21, 2024 · 9f1099c · 9f1099c
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diff --git a/README.Rmd b/README.Rmd
@@ -130,15 +130,15 @@ Then, let's estimate an event study did. Note that relative year has a value of
 # Event Study
 es <- did2s(df_het,
   yname = "dep_var", first_stage = ~ 0 | state + year,
-  second_stage = ~ i(rel_year, ref = c(-1, Inf)), treatment = "treat",
+  second_stage = ~ i(rel_year, ref = Inf), treatment = "treat",
   cluster_var = "state"
 )
 ```
 
 And plot the results:
 
 ```{r plot-es, fig.cap="Event-study plot with example data", fig.width=8, fig.height=5, dpi=300}
-fixest::iplot(es, main = "Event study: Staggered treatment", xlab = "Relative time to treatment", col = "steelblue", ref.line = -0.5)
+fixest::iplot(es, main = "Event study: Staggered treatment", xlab = "Relative time to treatment", col = "steelblue", ref.line = -0.5, drop = "Inf")
 
 # Add the (mean) true effects
 true_effects <- head(tapply((df_het$te + df_het$te_dynamic), df_het$rel_year, mean), -1)
@@ -155,16 +155,17 @@ legend(
 ### Comparison to TWFE
 
 ```{r plot-compare, fig.cap="TWFE and Two-Stage estimates of Event-Study", fig.width=8, fig.height=5, dpi=300}
-twfe <- feols(dep_var ~ i(rel_year, ref = c(-1, Inf)) | unit + year, data = df_het)
+twfe <- feols(dep_var ~ i(rel_year, ref = c(Inf)) | unit + year, data = df_het)
 
-fixest::iplot(list(es, twfe),
+fixest::iplot(
+  list(es, twfe),
   sep = 0.2, ref.line = -0.5,
   col = c("steelblue", "#82b446"), pt.pch = c(20, 18),
   xlab = "Relative time to treatment",
-  main = "Event study: Staggered treatment (comparison)"
+  main = "Event study: Staggered treatment (comparison)",
+  drop = "Inf"
 )
 
-
 # Legend
 legend(
   x = -20, y = 3, col = c("steelblue", "#82b446"), pch = c(20, 18),
@@ -198,7 +199,7 @@ es_did2s <- did2s(
   cluster_var = "stfips"
 )
 
-coefplot(es_did2s)
+iplot(es_did2s, drop = "-100")
 ```
 
 ```{r sensitivity, fig.cap="Sensitivity analysis for the example data", fig.width=8, fig.height=5, dpi=300}

diff --git a/README.md b/README.md
@@ -83,6 +83,9 @@ library(did2s)
 #>     year = {2021},
 #>     url = {https://journal.r-project.org/articles/RJ-2022-048/},
 #>   }
+```
+
+``` r
 
 # Load Data from R package
 data("df_het", package = "did2s")
@@ -149,6 +152,9 @@ static <- did2s(
 #>  - second stage formula `~ i(treat, ref = FALSE)`
 #>  - The indicator variable that denotes when treatment is on is `treat`
 #>  - Standard errors will be clustered by `state`
+```
+
+``` r
 
 fixest::etable(static)
 #>                            static
@@ -174,20 +180,20 @@ second stage formula.
 # Event Study
 es <- did2s(df_het,
   yname = "dep_var", first_stage = ~ 0 | state + year,
-  second_stage = ~ i(rel_year, ref = c(-1, Inf)), treatment = "treat",
+  second_stage = ~ i(rel_year, ref = Inf), treatment = "treat",
   cluster_var = "state"
 )
 #> Running Two-stage Difference-in-Differences
 #>  - first stage formula `~ 0 | state + year`
-#>  - second stage formula `~ i(rel_year, ref = c(-1, Inf))`
+#>  - second stage formula `~ i(rel_year, ref = Inf)`
 #>  - The indicator variable that denotes when treatment is on is `treat`
 #>  - Standard errors will be clustered by `state`
 ```
 
 And plot the results:
 
 ``` r
-fixest::iplot(es, main = "Event study: Staggered treatment", xlab = "Relative time to treatment", col = "steelblue", ref.line = -0.5)
+fixest::iplot(es, main = "Event study: Staggered treatment", xlab = "Relative time to treatment", col = "steelblue", ref.line = -0.5, drop = "Inf")
 
 # Add the (mean) true effects
 true_effects <- head(tapply((df_het$te + df_het$te_dynamic), df_het$rel_year, mean), -1)
@@ -212,16 +218,21 @@ Event-study plot with example data
 ### Comparison to TWFE
 
 ``` r
-twfe <- feols(dep_var ~ i(rel_year, ref = c(-1, Inf)) | unit + year, data = df_het)
+twfe <- feols(dep_var ~ i(rel_year, ref = c(Inf)) | unit + year, data = df_het)
+#> The variable 'rel_year::20' has been removed because of collinearity (see $collin.var).
+```
+
+``` r
 
-fixest::iplot(list(es, twfe),
+fixest::iplot(
+  list(es, twfe),
   sep = 0.2, ref.line = -0.5,
   col = c("steelblue", "#82b446"), pt.pch = c(20, 18),
   xlab = "Relative time to treatment",
-  main = "Event study: Staggered treatment (comparison)"
+  main = "Event study: Staggered treatment (comparison)",
+  drop = "Inf"
 )
 
-
 # Legend
 legend(
   x = -20, y = 3, col = c("steelblue", "#82b446"), pch = c(20, 18),
@@ -275,8 +286,11 @@ es_did2s <- did2s(
 #>  - second stage formula `~ 0 + i(rel_year, ref = -100)`
 #>  - The indicator variable that denotes when treatment is on is `treated`
 #>  - Standard errors will be clustered by `stfips`
+```
+
+``` r
 
-coefplot(es_did2s)
+iplot(es_did2s, drop = "-100")
 ```
 
 <div class="figure">
@@ -303,14 +317,15 @@ sensitivity_results <- es_did2s |>
 #> Warning in .ARP_computeCI(betahat = betahat, sigma = sigma, numPrePeriods =
 #> numPrePeriods, : CI is open at one of the endpoints; CI length may not be
 #> accurate
-
 #> Warning in .ARP_computeCI(betahat = betahat, sigma = sigma, numPrePeriods =
 #> numPrePeriods, : CI is open at one of the endpoints; CI length may not be
 #> accurate
-
 #> Warning in .ARP_computeCI(betahat = betahat, sigma = sigma, numPrePeriods =
 #> numPrePeriods, : CI is open at one of the endpoints; CI length may not be
 #> accurate
+```
+
+``` r
 
 # Create plot
 HonestDiD::createSensitivityPlot_relativeMagnitudes(
@@ -341,6 +356,9 @@ library(tidyverse)
 #> ✖ dplyr::filter() masks stats::filter()
 #> ✖ dplyr::lag()    masks stats::lag()
 #> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
+```
+
+``` r
 data(df_het)
 df = df_het
 multiple_ests = did2s::event_study(
@@ -358,6 +376,9 @@ multiple_ests = did2s::event_study(
 #> Estimating using Sun and Abraham (2020)
 #> Estimating using Borusyak, Jaravel, Spiess (2021)
 #> Estimating using Roth and Sant'Anna (2021)
+```
+
+``` r
 did2s::plot_event_study(multiple_ests)
 ```
 

diff --git a/man/figures/README-ehec-data-est-1.png b/man/figures/README-ehec-data-est-1.png
diff --git a/man/figures/README-plot-compare-1.png b/man/figures/README-plot-compare-1.png
diff --git a/man/figures/README-plot-es-1.png b/man/figures/README-plot-es-1.png
diff --git a/man/figures/README-unnamed-chunk-3-1.png b/man/figures/README-unnamed-chunk-3-1.png
diff --git a/vignettes/Two-Stage-Difference-in-Differences.Rmd b/vignettes/Two-Stage-Difference-in-Differences.Rmd
@@ -90,7 +90,7 @@ The main function is **`did2s()`**, which estimates the two-stage DiD procedure.
 
 - `yname`: The outcome variable. For example, `"y"`.
 - `first_stage`: Formula indicating the first stage. This can include fixed effects and covariates, but do not include treatment variable(s)! For efficiency, it is recommended to use the **fixest** convention of specifying fixed effects after a vertical bar. For example, `~ x1 + x2 | fe1 + fe2`.
-- `second_stage`: Formula indicating the treatment variable or, in the case of event studies, treatment variables. Again, following **fixest** conventions, it is recommended to use the [`i()`](https://lrberge.github.io/fixest/reference/i.html) syntax. For example, `~ i(time_to_treatment, ref = 0)`.
+- `second_stage`: Formula indicating the treatment variable or, in the case of event studies, treatment variables. Again, following **fixest** conventions, it is recommended to use the [`i()`](https://lrberge.github.io/fixest/reference/i.html) syntax. For example, `~ i(time_to_treatment)`.
 - `treatment`: A binary (1/0) or logical (TRUE/FALSE) variable demarcating when treatment turns on for a unit. For example, `"treated"`.
 
 Optional arguments include the ability to implement weighted regressions and whether to cluster or bootstrap standard errors.
@@ -101,7 +101,7 @@ Let's walk through an example dataset from the package.
 
 ```{r load-data, code_folding=TRUE,}
 library(did2s) ## The main package. Will automatically load fixest as well.
-library(ggplot2) 
+library(ggplot2)
 
 ## Load heterogeneous treatment dataset from the package
 data("df_het")
@@ -111,9 +111,8 @@ head(df_het)
 Here is a plot of the average outcome variable for each of the groups:
 
 ```{r plot-df-het, fig.width=8, fig.height=4, fig.cap="Example data with heterogeneous treatment effects"}
-
 # Mean for treatment group-year
-agg <- aggregate(df_het$dep_var, by=list(g = df_het$g, year = df_het$year), FUN = mean)
+agg <- aggregate(df_het$dep_var, by = list(g = df_het$g, year = df_het$year), FUN = mean)
 
 agg$g <- as.character(agg$g)
 agg$g <- ifelse(agg$g == "0", "Never Treated", agg$g)
@@ -122,83 +121,85 @@ never <- agg[agg$g == "Never Treated", ]
 g1 <- agg[agg$g == "2000", ]
 g2 <- agg[agg$g == "2010", ]
 
-
-plot(0, 0, xlim = c(1990,2020), ylim = c(4,7.2), type = "n",
-     main = "Data-generating Process", ylab = "Outcome", xlab = "Year")
+plot(0, 0,
+  xlim = c(1990, 2020), ylim = c(4, 7.2), type = "n",
+  main = "Data-generating Process", ylab = "Outcome", xlab = "Year"
+)
 abline(v = c(1999.5, 2009.5), lty = 2)
 lines(never$year, never$x, col = "#8e549f", type = "b", pch = 15)
 lines(g1$year, g1$x, col = "#497eb3", type = "b", pch = 17)
 lines(g2$year, g2$x, col = "#d2382c", type = "b", pch = 16)
-legend(x=1990, y=7.1, col = c("#8e549f", "#497eb3", "#d2382c"), 
-       pch = c(15, 17, 16),
-       legend = c("Never Treated", "2000", "2010"))
-
-
+legend(
+  x = 1990, y = 7.1, col = c("#8e549f", "#497eb3", "#d2382c"),
+  pch = c(15, 17, 16),
+  legend = c("Never Treated", "2000", "2010")
+)
 ```
 
 ### Estimate Two-stage Difference-in-Differences 
 
 First, lets estimate a static did. There are two things to note here. First, note that I can use `fixest::feols` formula including the `|` for specifying fixed effects and `fixest::i` for improved factor variable support. Second, note that `did2s` returns a `fixest` estimate object, so `fixest::esttable`, `fixest::coefplot`, and `fixest::iplot` all work as expected.
 
 ```{r static}
-
 # Static
-static <- did2s(df_het, 
-				yname = "dep_var", first_stage = ~ 0 | state + year, 
-				second_stage = ~i(treat, ref=FALSE), treatment = "treat", 
-				cluster_var = "state")
+static <- did2s(df_het,
+  yname = "dep_var", first_stage = ~ 0 | state + year,
+  second_stage = ~ i(treat, ref = FALSE), treatment = "treat",
+  cluster_var = "state"
+)
 
 fixest::esttable(static)
-
 ```
 
 This is very close to the true treatment effect of ~2.23.
 
 Then, let's estimate an event study did. Note that relative year has a value of `Inf` for never treated, so I put this as a reference in the second stage formula.
 
 ```{r event-study}
-
 # Event Study
 es <- did2s(df_het,
-			yname = "dep_var", first_stage = ~ 0 | state + year, 
-			second_stage = ~i(rel_year, ref=c(-1, Inf)), treatment = "treat", 
-			cluster_var = "state")
-
+  yname = "dep_var", first_stage = ~ 0 | state + year,
+  second_stage = ~ i(rel_year, ref = c(Inf)), treatment = "treat",
+  cluster_var = "state"
+)
 ```
 
 And plot the results:
 
 ```{r plot-es, fig.cap="Event-study plot with example data"}
-
-fixest::iplot(es, main = "Event study: Staggered treatment", xlab = "Relative time to treatment", col = "steelblue", ref.line = -0.5)
+fixest::iplot(es, main = "Event study: Staggered treatment", xlab = "Relative time to treatment", col = "steelblue", ref.line = -0.5, drop = "Inf")
 
 # Add the (mean) true effects
-true_effects = head(tapply((df_het$te + df_het$te_dynamic), df_het$rel_year, mean), -1)
+true_effects <- head(tapply((df_het$te + df_het$te_dynamic), df_het$rel_year, mean), -1)
 points(-20:20, true_effects, pch = 20, col = "black")
 
 # Legend
-legend(x=-20, y=3, col = c("steelblue", "black"), pch = c(20, 20), 
-       legend = c("Two-stage estimate", "True effect"))
-
+legend(
+  x = -20, y = 3, col = c("steelblue", "black"), pch = c(20, 20),
+  legend = c("Two-stage estimate", "True effect")
+)
 ```
 
 
 ### Comparison to TWFE
 
 ```{r plot-compare, ig.cap="TWFE and Two-Stage estimates of Event-Study"}
-
-twfe = feols(dep_var ~ i(rel_year, ref=c(-1, Inf)) | unit + year, data = df_het) 
-
-fixest::iplot(list(es, twfe), sep = 0.2, ref.line = -0.5,
-      col = c("steelblue", "#82b446"), pt.pch = c(20, 18), 
-      xlab = "Relative time to treatment", 
-      main = "Event study: Staggered treatment (comparison)")
+twfe <- feols(dep_var ~ i(rel_year, ref = c(-1, Inf)) | unit + year, data = df_het)
+
+fixest::iplot(list(es, twfe),
+  sep = 0.2, ref.line = -0.5,
+  col = c("steelblue", "#82b446"), pt.pch = c(20, 18),
+  xlab = "Relative time to treatment",
+  main = "Event study: Staggered treatment (comparison)",
+  drop = "Inf"
+)
 
 
 # Legend
-legend(x=-20, y=3, col = c("steelblue", "#82b446"), pch = c(20, 18), 
-       legend = c("Two-stage estimate", "TWFE"))
-
+legend(
+  x = -20, y = 3, col = c("steelblue", "#82b446"), pch = c(20, 18),
+  legend = c("Two-stage estimate", "TWFE")
+)
 ```
 
 # Citation