### Install github package in Stata Source: https://github.com/lsun20/eventstudyinteract/blob/main/README.md Use this command to install the github package manager in Stata, which is required for installing other packages from GitHub. ```Stata net install github, from("https://haghish.github.io/github/") ``` -------------------------------- ### Manually install dependency packages in Stata Source: https://github.com/lsun20/eventstudyinteract/blob/main/README.md If the automatic installation of eventstudyinteract fails, manually install the required dependency packages: avar, reghdfe, and ftools. ```Stata ssc install avar ``` ```Stata ssc install reghdfe ``` ```Stata ssc install ftools ``` -------------------------------- ### Prepare Data and Run IW Estimator with Never-Treated Control Source: https://context7.com/lsun20/eventstudyinteract/llms.txt This example demonstrates the full data preparation and execution of the eventstudyinteract command using never-treated units as the control cohort. Ensure matrix size is set appropriately for a large number of indicators. ```stata * Load example data - National Longitudinal Survey of Young Women and Mature Women webuse nlswork, clear * Step 1: Create cohort variable based on initial treatment timing * Cohort = year when unit first received treatment (joined union) gen union_year = year if union == 1 bysort idcode: egen first_union = min(union_year) drop union_year * Step 2: Create relative time variable gen ry = year - first_union * Step 3: Define control cohort (never-treated units) gen never_union = (first_union == .) * Step 4: Generate relative time indicator variables * Pre-treatment indicators (leads) forvalues k = 18(-1)2 { gen g_`k' = ry == -`k' } * Post-treatment indicators (lags) forvalues k = 0/18 { gen g`k' = ry == `k' } * Step 5: Set matrix size for large number of indicators set matsize 800 * Step 6: Run eventstudyinteract eventstudyinteract ln_wage g_* g0-g18, \ cohort(first_union) \ control_cohort(never_union) \ covariates(south) \ absorb(i.idcode i.year) \ vce(cluster idcode) * Output: IW estimates for dynamic effects * Results stored in e(b_iw) and e(V_iw) ``` -------------------------------- ### Install eventstudyinteract package in Stata Source: https://github.com/lsun20/eventstudyinteract/blob/main/README.md Installs the eventstudyinteract package and its dependencies from GitHub. If this fails, manual installation of dependencies may be required. ```Stata github install lsun20/eventstudyinteract ``` -------------------------------- ### Run IW Estimator with Last-Treated Control Cohort Source: https://context7.com/lsun20/eventstudyinteract/llms.txt This example shows how to use the eventstudyinteract command when never-treated units are unavailable, by specifying the last-treated units as the control cohort. It requires sample restrictions and careful definition of indicator variables. ```stata * Define last-treated cohort as control (units first unionized in year 88) gen last_union = (first_union == 88) * Create binned indicator for distant leads (assuming constant effects) gen g_l4 = ry <= -4 * Run with last-treated control cohort * Restrict sample to: treated units only AND before last cohort's treatment eventstudyinteract ln_wage g_l4 g_3 g_2 g0-g18 \ if first_union != . & year < 88, \ cohort(first_union) \ control_cohort(last_union) \ covariates(south) \ absorb(i.idcode i.year) \ vce(cluster idcode) ``` -------------------------------- ### Perform Post-Estimation Hypothesis Testing Source: https://context7.com/lsun20/eventstudyinteract/llms.txt Enables standard Stata commands like test and lincom by posting IW estimates to e(b) and e(V) matrices. ```stata * Post IW estimates to enable standard Stata post-estimation commands matrix b = e(b_iw) matrix V = e(V_iw) ereturn post b V * Joint F-test of pretrends * Tests whether all pre-event coefficients are jointly zero test (g_l4=0) (g_3=0) (g_2=0) * Aggregate effects over time periods * Example: Average effect over first 5 years post-treatment lincom (g0 + g1 + g2 + g3 + g4)/5 ``` -------------------------------- ### Create Event Study Plots with coefplot Source: https://context7.com/lsun20/eventstudyinteract/llms.txt Extracts point estimates and standard errors from stored matrices to visualize dynamic treatment effects. ```stata * Extract IW estimates matrix C = e(b_iw) * Extract standard errors from variance-covariance matrix diagonal mata st_matrix("A", sqrt(diagonal(st_matrix("e(V_iw)")))) * Combine estimates and standard errors matrix C = C \ A' * View the combined matrix matrix list C * Create event study plot * First row = coefficients, second row = standard errors coefplot matrix(C[1]), se(C[2]) ``` -------------------------------- ### Accessing Stored Estimation Results from EventStudyInteract Source: https://context7.com/lsun20/eventstudyinteract/llms.txt After running eventstudyinteract, various estimation results are stored in e(). This snippet shows how to list key matrices such as IW estimates, variance-covariance matrices, cohort-specific effects, and cohort shares for further analysis. ```stata * After running eventstudyinteract, access stored results: * IW estimates vector matrix list e(b_iw) * Variance-covariance matrix for IW estimates matrix list e(V_iw) * Cohort-specific effects for each relative time * Rows = cohorts, Columns = relative times matrix list e(b_interact) * Variance of cohort-specific effect estimators matrix list e(V_interact) * Cohort shares underlying each relative time * Shows how much weight each cohort contributes matrix list e(ff_w) * Variance of cohort share estimators matrix list e(Sigma_ff) * Verify IW estimate is weighted average of cohort-specific effects * Example: For the first relative time indicator (g_l4) matrix delta = e(b_interact) matrix weight = e(ff_w) matrix nu = delta[1...,1]' * weight[1...,1] matrix list nu * This should match first element of e(b_iw) ``` -------------------------------- ### Compile reghdfe package in Stata Source: https://github.com/lsun20/eventstudyinteract/blob/main/README.md If you encounter a 'class FixedEffects undefined' error, try recompiling the reghdfe package as suggested by its repository. ```Stata reghdfe, compile ``` -------------------------------- ### Conduct Subsample Comparison Analysis Source: https://context7.com/lsun20/eventstudyinteract/llms.txt Compares treatment effects across subgroups by interacting relative time indicators with group indicators. ```stata * Generate relative time indicators interacted with college graduate status * For non-college graduates (collgrad == 0) forvalues k = 18(-1)2 { gen g_`k'_collgrad0 = ry == -`k' & collgrad == 0 } forvalues k = 0/18 { gen g`k'_collgrad0 = ry == `k' & collgrad == 0 } gen g_l4_collgrad0 = ry <= -4 & collgrad == 0 * For college graduates (collgrad == 1) forvalues k = 18(-1)2 { gen g_`k'_collgrad1 = ry == -`k' & collgrad == 1 } forvalues k = 0/18 { gen g`k'_collgrad1 = ry == `k' & collgrad == 1 } gen g_l4_collgrad1 = ry <= -4 & collgrad == 1 * Run combined estimation eventstudyinteract ln_wage /// g_l4_collgrad* g_3_collgrad* g_2_collgrad* /// g0_collgrad0-g18_collgrad0 g0_collgrad1-g18_collgrad1 /// if first_union != . & year < 88, /// cohort(first_union) /// control_cohort(last_union) /// absorb(i.idcode i.year) /// vce(cluster idcode) * Test difference in average effects over first 5 years matrix b = e(b_iw) matrix V = e(V_iw) ereturn post b V lincom (g0_collgrad1 + g1_collgrad1 + g2_collgrad1 + g3_collgrad1 + g4_collgrad1)/5 /// - (g0_collgrad0 + g1_collgrad0 + g2_collgrad0 + g3_collgrad0 + g4_collgrad0)/5 ``` -------------------------------- ### Update eventstudyinteract package in Stata Source: https://github.com/lsun20/eventstudyinteract/blob/main/README.md Use this command to update the eventstudyinteract package to the latest version available on GitHub. ```Stata github update eventstudyinteract ``` === COMPLETE CONTENT === This response contains all available snippets from this library. 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