Event Plot with `coefplot` (Event Studies)

In this exercise, you’ll graph the results of the event study you estimated in the previous exercise so we can visualize the dynamic effect of STRV seed adoption on crop yields over time.

• You already estimated the robust event study in the previous exercise using eventstudyinteract. Re-run that exact eventstudyinteract command (without the erepost part) to ensure the model results are fresh in Stata’s memory.
• Extract the interaction-weighted coefficients into a matrix: use matrix C = e(b_iw) to grab the point estimates from the stored results.
• The variance-covariance matrix e(V_iw) contains the variances on its diagonal. To get standard errors, you need the square root of those diagonal elements. Use mata to do this in one line: mata st_matrix( "A", sqrt( diagonal( st_matrix ("e(V_iw)") ) ) ).
• Stack the standard errors below the coefficients: matrix C = C \ A'. The backslash (\) stacks matrices vertically, and A' transposes A from a column vector to a row vector so it aligns with C. Now row 1 of C is the coefficients and row 2 is the standard errors.
• Print the matrix to verify: matrix list C. You should see one row of point estimates and one row of standard errors, with columns named after your lead and lag dummies.

1. How many columns does your matrix C have? Does this match the number of lead and lag dummies you created?

2.Now use coefplot to plot directly from the matrix C. Build the plot with the following options:

• Source the coefficients from row 1 of C using matrix(C[1]) and pair them with standard errors from row 2 using se(C[2]).
• Set the graph region background to white with graphregion(fcolor(white)).
• Label the x-axis “Event years” using xtitle() with size(medlarge).
• Orient the plot vertically and include the omitted period as a zero with the vertical and omitted options.
• Use square markers (msymbol(s)), colored black (mc(black)) with white fill (mfcolor(white)).
• Add a horizontal reference line at zero using yline(0) with thin black styling.
• Connect the points with thick black lines using recast(connected), lw(thick), and lc(black).
• Display the confidence intervals as dashed lines using ciopts(recast(rline) lw(thin) lc(black) lp(dash)).
• Add a vertical treatment reference line at position 16 (where g_1 would be — the last pre-treatment period) using xline() in red.
• Suppress the grid with ylabel(, angle(0) nogrid) and keep only the lead/lag coefficients with keep(g_* g*).
• Clean up the coefficient labels using rename(g_* = "-" g* = "") so leads show as negative numbers and lags as positive.
• Set the y-axis title to “Median EVI” and manually label the x-axis tick marks at informative intervals (e.g., positions 02, 07, 12, 16, 21, 25 labeled as “-15”, “-10”, “-5”, “0”, “5”, “10”).
• Export the plot and import it into your Overleaf file.

3. Do the pre-treatment coefficients hover near zero, consistent with parallel trends? After adoption, does the effect of STRV seed on evi_med appear to grow over time, or is it immediate and constant?

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