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//! Event study estimation: DiD with leads and lags.
//!
//! Estimates dynamic treatment effects by regressing the outcome on
//! a set of event-time dummies (relative to a reference period),
//! plus controls and fixed effects.
use crate::ols::OlsResult;
use greeners_core::error::GreenersError;
use greeners_core::linalg::LinalgInverse as _;
use greeners_core::CovarianceType;
use ndarray::{Array1, Array2};
use statrs::distribution::{ContinuousCDF, StudentsT};
use std::fmt;
/// Result of an event study estimation.
#[derive(Debug)]
pub struct EventStudyResult {
/// Coefficients on event-time dummies (excluding reference period)
pub event_coefs: Array1<f64>,
/// Standard errors on event-time dummies
pub event_se: Array1<f64>,
/// t-statistics on event-time dummies
pub event_t: Array1<f64>,
/// p-values on event-time dummies
pub event_p: Array1<f64>,
/// Event times corresponding to each coefficient (excluding reference)
pub event_times: Vec<i64>,
/// Reference period (usually 0 or -1)
pub reference: i64,
/// Full OLS result (all coefficients)
pub ols: OlsResult,
/// Column indices of event dummies in the full design matrix
pub event_col_indices: Vec<usize>,
}
impl fmt::Display for EventStudyResult {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
writeln!(f, "\n{:=^78}", " Event Study Results ")?;
writeln!(
f,
"{:<20} {:>12} Reference period: t={}",
"Reference:", "", self.reference
)?;
writeln!(f, "{:-^78}", "")?;
let header = format!(
"{:<12} {:>12} {:>12} {:>10} {:>10}",
"Event time", "Coef.", "Std.Err.", "t", "P>|t|"
);
writeln!(f, "{header}")?;
writeln!(f, "{:-^78}", "")?;
for (i, &t) in self.event_times.iter().enumerate() {
writeln!(
f,
"t={:<8} {:>12.4} {:>12.4} {:>10.3} {:>10.4}",
t, self.event_coefs[i], self.event_se[i], self.event_t[i], self.event_p[i]
)?;
}
writeln!(f, "{:=^78}", "")
}
}
pub struct EventStudy;
impl EventStudy {
/// Estimate an event study (dynamic DiD with leads and lags).
///
/// Constructs event-time dummies for each period relative to the
/// event, excluding the reference period, and runs OLS.
///
/// # Arguments
/// * `y` - Outcome variable (n × 1)
/// * `event_time` - Event time for each observation (e.g., -3, -2, -1, 0, 1, 2)
/// Use `i64::MIN` or a very negative number for never-treated / not-yet-treated
/// * `x_controls` - Additional controls (n × k), can be empty (n × 0)
/// * `reference` - Event time to exclude as reference (usually -1 or 0)
/// * `min_event_time` - Minimum event time to include (e.g., -5)
/// * `max_event_time` - Maximum event time to include (e.g., 5)
/// * `cov_type` - Covariance type for standard errors
pub fn fit(
y: &Array1<f64>,
event_time: &[i64],
x_controls: &Array2<f64>,
reference: i64,
min_event_time: i64,
max_event_time: i64,
cov_type: CovarianceType,
) -> Result<EventStudyResult, GreenersError> {
let n = y.len();
if event_time.len() != n {
return Err(GreenersError::ShapeMismatch(format!(
"y (len={n}) and event_time (len={}) must have same length",
event_time.len()
)));
}
if x_controls.nrows() != n {
return Err(GreenersError::ShapeMismatch(format!(
"y (n={n}) and x_controls (nrows={}) must have same number of rows",
x_controls.nrows()
)));
}
// Build event-time dummies (excluding reference period)
let mut event_times: Vec<i64> = Vec::new();
for t in min_event_time..=max_event_time {
if t != reference {
event_times.push(t);
}
}
let n_event = event_times.len();
let k_controls = x_controls.ncols();
let k = 1 + n_event + k_controls; // intercept + event dummies + controls
let mut x = Array2::<f64>::zeros((n, k));
// Intercept
for i in 0..n {
x[(i, 0)] = 1.0;
}
// Event dummies
let mut event_col_indices = Vec::new();
for (j, &t) in event_times.iter().enumerate() {
let col = 1 + j;
event_col_indices.push(col);
for i in 0..n {
// Only include observations with valid event time
if event_time[i] != i64::MIN && event_time[i] == t {
x[(i, col)] = 1.0;
}
}
}
// Controls
for j in 0..k_controls {
for i in 0..n {
x[(i, 1 + n_event + j)] = x_controls[(i, j)];
}
}
// OLS
let x_t = x.t();
let xtx = x_t.dot(&x);
let xtx_inv = xtx.inv()?;
let xty = x_t.dot(y);
let beta = xtx_inv.dot(&xty);
let residuals = y - x.dot(&beta);
let df_resid = n - k;
let sigma2 = residuals.dot(&residuals) / df_resid as f64;
let cov = match cov_type {
CovarianceType::NonRobust => xtx_inv * sigma2,
CovarianceType::HC1 => {
let mut meat = Array2::<f64>::zeros((k, k));
for i in 0..n {
let xi = x.row(i);
let ei = residuals[i];
let w = if df_resid > 0 {
n as f64 / df_resid as f64
} else {
1.0
};
for a in 0..k {
for b in 0..k {
meat[(a, b)] += w * ei * ei * xi[a] * xi[b];
}
}
}
xtx_inv.dot(&meat).dot(&xtx_inv)
}
_ => {
// For other cov types, fall back to HC1
let mut meat = Array2::<f64>::zeros((k, k));
for i in 0..n {
let xi = x.row(i);
let ei = residuals[i];
let w = if df_resid > 0 {
n as f64 / df_resid as f64
} else {
1.0
};
for a in 0..k {
for b in 0..k {
meat[(a, b)] += w * ei * ei * xi[a] * xi[b];
}
}
}
xtx_inv.dot(&meat).dot(&xtx_inv)
}
};
let std_errors = cov.diag().mapv(|v| v.sqrt());
let t_values = &beta / &std_errors;
let t_dist = StudentsT::new(0.0, 1.0, df_resid as f64)
.map_err(|e| GreenersError::InvalidOperation(e.to_string()))?;
let p_values = t_values.mapv(|t| 2.0 * (1.0 - t_dist.cdf(t.abs())));
// Extract event dummy coefficients
let mut event_coefs = Vec::new();
let mut event_se = Vec::new();
let mut event_t = Vec::new();
let mut event_p = Vec::new();
for &col in &event_col_indices {
event_coefs.push(beta[col]);
event_se.push(std_errors[col]);
event_t.push(t_values[col]);
event_p.push(p_values[col]);
}
let r_squared = {
let y_mean = y.mean().unwrap_or(0.0);
let tss = y.mapv(|v| (v - y_mean).powi(2)).sum();
let rss = residuals.dot(&residuals);
if tss > 1e-15 {
1.0 - rss / tss
} else {
0.0
}
};
let ols = OlsResult {
params: beta,
std_errors,
t_values,
p_values,
conf_lower: Array1::zeros(k),
conf_upper: Array1::zeros(k),
r_squared,
adj_r_squared: 1.0 - (1.0 - r_squared) * (n - 1) as f64 / df_resid.max(1) as f64,
f_statistic: 0.0,
prob_f: 0.0,
log_likelihood: 0.0,
aic: 0.0,
bic: 0.0,
n_obs: n,
df_resid,
df_model: k - 1,
sigma: sigma2.sqrt(),
cov_type,
inference_type: greeners_core::types::InferenceType::StudentT,
variable_names: None,
omitted_vars: Vec::new(),
x_clean: None,
};
Ok(EventStudyResult {
event_coefs: Array1::from(event_coefs),
event_se: Array1::from(event_se),
event_t: Array1::from(event_t),
event_p: Array1::from(event_p),
event_times,
reference,
ols,
event_col_indices,
})
}
}