descending – more details about this option can be found in Lunt (2014).logit – use logit instead of the default probit to estimate the propensity score.I would add this option to find more unique matched controls. noreplacement – perform one-to-one matching without replacement.There are three options in the above command: First, we need to install the module in Stata by typing: Psmatch2 is a user-written module to find out matched controls using PSM. The single nearest neighbour in terms of propensity score will be selected as the matched control, and then DID regressions can be done subsequently. We need to do a probit or logit regression for PSM: The pre-event variables can be measured either at the most recent date before the event (e.g., the total assets at the most recent quarter end before the event) or at the average over the pre-event period (e.g., the average total assets in the four quarters preceding the event). It is common that we do a one-to-one matching, and it arguably makes more sense that such one-to-one matching is done by using selected pre-event and firm-level variables ( Xs). Where TREATMENT often indicates an event and POST indicates before or after that event. Outcome = TREATMENT + POST + TREATMENT * POST firm-years) in a difference-in-differences (DID) research design, so that there are two dummy variables, TREATMENT and POST, in the following regression: However, accounting research often uses panel data (i.e., observations with two subscripts i and t, e.g. Most propensity score matching (PSM) examples are using cross-sectional data instead of panel data.
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