Could you please clarify how to add robust (white) errors to the function -xthenreg in Stata14? I am running Threshold GMM Estimator (Seo and Shin (2016)) for my panel (N=60, T=28). Commands such as -vce or -robust, that are applicable for other types of threshold regressions or usual GMM, do not work here. Thank ypu for your help!
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Chuck A Arize
Visual Inspection: Start by plotting the time series data before and after the event. Look for any noticeable changes in the trend, level, or seasonality of the data following the event. This can give you an initial understanding of the impact. Regression Analysis: Use regression models to estimate the relationship between the event and the dependent variable while controlling for other factors. You can include dummy variables for the event period or use interrupted time series analysis to model the effect of the event on the trend. Interrupted Time Series Analysis: This method involves comparing the trend of the dependent variable before and after the event while accounting for any pre-existing trend. It often uses segmented regression models to estimate separate slopes for the pre- and post-event periods. Control Group Analysis: If possible, identify a control group that was not affected by the event and compare their trends with those of the treatment group. This helps isolate the effect of the event from other factors that might influence the trend. Difference-in-Differences (DID): Similar to control group analysis, DID compares the changes in the dependent variable between the treatment group (affected by the event) and the control group (unaffected by the event) before and after the event. Granger Causality Test: This statistical test examines whether past values of one time series variable can predict the current values of another time series variable. It can help determine if the event has a causal relationship with the trend of the dependent variable. ARIMA Models: Autoregressive Integrated Moving Average (ARIMA) models can be used to forecast future values of the dependent variable while considering its past values and any identified trends or seasonal patterns. Changes in the model parameters before and after the event can indicate its impact on the trend.
Studying the impact of an event on the trend of a dependent variable in time series data involves various statistical methods. Here are some common approaches:
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