What are the different methods that can be used to study a impact of an event on the trend of a dependent variable in a time series data?

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Lee De Cola

Create a dummy variable with the values 0 = before and 1 = after and include it in a regression with time as the other independent variable.

Michael R Jonas

An Event Study would be an appropriate model in this case. See Chapter 17 of the excellent textbook The Effect (https://theeffectbook.net/ch-EventStudies.html) by Nick Huntington-Klein (https://www.researchgate.net/profile/Nick-Huntington-Klein)

Chuck A Arize

Studying the impact of an event on the trend of a dependent variable in time series data requires careful analysis. Here are several methods commonly used for this purpose:

Visual Inspection: Start by plotting the time series data and visually inspecting it for any noticeable changes or anomalies around the time of the event. This can provide initial insights into the potential impact of the event on the trend.

Control Charts: Control charts, such as the Shewhart control chart or the CUSUM chart, are useful for detecting shifts or changes in the mean or variance of a time series. These charts can help identify when the event occurred and its impact on the trend.

Interrupted Time Series Analysis: This method involves modeling the time series both before and after the event using regression analysis. By comparing the pre-event and post-event trends, you can assess the impact of the event on the dependent variable.

Difference-in-Differences (DID) Analysis: DID analysis compares changes in the dependent variable over time between a treatment group (exposed to the event) and a control group (not exposed to the event). This method helps isolate the effect of the event from other confounding factors.

Event Study Analysis: Event study analysis focuses on examining the abnormal changes in the dependent variable around the time of the event. This involves estimating the expected trend of the dependent variable and comparing it to the actual trend observed after the event.

Granger Causality Test: Granger causality tests whether past values of one time series help in predicting future values of another time series. This method can be used to assess if the event has a causal effect on the trend of the dependent variable.

Regression Analysis with Dummy Variables: Include dummy variables in a regression model to account for the effect of the event. Dummy variables take the value of 1 during the period affected by the event and 0 otherwise, allowing you to estimate the impact of the event on the trend.

Studying the impact of an event on the trend of a dependent variable in a time series data requires careful analysis to understand how the event influences the variable over time. Here are some common methods used for this purpose:

Visual Inspection: Plotting the time series data along with the event occurrence can provide initial insights into any apparent changes in trend or pattern following the event.

Descriptive Statistics: Calculating summary statistics such as mean, median, or standard deviation before and after the event can help quantify the impact.

Interrupted Time Series Analysis: This method involves modeling the time series data both before and after the event using regression techniques to estimate the effect of the event while controlling for pre-existing trends.

Justin Ejukwa

Use correlation or regression methods