Determine location of structural break in cross-section data

In summary, the speaker is trying to run a regression for a data set with 165 observations and has found a structural break using a Chow Test. They have also found two different estimates when breaking the data into two categories based on the explanatory variable. However, they are looking for a more precise and mathematical method to determine where the structural break occurs. Suggestions for further analysis include cluster analysis or discriminant analysis, with a rule of thumb being to try different subsets of the data.
  • #1
phantomcow2
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I'm trying to run a simple regression for a data set (n=165). I've determined via a Chow Test that there exists a structural break somewhere; the same least squares estimate cannot be accurately used to represent the entire range of values the explanatory variable takes on. I've found two very different (.08 and .2) estimates if I break the data down into two catagories: whenexplanatory variable takes values of less than 70000, and where it takes values above 70,000.

However I only took an educated guess to arrive at the 70,000 figure. Is there a more precise, mathematical method available that allows me to determine where the structural break begins to occur?
 
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  • #2
phantomcow2 said:
However I only took an educated guess to arrive at the 70,000 figure. Is there a more precise, mathematical method available that allows me to determine where the structural break begins to occur?

Cluster analysis or discriminant analysis may be useful here. The latter is primarily for anticipated or predetermined subsets in the data.http://faculty.chass.ncsu.edu/garson/PA765/cluster.htm
 
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  • #3
There is no hard and fast rule -- a rule of thumb is to rank-order the data and try 5%, 10%, 25% and 50%.
 

Related to Determine location of structural break in cross-section data

1. How is a structural break defined in cross-section data?

A structural break in cross-section data refers to a significant change or shift in the underlying relationship between the variables being studied. This could be caused by a variety of factors such as policy changes, economic events, or natural disasters.

2. Why is it important to determine the location of a structural break?

Determining the location of a structural break is important because it allows researchers to identify and understand the causes of changes in the data, which can impact the validity of their findings and conclusions. It also helps to identify any potential outliers or anomalies in the data.

3. What methods can be used to determine the location of a structural break?

There are several methods that can be used to determine the location of a structural break, including statistical tests such as the Chow test, the Bai-Perron test, and the Quandt-Andrews test. These tests compare the data before and after a potential break point to determine if there is a significant difference.

4. How do researchers interpret the results of a structural break analysis?

The interpretation of a structural break analysis depends on the specific method used and the context of the data being studied. In general, if a significant break is detected, it suggests that the relationship between the variables has changed, and further investigation may be needed to understand the underlying causes.

5. Can structural breaks be predicted or prevented?

Structural breaks are often difficult to predict or prevent as they can be caused by a variety of external factors. However, understanding the potential causes and identifying patterns in the data may help researchers anticipate potential breaks and adjust their analysis accordingly.

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