Dear Statalist Members,
I am a beginner in this area and have been grappling with the correct methodological approach for my study. I would greatly appreciate any insights from the community.
Background & Research Objective
I am studying the potential liquidity and economic benefits of a hypothetical Western Balkan stock exchange merger. Since no actual merger has taken place, standard difference-in-differences (DiD) or event-study methodologies do not apply due to the absence of an observable treatment group.
Given this, I am considering using the Synthetic Control Method (SCM) to construct a counterfactual comparison. My approach involves creating two separate synthetic control groups:
1. First synthetic control (Non-Merged WBSE): Construct a counterfactual Western Balkan stock exchange that represents the region’s stock market in the absence of a merger. The donor pool consists of the Baltic states (Estonia, Latvia, Lithuania) before their integration into NASDAQ OMX, selected for their historical similarities with the Western Balkans in terms of market size, transition from socialist economies, and financial integration processes.
2. Second synthetic control (Hypothetical Merged WBSE): Construct a synthetic version of a merged Western Balkan stock exchange. The donor pool remains the same, but the weighting scheme would be adjusted to reflect expected post-merger liquidity and efficiency improvements.
Challenges & Questions
• Is SCM appropriate for this setup? SCM is typically used to estimate a counterfactual for an actual intervention, whereas here, both the “treated” and “control” units are synthetic constructs. Would a modified version of SCM still be valid?
• Weight adjustment for the merged exchange: Standard SCM optimizes weights based on pre-treatment characteristics, but I am proposing an adjustment to reflect expected post-merger improvements. Is there an established way to handle such cases in SCM?
• Implementation in Stata: Given that I am constructing two synthetic controls, should I run separate SCM estimations for each case, or is there an alternative way to compare them directly within the same framework?
• Alternative methodologies: If SCM is not the best-suited approach, what alternative econometric techniques would you recommend for evaluating a hypothetical merger?
Many thanks!
I am a beginner in this area and have been grappling with the correct methodological approach for my study. I would greatly appreciate any insights from the community.
Background & Research Objective
I am studying the potential liquidity and economic benefits of a hypothetical Western Balkan stock exchange merger. Since no actual merger has taken place, standard difference-in-differences (DiD) or event-study methodologies do not apply due to the absence of an observable treatment group.
Given this, I am considering using the Synthetic Control Method (SCM) to construct a counterfactual comparison. My approach involves creating two separate synthetic control groups:
1. First synthetic control (Non-Merged WBSE): Construct a counterfactual Western Balkan stock exchange that represents the region’s stock market in the absence of a merger. The donor pool consists of the Baltic states (Estonia, Latvia, Lithuania) before their integration into NASDAQ OMX, selected for their historical similarities with the Western Balkans in terms of market size, transition from socialist economies, and financial integration processes.
2. Second synthetic control (Hypothetical Merged WBSE): Construct a synthetic version of a merged Western Balkan stock exchange. The donor pool remains the same, but the weighting scheme would be adjusted to reflect expected post-merger liquidity and efficiency improvements.
Challenges & Questions
• Is SCM appropriate for this setup? SCM is typically used to estimate a counterfactual for an actual intervention, whereas here, both the “treated” and “control” units are synthetic constructs. Would a modified version of SCM still be valid?
• Weight adjustment for the merged exchange: Standard SCM optimizes weights based on pre-treatment characteristics, but I am proposing an adjustment to reflect expected post-merger improvements. Is there an established way to handle such cases in SCM?
• Implementation in Stata: Given that I am constructing two synthetic controls, should I run separate SCM estimations for each case, or is there an alternative way to compare them directly within the same framework?
• Alternative methodologies: If SCM is not the best-suited approach, what alternative econometric techniques would you recommend for evaluating a hypothetical merger?
Many thanks!
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