What is the main purpose of using the synthetic control method (SCM) in economic research?

The synthetic control method (SCM) is primarily used to assess the impact of specific interventions or events on a single entity, like a state or region. SCM constructs a 'synthetic' version of the entity by weighting similar unaffected entities. By comparing the outcomes of the affected entity to its synthetic counterpart, researchers can isolate the impact of the event or intervention, providing a more accurate estimate of its effect than simple before-and-after comparisons.

What are the main challenges when applying the synthetic control method (SCM) to high-frequency data, such as monthly or daily data?

When applying the synthetic control method (SCM) to high-frequency data, two major challenges arise. First, achieving a good 'pre-treatment fit' becomes more difficult due to the increased number of data points that need to be aligned before the intervention. Second, there's a higher risk of overfitting to noise, where random fluctuations in the data might be misinterpreted as genuine patterns related to the intervention.

How does temporal aggregation help in mitigating the challenges associated with using the synthetic control method (SCM) on high-frequency data?

Temporal aggregation, like converting monthly data into annual averages, can help mitigate challenges by smoothing out noise and potentially improving the pre-treatment fit in the synthetic control method (SCM). However, temporal aggregation can also obscure important short-term variations in the data, so researchers must carefully consider the trade-off between reducing noise and preserving the signal.

What key factors should researchers consider when determining the optimal level of temporal aggregation to use with the synthetic control method (SCM)?

When deciding on the optimal level of temporal aggregation to use with the synthetic control method (SCM), researchers should consider four key factors: pre-treatment fit (how well the synthetic control matches the treated unit before the intervention), the risk of overfitting (whether noise is being mistaken for a real signal), signal strength (how much genuine variation exists in the disaggregated data), and bias reduction (how much does aggregation reduce bias). Balancing these considerations helps to ensure the reliability and accuracy of the analysis.

Why is it important to balance both disaggregated and aggregated data when using the synthetic control method (SCM), and what does this balance achieve?

Balancing both disaggregated and aggregated data in the synthetic control method (SCM) is crucial for robustness. By finding a 'synthetic control' that reflects pre-treatment trends in both high-frequency and lower-frequency data, researchers can potentially minimize bias and maximize the reliability of their findings. This approach ensures a more comprehensive understanding, mitigating risks of drawing conclusions based solely on noisy, short-term fluctuations or overly smoothed long-term trends.

Data stream flowing through an hourglass

Is Your Data Lying to You? How to Avoid Bias in Data Analysis

Beau Callahan in Business & Economy August 2025 • 5 min read.

"Discover the secret weapon economists use to refine accuracy in data - temporal aggregation. Unlock insights and make your data trustworthy."

In today's data-driven world, the synthetic control method (SCM) has become a popular tool for economists and researchers aiming to understand the impact of specific events or treatments on a single entity. Imagine you're trying to determine the economic effect of a new policy in one state. SCM allows you to create a 'synthetic' version of that state, based on a weighted average of other, similar states that weren't affected by the policy. This synthetic control acts as a benchmark, helping you isolate the true impact of the policy by comparing the affected state to its synthetic counterpart.

However, like any powerful tool, SCM comes with its own set of challenges, especially when dealing with data collected at high frequencies – think monthly or even daily, rather than annually. Two major hurdles arise: first, achieving a good 'pre-treatment fit' becomes much harder when you have many more data points to align before the event you're studying. Second, there's a higher risk of overfitting to noise, mistaking random fluctuations in the data for genuine patterns.

One potential solution is temporal aggregation, which involves combining data over time – for example, converting monthly data into yearly averages. While this can smooth out noise and improve the pre-treatment fit, it also risks obscuring important signals and nuances within the data. So, how do we strike the right balance? Recent research offers valuable insights into navigating this trade-off and ensuring the reliability of your data analysis.

Decoding Temporal Aggregation: Balancing Accuracy and Detail

Data stream flowing through an hourglass

Temporal aggregation, in essence, is about finding the sweet spot between the granularity of your data and the clarity of the insights you can extract. When you aggregate data (e.g., converting monthly sales figures to quarterly summaries), you reduce noise and simplify the overall picture. This can be particularly helpful when dealing with high-frequency data, where random fluctuations might otherwise obscure the underlying trends. However, aggregation also comes with a cost: the loss of detail. Important short-term variations can be smoothed out, potentially leading to a distorted view of reality.

The challenge, then, is to determine the optimal level of aggregation. Too little aggregation, and you risk overfitting to noise; too much, and you risk losing sight of the true signal. Researchers have identified several key considerations to help guide this decision:

Pre-treatment Fit: How well does your synthetic control match the treated unit's outcomes before the intervention? If you're struggling to achieve a good fit with disaggregated data, aggregation might help.
Overfitting: Are you potentially mistaking noise for a real signal? Aggregation can reduce the risk of overfitting, but be mindful of the potential loss of information.
Signal Strength: How much genuine variation exists within the disaggregated data? If the underlying signal is strong, you might be able to get away with less aggregation.
Bias Reduction: How much does aggregation reduce bias?

One promising approach involves finding a 'synthetic control' that balances both the disaggregated and aggregated series. This means creating a control group that accurately reflects the pre-treatment trends in both the high-frequency and lower-frequency data. By considering both perspectives, you can potentially minimize bias and maximize the robustness of your findings.

The Future of Data Analysis: Combining Perspectives for Robust Insights

Temporal aggregation is more than just a technical fix; it's a reminder of the importance of critical thinking and careful consideration when working with data. By understanding the trade-offs involved and embracing techniques that combine different perspectives, we can unlock more robust and reliable insights, ensuring that our data tells us the truth, the whole truth, and nothing but the truth. For those navigating complex datasets, remember that the right approach to aggregation can be a powerful tool in uncovering meaningful patterns while mitigating the risks of bias and noise.