Panel data is a dataset that contains the same units of observations over multiple periods collected at a regular or irregular frequency. The behavior of each entity in a panel data is observed at different points in time.
Unlike time series, panel data contains more variability, efficiency, and even information. It can also help to identify and measure statistical effects that time series cannot. In this guide, we’ll walk you through the definition of panel data, why it matters, how it is collected, as well as the modelling tools used in panel data analysis.
What Is Panel Data?
Panel data, which is also called longitudinal data, is multi-dimensional data that tracks the same units over multiple time frames. In plain terms, it’s like watching the same people, companies, or places repeatedly over time to see how they change.
According to Wikipedia, panel data combines the strengths of cross-sectional data and time-series data. It aids causal analysis by controlling time-invariant factors (e.g., with FE), but identification still requires design-specific assumptions (e.g., DiD’s parallel trends).
Panel data is either balanced or unbalanced. For a balanced panel data, every unit has data for all time periods, while some units may be missing in some periods in an unbalanced panel data.
How Does a Panel Dataset Look in Practice?
Here is how a simple 2 × 3 panel dataset might look:
| Year | Person A | Person B |
|---|---|---|
| 2021 | $45,000 | $50,000 |
| 2022 | $47,000 | $52,000 |
| 2023 | $49,000 | $— |
In this example, person A has income data for all three years, while person B has income data for two years and is missing a value in 2023. Since a value is missing, the panel is unbalanced. When working with an unbalanced panel, the model accuracy and estimation may be affected because of the missing values.
Fixed Effects (FE) and Random Effects (RE) models can handle unbalanced panels, although missing data may reduce efficiency and precision compared to balanced panels. Where missing attributes can not be ignored, it is best to use diagnostics and methods like Multiple Imputation (MI) or Inverse Probability Weighting (IPW) to guard against attrition bias.
Further reading: What Is Data Parsing: Benefits, Tools, and How It Works and Web Scraping with Javascript and Nodejs (2025): Automation and Dynamic Content Scraping.
Why Does Panel Data Matter for Modern Analytics?
Panel data is important as it reveals not only how things change across entities but within them as well. Panel data makes it possible for analysts to isolate individual traits that influence outcomes. With the data for repeated observation of the same unit, it is easier to study dynamics like income mobility or disease progression.
When applied in the real world, panel data can make a whole lot of difference. Governments utilize panel data to study employment trends and the long-term effects of policies. Companies can track customer behavior over a period and have a better understanding of purchasing patterns with information obtained from panel data. Researchers in the medical field are able to evaluate the impact of treatments and lifestyle interventions.
How Is Panel Data Collected in the Real World?
The following are the common sources of panel data:
- Longitudinal Surveys: These are structured studies that are designed to monitor the same participants over different time frames. An example is Nielsen’s Homescan Consumer panel, where participating households are required to scan the items they purchased in order to provide insight into consumer behaviour.
- Web-Scraped Price Panel: Companies crawl e-commerce websites daily to document the prices of store-keeping units (SKUs) over a time frame. They then utilize this data to build product price panels. Some teams use proxies to lower blocking risk. Note that robots.txt is a technical guideline, not a legal authorization. Since scraping laws vary by jurisdiction and context, it is best to seek legal counsel before collecting data from websites.
- Administrative Feeds: Businesses can collect data from various systems like mobile apps, IoT sensors, and loyalty cards. This type of panel data does not rely on the active input of users.
What Is Consumer Panel Data?
Consumer panel data is data that is collected from a group of individuals who regularly provide information concerning their everyday purchases for a period of time. This offers a buffet of information on customer behavior that businesses in various sectors can utilize.
Participants, usually known as a panel, are given either mobile apps or barcode scanners to log the items they purchase as well as other vital information. Responses are usually weighted using demographics like income, region, and age. Consumer packaged goods (CPG) firms use the data to measure market share with a higher degree of accuracy.
How Do Proxies Sustain Web-Scraped Panels?
Web-scraped panels collect consistent, time-stamped data. To effectively carry out this function, they will need to rely on rotating residential proxies so as not to get blocked.
Rotating residential proxies from Live Proxies, for instance, gives you access to ethically sourced, authentic IPs that mimic real user behavior. The IPs are flexible, easy to scale, and adapt across multiple use cases.
However, modern defenses also check for fingerprints and behavior to determine bot activities. So, additional controls may be needed.
What Types of Panel Data Exist?
Panel datasets come in several forms, including:
Balanced Versus Unbalanced Panels
Balanced panels have complete observations for all the units across all time periods, while unbalanced panels may have some data points missing for some units. The difference between these two comes down to the completeness of observations over time.
For example, if you are covering 100 customers for a period of 6 months, each customer should appear exactly 6 times. However, for unbalanced panels, some customers may appear less than 6 times.
Short (large N, small T) vs. Long (small N, large T) Panels
Short panels ( large N, small T) have a large number of units (N) and a small number of time periods (T). Short panels are more common in marketing, where companies track a large number of consumers for a short time frame. Long panels ( small N, large T), however, track a small number of units over a large time period. They are common in macro panel time series.
Micro vs. Macro Panels
Micro panels track individual-level data and often require more memory and storage. Meanwhile, Long T panels (often macro) most time require tools for nonstationarity and structural breaks. Sensitivity is often a reflection of long horizons and not merely the aggregation level.
Which Econometric Models Unlock Panel Insights?
Here are four economic models that are commonly used in panel insight:
- Fixed Effects (FE): This model controls all time-invariant differences across units. It is perfect when you want to ask questions like “What changes within each unit explain the outcome?”
- Random Effects (RE): This assumes that the unobserved differences across units are uncorrelated with predictors. This model is useful when variation between units provides a lot of information.
- Difference-in-Differences (DiD): This model compares before-and-after changes between a treated and control group. Use RiD when parallel trends (and related assumptions) are plausible. Ensure you check pre-trends and consider modern DiD estimators for staggered adoption.
- Dynamic Panels: Dynamic panel models outcomes as functions of past values. (e.g., Arellano-Bond / Blundell-Bond for short T, large N). It is ideal for capturing feedback effects.
These models are supported in all major tools, including:
- Stata: xtreg, xtabond, xtdidregress/xthdidregress
- R: fixest, plm, did/DRDID
- Python: linearmodels
When Should Analysts Choose FE Over RE?
Choose Fixed Effects (FE) if there are unobserved differences between units related to your explanatory variables. Fixed effect controls for time-invariant traits like location so that your results do not become biased.
However, if the unobserved traits are truly unrelated to your regressors, it is more efficient to use random effects. This is the logic that is used for the Hausman test, which checks whether FE and RE give significantly different results. If they do, it’s a sign that RE’s assumptions don’t hold and FE is the better, more reliable choice.
How Do Researchers Handle Unbalanced Panels?
Researchers use multiple techniques to handle unbalanced panels. They include:
- Dropping Sparse Units: If specific units have too few observations, they may be excluded to avoid skewing results.
- Multiple Imputation: Missing values may be filled based on observed data. This preserves sample size and may lower bias.
- Inverse Probability Weighting (IPW): Weights are applied based on the likelihood that a unit is observed. This helps to correct non-random attrition.
Advantages and Drawbacks of Panel Data
The advantages of panel data include:
- It offers greater variability and efficiency as it combines time-series and cross-sectional data. This helps to improve the estimate precision.
- It controls for unobserved individual traits, which strengthens causal inference.
- Panel data captures behavioral changes over time.
Here are the drawbacks of panel data
- Dropouts over time can skew results, resulting in attrition bias.
- Repeated data collection increases the risk of noise, which may lead to measurement error.
- Time-based data often violates independence assumptions, which complicates analysis.
- It requires intensive data cleaning, merging, and validation.
How Do You Build a Panel Dataset from Scratch?
Here are step-by-step methods to build a panel dataset:
- Choose Entities and Time Cadence: Determine what you want to track and the time period. It can be products or households, and the period can be weekly, monthly, or yearly.
- Define Your Collection Method: Decide the exact data you want to capture and how it will be collected. For instance, if you want to track opinion, it may be best to use surveys as a means of capturing the data.
- Maintain Stable Identifiers: Ensure each entity has a persistent ID across time periods. Having stable IDs helps to prevent duplicates and mismatches.
- Reshape to Long Format: Transform raw wide-format data into long format, where each row represents an entity-time observation. Tools like Pandas.melt (Python) or Pivot_longer (R) can help structure the dataset for time-based analysis.
- Run Descriptive Diagnostics: Check for gaps, duplicates, and unbalanced panel issues. You should also track metrics like missing periods for each entity or variance across time before modelling.
What Risks Threaten Panel Quality and How Can They Be Mitigated?
Several risks can undermine panel integrity, including:
- Attrition: Participants may drop out before the end of the time frame. This may lead to reduced sample diversity. You can prevent this by offering retention incentives and rewards to keep participants engaged.
- Conditioning: Individuals who may have participated for a long time may start to predict question intent, which may lead to skewed responses. You should therefore diversify how the question is phrased as well as survey paths to help preserve data authenticity.
- Measurement Drift: Inconsistencies in data collection processes or even question interpretation can happen as panels evolve. Therefore, regular audits can help to keep outputs stable.
- IP Bans and Gaps in Scraped Panels: IP blocks and domain blacklists may reduce sample reach in scraped panels. You should implement automated gap alerts to flag anomalies early.
Where Is Panel Data Headed Between 2025 and 2030?
The future of panel data is undergoing a transformative shift. Between 2025 and 2030, it is speculated that traditional panels will give way to passively metered panels. These panels will be powered by wearables like health bands and smartwatches. Synthetic panels that are generated through machine learning are also gaining momentum. These virtual panels are able to simulate behavior without exposing people's identities.
Data collection is also decentralizing, and in a few years, there will be an increase in federated panel architectures where data will be collected through smart appliances and mobile devices. In a nutshell, panel data in the next five years will be safer, smarter, and more distributed.
Further reading: How to Scrape Amazon: Product Data & Reviews (2025) and What Is an HTTP Proxy? Definition, Uses & How It Works.
Conclusion
Panel data is essential for the functioning of econometric models, which transform raw behavior into actionable insights. As the future leans towards passive metering and AI synthesis, panel data will continue to evolve to align with emerging trends.
FAQs about Panel Data
How Large Should a Panel Be for Reliable Regression?
There is no universal minimum for reliable regression. You should use power/precision targets. When working with logistics models, consider events-per-variable guidance (debated to be between 10-20 EPV).
Can Machine-Learning Models Handle Panel Structures?
Yes, machine-learning models can handle panel structures. With gradient boosting, you can incorporate entity dummies. Recurrent neural networks are also ideal for sequential patterns. However, you need to structure cross-validation to preserve time order and avoid data leakage.
Is a Balanced Panel Essential for Fixed-Effects Models?
No, balanced data isn't required for FE. You can assess missingness (e.g., attrition) and use MI/IPW or weighting when appropriate.
Are Proxies Ethical for Building Price Panels?
Yes, proxies can be ethical for building price panels when used responsibly. Use robots.txt as a technical guideline, but bear in mind that legal compliance is separate. Also, pace your requests to reduce server strain, and go for reputable providers like Live Proxies that publish clear, responsible scraping guidelines.
