Futuristic cityscape with economic indicators illustrating panel data forecasting.

Panel Data Forecasting: How to Select the Best Model for Accurate Predictions

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Elliot Brynn in Business & Economy August 2025 • 4 min read.

"Unlock the power of panel data for economic forecasting with new model selection methods that enhance accuracy and efficiency."


In today's interconnected world, economic forecasting plays a crucial role in informing policy decisions and investment strategies. Panel data models, which combine cross-sectional and time-series data, offer a powerful approach to forecasting economic variables. These models can capture the complexities of economic systems, providing insights that traditional time-series specifications may miss. The ability to account for cross-sectional heterogeneity—the differences between individual units such as countries, firms, or households—makes panel data models particularly appealing for forecasting applications.

Empirical evidence supports the superiority of panel data models in many forecasting scenarios. Studies have shown that panel models, when appropriately specified, often produce more accurate forecasts than their time-series counterparts. This advantage holds across various macroeconomic and microeconomic datasets, including those with long time series dimensions. For example, panel models have been successfully applied to forecast exchange rates, migration patterns, gasoline consumption, and firm-level investment. Despite the proven benefits of panel data forecasting, practitioners face a significant challenge: selecting the optimal model specification.

Unlike time-series forecasting, where a well-established theoretical framework guides model selection, panel data forecasting lacks such a clear roadmap. The existing theoretical research offers limited guidance on how to choose the most appropriate specification for a panel forecasting model. This gap between theory and practice motivates the development of new model selection methods tailored to the unique characteristics of panel data.

Optimizing Panel Data Forecasts: A New Approach to Model Selection

Futuristic cityscape with economic indicators illustrating panel data forecasting.

This article introduces new model selection methods designed to enhance the accuracy and efficiency of panel data forecasting. These methods build upon a foundation of least squares (LS) vector autoregressions, where model selection is based on minimizing the estimated quadratic forecast risk among candidate models. By carefully evaluating the trade-offs between model complexity and goodness of fit, these techniques aim to identify the specification that yields the most reliable predictions.

A key feature of the proposed model selection criteria is their asymptotic efficiency. Under specific conditions, these criteria can achieve Shibata's (1980) notion of efficiency as both the number of cross-sections (n) and the number of time series observations (T) grow infinitely large. This asymptotic property ensures that the model selection process becomes increasingly accurate as more data becomes available.

  • Improved Accuracy: Panel data models often outperform time-series models.
  • Accounts for Heterogeneity: Essential for capturing real-world complexities.
  • Minimizes Forecast Risk: Balances model complexity and fit for optimal prediction.
  • Asymptotic Efficiency: Accuracy increases with more data over time.
Compared to existing selection criteria, the new approach places a greater emphasis on model dimensionality. This heavier penalty is necessary to account for the effects of parameterized forms of cross-sectional heterogeneity, such as fixed effects, on forecast loss. In addition, the analysis extends to bias-corrected least squares, demonstrating that significant reductions in forecast risk can be achieved through bias correction techniques.

Future Directions: Enhancing Panel Data Forecasting

The methods presented in this article offer a promising avenue for improving the accuracy and efficiency of panel data forecasting. Several avenues for future research could further enhance the applicability and performance of these techniques. These avenues can lead to higher forecasting standards and practices in the area.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1017/s0266466618000294, Alternate LINK

Title: Asymptotically Efficient Model Selection For Panel Data Forecasting

Subject: Economics and Econometrics

Journal: Econometric Theory

Publisher: Cambridge University Press (CUP)

Authors: Ryan Greenaway-Mcgrevy

Published: 2018-10-30

Everything You Need To Know

1

Why are Panel Data Models preferred for economic forecasting?

Panel Data Models are favored because they integrate both cross-sectional and time-series data. This integration enables the capture of economic system complexities often missed by traditional time-series models. They excel at accounting for cross-sectional heterogeneity, which is the variance between individual units like countries or firms, making them highly applicable for forecasting diverse economic variables.

2

What is the role of Model Selection in Panel Data Forecasting?

Model selection is crucial in Panel Data Forecasting as it directly impacts the accuracy and reliability of predictions. Selecting the optimal model specification is a challenge because, unlike time-series forecasting, there isn't a clear theoretical framework. The new model selection methods presented here aim to minimize forecast risk by balancing model complexity and goodness of fit, ultimately leading to more reliable forecasts.

3

How do the new model selection methods improve Panel Data Forecasting?

The new methods optimize Panel Data Forecasting by using least squares (LS) vector autoregressions and minimizing estimated quadratic forecast risk among candidate models. These methods prioritize model dimensionality to account for parameterized forms of cross-sectional heterogeneity, such as fixed effects, on forecast loss. This approach also extends to bias-corrected least squares, which further reduces forecast risk through bias correction techniques.

4

What is meant by 'asymptotic efficiency' in the context of Panel Data Forecasting?

Asymptotic efficiency, in Panel Data Forecasting, refers to the property of model selection criteria to become increasingly accurate as more data becomes available. Under specific conditions, these criteria can achieve Shibata's (1980) notion of efficiency as both the number of cross-sections (n) and the number of time series observations (T) grow infinitely large. This ensures the model selection process becomes more reliable with a larger dataset.

5

What are the main advantages of using Panel Data Models over traditional time-series models in economic forecasting?

Panel Data Models offer several key advantages. They often produce more accurate forecasts than time-series models across various datasets. Panel models can capture cross-sectional heterogeneity, essential for reflecting real-world complexities by accounting for differences between units such as countries or firms. The new methods also minimize forecast risk, balancing model complexity and goodness of fit for optimal predictions. Furthermore, these methods are designed for asymptotic efficiency, meaning their accuracy improves as more data becomes available over time, leading to higher forecasting standards.

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