Balancing complexity in economic forecasts

Navigating Economic Forecasts: Can Simpler Models Outperform Complex Ones?

Theo Raines in Business & Economy August 2025 • 4 min read.

"Discover when smaller, sophisticated economic models can beat larger, simpler ones, and how shrinkage priors and dynamic model selection can improve forecasting accuracy."

In the realm of economic forecasting, a key challenge lies in balancing model size and complexity. Simple models are often favored for their ease of use and interpretability, while sophisticated models aim to capture the intricate dynamics of the economy more accurately. The rise of increasingly sophisticated models comes from a need to avoid functional misspecification. Conversely, more data availability means models can increase in dimensionality, decreasing the likelihood of omitted variable bias.

This article explores the trade-offs between these two approaches, focusing on Bayesian Vector Autoregressions (VARs). We investigate when it pays off to introduce drifting coefficients in these models, allowing for time-varying relationships between economic variables. By comparing the predictive performance of different models across various macroeconomic datasets, we aim to provide insights into the optimal balance between size and complexity.

Our analysis will cover three major economies – the euro area, the United Kingdom, and the United States – and examine how model size and complexity affect forecasting accuracy. We'll also delve into the use of shrinkage priors, which help to mitigate the curse of dimensionality, and dynamic model selection, a technique for combining the strengths of different models at different points in time. Whether you're an economist, a financial analyst, or simply interested in understanding the forces that shape our economy, this article offers valuable insights into the art and science of economic forecasting.

The Size vs. Complexity Trade-Off in Economic Modeling

Balancing complexity in economic forecasts

The choice between model size and model complexity is a critical one. Large models, such as VARs with many endogenous variables, can naturally avoid omitted variable bias. This often translates into superior predictive performance and avoids puzzles commonly observed in empirical macroeconomics. However, large models can also be over-parameterized and computationally intensive.

Conversely, complicated models, particularly time-varying parameter VARs (TVP-VARs), are often difficult to estimate and interpret and do not scale well to large datasets. Yet, they allow for capturing nonlinearities of unknown form, often related to parameter change and structural breaks over longer time periods. These models can control for an omitted variable bias in small-scale models, while larger information sets can substitute for non-linear model dynamics.

Small Data Sets: Sophisticated dynamics through drifting coefficients are important.
Sizeable Data Sets: Simpler models tend to perform better.
Shrinkage Priors: Combine the best of both worlds, helping to mitigate the curse of dimensionality.
Dynamic Model Selection: Improves upon the best-performing individual model for each point in time.

To control for overfitting, two recent shrinkage priors, the Normal-Gamma (NG) prior and the Dirichlet-Laplace (DL) prior, are used to induce shrinkage in our different model specifications. These priors help to prevent models from becoming too complex and fitting the noise in the data rather than the underlying economic relationships.

Balancing Act: Model Selection for Superior Forecasts

The art of economic forecasting lies in finding the right balance between model size and complexity. While sophisticated models with drifting coefficients can excel in data-scarce environments, simpler models often prove more robust when ample data is available. By carefully considering the trade-offs and employing techniques like shrinkage priors and dynamic model selection, forecasters can improve their accuracy and gain a deeper understanding of the economic forces that shape our world. Whether you favor sophisticated TVP-VAR-SV models with DL priors or simpler constant-parameter VAR-SV models, the key is to adapt your approach to the specific context and data at hand.

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: 10.1002/for.3121,

Title: Sophisticated And Small Versus Simple And Sizeable: When Does It Pay Off To Introduce Drifting Coefficients In Bayesian Vars?

Subject: stat.me econ.em stat.ap stat.co

Authors: Martin Feldkircher, Luis Gruber, Florian Huber, Gregor Kastner

Published: 01-11-2017

Everything You Need To Know

What are the core differences between simple and sophisticated models in economic forecasting?

In economic forecasting, simple models are favored for their ease of use and interpretability, often employing techniques like constant-parameter VARs. Sophisticated models, on the other hand, aim to capture the intricate dynamics of the economy more accurately, often involving time-varying parameter VARs (TVP-VARs) and drifting coefficients. While simpler models might struggle with capturing complex non-linear relationships, sophisticated ones can control for them. The selection between the two depends on the specific context and data at hand.

How does data availability influence the choice between model size and complexity in economic forecasting?

Data availability significantly impacts the model selection process. With sizable datasets, simpler models, such as constant-parameter VARs, tend to perform better. They benefit from the abundance of information to estimate parameters effectively. Conversely, in data-scarce environments, sophisticated models with drifting coefficients are more advantageous, as they can still capture time-varying relationships even with limited data points. Sophisticated models can control for an omitted variable bias in small-scale models, while larger information sets can substitute for non-linear model dynamics.

What is the role of shrinkage priors in economic forecasting, and how do they contribute to model accuracy?

Shrinkage priors, such as the Normal-Gamma (NG) prior and the Dirichlet-Laplace (DL) prior, play a crucial role in mitigating the curse of dimensionality in economic forecasting. They prevent models from becoming overly complex and fitting noise in the data rather than the underlying economic relationships. By inducing shrinkage, these priors help to regularize the model, improving its predictive performance and generalizability. They help combine the best features of both worlds.

How does dynamic model selection enhance the accuracy of economic forecasts?

Dynamic model selection enhances forecast accuracy by combining the strengths of different models at different points in time. This technique allows forecasters to adapt their approach based on the evolving economic conditions and the performance of individual models. Dynamic model selection can improve upon the best-performing individual model for each point in time, leading to more robust and accurate forecasts.

Can you explain the practical implications of using Bayesian Vector Autoregressions (VARs) in economic forecasting?

Bayesian Vector Autoregressions (VARs) are central to the forecasting approach. They help investigate when introducing drifting coefficients, allowing for time-varying relationships, is beneficial. The analysis covered three major economies – the euro area, the United Kingdom, and the United States. It examines how model size and complexity affect forecasting accuracy. Whether one favors sophisticated TVP-VAR-SV models with DL priors or simpler constant-parameter VAR-SV models, the key is to adapt your approach to the specific context and data at hand. By comparing the predictive performance of different models across various macroeconomic datasets, valuable insights into the art and science of economic forecasting are produced.