A tug-of-war scene symbolizing Local Projections and VARs competing in economic forecasting.

Local Projections vs. VARs: Decoding the Economic Forecasting Face-Off

Mira Elwood in Business & Economy September 2025 • 5 min read.

"Explore the surprising twists in the battle of Local Projections and Vector Autoregressions. Find out when to choose one over the other for superior economic predictions."

For years, economists have relied on sophisticated tools to predict the future of our economies. Among these, Local Projections (LPs) and Vector Autoregressions (VARs) have emerged as leading contenders, each with its own set of strengths and weaknesses. The debate over which method is superior has been ongoing, spurring countless studies and discussions. This article breaks down what recent research reveals about these two powerful tools, offering practical guidance for anyone involved in economic analysis and forecasting.

At their core, both LPs and VARs aim to estimate structural impulse responses, which are crucial for understanding how the economy reacts to different shocks. However, they approach this task from different angles. LPs directly project future outcomes onto current conditions, offering flexibility but potentially sacrificing precision. VARs, on the other hand, extrapolate long-term responses from short-term data, which can lead to smoother forecasts but may introduce bias. Understanding this bias-variance trade-off is key to choosing the right method.

Recent research, leveraging thousands of simulated data scenarios, sheds new light on this debate. By mimicking the complexities of the U.S. macroeconomic landscape, these simulations provide a rigorous testing ground for LPs and VARs, helping to clarify when each method is most effective. Whether you're a seasoned economist or just starting out, this guide will equip you with the knowledge to navigate the world of economic forecasting with confidence.

Local Projections vs. VARs: Unveiling the Core Differences

A tug-of-war scene symbolizing Local Projections and VARs competing in economic forecasting.

Local Projections (LPs) and Vector Autoregressions (VARs) are powerful tools that economists use to understand how the economy reacts to different events, such as changes in interest rates or government spending. Both methods try to capture the 'impulse response,' which shows how key economic variables respond over time to an initial 'shock.' However, they go about this task in very different ways, leading to distinct strengths and weaknesses.

The key difference lies in how they handle the relationship between past and future data. LPs take a direct approach: they use current data to project future outcomes, without making strong assumptions about the underlying economic structure. This flexibility is a major advantage, as it allows LPs to capture complex relationships without imposing rigid models. VARs, conversely, use current and past data to build a system of equations that describes the economy's dynamics. They then use this system to extrapolate how the economy will evolve over time in response to a shock.

Local Projections (LPs): Direct, flexible, but can be less precise.
Vector Autoregressions (VARs): Model-based, smooth forecasts, but risk bias.

Choosing between LPs and VARs often boils down to a trade-off between bias and variance. LPs tend to have lower bias, meaning they are less likely to systematically overestimate or underestimate the true response. However, they can also have higher variance, meaning their forecasts are more sensitive to small changes in the data. VARs, on the other hand, tend to have lower variance but higher bias. In other words, they may provide more stable forecasts, but these forecasts may be systematically off the mark.

The Future of Economic Forecasting: Combining the Best of Both Worlds

As our understanding of LPs and VARs continues to evolve, future research may explore hybrid approaches that combine the strengths of both methods. For example, researchers could use LPs to estimate short-term responses and VARs to extrapolate long-term trends, potentially achieving a more accurate and robust forecast. By carefully considering the bias-variance trade-off and tailoring their approach to the specific characteristics of the data, economists can continue to refine their forecasting tools and provide valuable insights into the ever-changing economic landscape.

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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: https://doi.org/10.48550/arXiv.2104.00655,

Title: Local Projections Vs. Vars: Lessons From Thousands Of Dgps

Subject: econ.em

Authors: Dake Li, Mikkel Plagborg-Møller, Christian K. Wolf

Published: 01-04-2021

Everything You Need To Know

What is the core function that both Local Projections (LPs) and Vector Autoregressions (VARs) attempt to estimate in economic forecasting?

Both Local Projections (LPs) and Vector Autoregressions (VARs) are designed to estimate structural impulse responses. These responses are crucial for understanding how the economy reacts to various shocks, such as changes in interest rates or government spending. The impulse response reveals how key economic variables, like GDP or inflation, behave over time after an initial economic disturbance. By estimating these responses, economists can gain insights into the dynamic effects of different economic policies and events.

What is the primary difference in how Local Projections (LPs) and Vector Autoregressions (VARs) approach economic forecasting?

The main difference lies in their methodologies. Local Projections (LPs) use a direct approach, projecting future outcomes directly onto current conditions. This offers flexibility because it does not impose strong assumptions about the economic structure. Vector Autoregressions (VARs) employ a model-based approach, building a system of equations with current and past data to describe the economy's dynamics. They then extrapolate how the economy evolves over time. The flexibility of LPs contrasts with the structured, often smoother, forecasts of VARs.

Explain the concept of the bias-variance trade-off as it applies to Local Projections (LPs) and Vector Autoregressions (VARs).

The bias-variance trade-off is central to understanding the performance of Local Projections (LPs) and Vector Autoregressions (VARs). LPs typically have lower bias, meaning their forecasts are less likely to systematically overestimate or underestimate the true economic response. However, they can have higher variance, making them more sensitive to data fluctuations. VARs, conversely, tend to exhibit lower variance, leading to more stable forecasts, but they may have higher bias. This means the forecasts could be systematically off the mark due to the model's assumptions. Choosing between the two involves deciding which type of error, bias or variance, is more acceptable given the specific forecasting context.

How do recent research and simulations contribute to the understanding and application of Local Projections (LPs) and Vector Autoregressions (VARs) in economic forecasting?

Recent research has leveraged thousands of simulated data scenarios to shed new light on the effectiveness of Local Projections (LPs) and Vector Autoregressions (VARs). These simulations mimic the complexities of the U.S. macroeconomic landscape, providing a rigorous testing ground for both methods. This helps clarify the situations in which each method is most effective. By analyzing the performance of LPs and VARs under different simulated conditions, economists can better understand their respective strengths and weaknesses and make more informed decisions about which method to use for a particular forecasting task. This research offers practical guidance for economists and those new to the field to navigate economic forecasting with greater confidence.

What are some potential future developments in economic forecasting involving Local Projections (LPs) and Vector Autoregressions (VARs)?

Future research may explore hybrid approaches that combine the strengths of Local Projections (LPs) and Vector Autoregressions (VARs). One possible approach is to use LPs to estimate short-term responses and VARs to extrapolate long-term trends. This would allow economists to leverage the flexibility and low-bias characteristics of LPs for immediate predictions, while using the smoothing capabilities of VARs to project longer-term economic behavior. The goal is to achieve more accurate and robust forecasts. This approach could help in refining forecasting tools and provide valuable insights into the economic landscape.