Evaluating Government Budget Forecasts

Ericsson, Neil R.; Martinez, Andrew B.

doi:10.1007/978-3-030-18195-6_3

Cited by 4 publications

(4 citation statements)

References 66 publications

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“…FAPRI was established by the US Congress, part of the legislative branch. Thus, our work is complimentary to previous studies that evaluate forecasts produced by agencies from different branches of the federal government (for a recent review, see Ericsson & Martinez, 2019).…”

Section: Agricultural Baseline Projectionsmentioning

confidence: 69%

The accuracy and informativeness of agricultural baselines

Bora

Katchova

Kuethe

2022

American J Agri Economics

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Agricultural baselines facilitate policy and investment decisions by governments and market participants by providing long‐term projections about the farm sector. Despite their importance in shaping agricultural policy, the agricultural baselines have not been rigorously evaluated. This study evaluates the accuracy and informativeness of two widely used baselines for the US farm sector published by the United States Department of Agriculture (USDA) and the Food and Agricultural Policy Research Institute (FAPRI) in three steps. First, we examine the average percent errors of the projections and perform tests of bias. Second, we use a testing framework based on the encompassing principle to test the predictive content of the projections for each horizon, determining the longest informative projection horizon. Third, we compare the USDA and FAPRI baseline projections using a multi‐horizon framework that considers all projection horizons jointly. We find that prediction error and bias increase with the horizon's length. The predictive content of the baselines projections for most variables diminishes after 4–5 years. The multi‐horizon comparison suggests that neither USDA nor FAPRI projections have uniform or average superior predictive ability over the other for most variables. Our findings are useful for the agencies producing these baselines and for the policymakers, agricultural businesses, and other stakeholders who use them. The study contributes to the recent literature on long‐term agricultural projections and establishes the groundwork for future research inquiries.

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Section: Agricultural Baseline Projectionsmentioning

confidence: 69%

The accuracy and informativeness of agricultural baselines

Bora

Katchova

Kuethe

2022

American J Agri Economics

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“…As an alternative way of visualizing these comparisons, we provide in Figures A4 and A5 in the Appendix C take-off versions of hedgehog graphs for the VAR-ECM and benchmark VAR models, where "spine" T * represents x T * +i T * ;λ , T * + i ∈ W j , i = 0, 1, 2, 3. See Castle et al (2010) or Ericsson and Martinez (2019) for related images of such graphs and additional details.…”

Section: Resultsmentioning

confidence: 99%

Balanced Growth Approach to Tracking Recessions

Boczon

Richard

2020

Econometrics

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In this paper, we propose a hybrid version of Dynamic Stochastic General Equilibrium models with an emphasis on parameter invariance and tracking performance at times of rapid changes (recessions). We interpret hypothetical balanced growth ratios as moving targets for economic agents that rely upon an Error Correction Mechanism to adjust to changes in target ratios driven by an underlying state Vector AutoRegressive process. Our proposal is illustrated by an application to a pilot Real Business Cycle model for the US economy from 1948 to 2019. An extensive recursive validation exercise over the last 35 years, covering 3 recessions, is used to highlight its parameters invariance, tracking and 1- to 3-step ahead forecasting performance, outperforming those of an unconstrained benchmark Vector AutoRegressive model.

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“…Some are model-based, some use multiple models, and some are subjective judgements made by individuals, using unspecified data sources and algorithms. Recent extensions use machine-learning algorithms [32]. However, irrespective of the platform or approach, the ideologies, methods and contexts of the forecasters can influence economic forecast accuracies substantially [33].…”

Section: Discussionmentioning

confidence: 99%

Can Groups Improve Expert Economic and Financial Forecasts?

2022

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Economic and financial forecasts are important for business planning and government policy but are notoriously challenging. We take advantage of recent advances in individual and group judgement, and a data set of economic and financial forecasts compiled over 25 years, consisting of multiple individual and institutional estimates, to test the claim that nominal groups will make more accurate economic and financial forecast than individuals. We validate the forecasts using the subsequent published (real) outcomes, explore the performance of nominal groups against institutions, identify potential superforecasters and discuss the benefits of implementing structured judgment techniques to improve economic and financial forecasts.

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Evaluating Government Budget Forecasts

Cited by 4 publications

References 66 publications

The accuracy and informativeness of agricultural baselines

The accuracy and informativeness of agricultural baselines

Balanced Growth Approach to Tracking Recessions

Can Groups Improve Expert Economic and Financial Forecasts?

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