Sunday, February 1, 2026
From a paper by Panagiotis Delis, and Georgios Kontogeorgos:
“Evaluating macroeconomic forecasts for their unbiasedness and efficiency is essential for policymakers, economists, and investors. The degree to which these stakeholders incorporate expectations into their decision-making processes depends heavily on how these forecasts have been formed. Existing methodologies do not explicitly address critical dimensions, such as the variability of bias across target events and forecast horizons, the forecast errors’ heteroscedasticity, and the potential state-dependence in bias. More importantly, they encounter difficulties during high-uncertainty periods, which can lead to inaccurate inference due to the presence of outliers. Apart from generalising the unbiasedness tests, this study contributes to the literature on both strong and weak efficiency by incorporating these aspects. Finally, the proposed methods are applied to the expectations of a crucial survey of the US economy, namely, the Survey of Primary Dealers (SPD). The findings from this application indicated that interested parties should investigate unbiasedness and efficiency in an outlier-robust way, while also allowing for greater flexibility in the methods regarding the variables and periods examined.”
From a paper by Panagiotis Delis, and Georgios Kontogeorgos:
“Evaluating macroeconomic forecasts for their unbiasedness and efficiency is essential for policymakers, economists, and investors. The degree to which these stakeholders incorporate expectations into their decision-making processes depends heavily on how these forecasts have been formed. Existing methodologies do not explicitly address critical dimensions, such as the variability of bias across target events and forecast horizons, the forecast errors’ heteroscedasticity, and the potential state-dependence in bias.
Posted by at 1:30 PM
Labels: Forecasting Forum
From a paper by Antoine Gaudin, Brendan Harnoys-Vannier, and Martin Kessler:
“In the context of the ongoing review of the Debt Sustainability Analysis (DSA) for Low-Income
Countries (LICs), this paper seeks to help shed light on IMF and World Bank macroeconomic
projections. DSAs are central to the financial architecture of developing countries. Yet, the ways the
projections are performed are rarely accessible to outside researchers.
The first contribution of this paper is to provide a newly constructed database of 605 DSAs
conducted from 2013 to 2024. It contains all the information of all published DSAs for LICs in Tables 1
(macro-economic and fiscal) and 2 (external debt dynamics), as well as the shock scenarios. It will be
updated regularly.
The second contribution of the paper is to analyze forecast errors concerning public and external
debt, as well as the main macroeconomic components. It highlights results on large optimistic biases,
with a 10 percentage point underestimation of the trajectory of the debt-to-GDP ratio on average after
5 years. Decomposing this result, it finds that:
From a paper by Antoine Gaudin, Brendan Harnoys-Vannier, and Martin Kessler:
“In the context of the ongoing review of the Debt Sustainability Analysis (DSA) for Low-Income
Countries (LICs), this paper seeks to help shed light on IMF and World Bank macroeconomic
projections. DSAs are central to the financial architecture of developing countries. Yet, the ways the
projections are performed are rarely accessible to outside researchers.
The first contribution of this paper is to provide a newly constructed database of 605 DSAs
conducted from 2013 to 2024.
Posted by at 1:28 PM
Labels: Forecasting Forum
From a paper by Óscar Peláez-Herreros:
“We develop the first disaggregation of Okun’s law that quantifies all of the information that is subsumed within its coefficients. The proposed method decomposes the coefficients into the sum of the direct effect of the change in output upon the unemployment rate, plus the indirect effects of the variations in the output per hour worked, the hours worked per employed person, the participation rate, and the size of the working-age population. With quarterly data for the United States from 1948 to 2024, we found that the value of the intercept in Okun’s relation is determined by the increases in working-age population and output per hour of work, along with the decrease in the number of hours worked per employed person, plus the growth of the participation rate until the 1990s and its subsequent decline. For its part, the slope, that is, the value of Okun’s coefficient, depends mainly upon the variations in output per hour of work and the hours per employed person. The other factors were scarcely relevant. Changes in these components caused the Okun’s relation to vary over time, showing a greater sensitivity of the unemployment rate to variations in production since the 2008 crisis.”
From a paper by Óscar Peláez-Herreros:
“We develop the first disaggregation of Okun’s law that quantifies all of the information that is subsumed within its coefficients. The proposed method decomposes the coefficients into the sum of the direct effect of the change in output upon the unemployment rate, plus the indirect effects of the variations in the output per hour worked, the hours worked per employed person, the participation rate, and the size of the working-age population.
Posted by at 1:20 PM
Labels: Inclusive Growth
Saturday, January 31, 2026
From a paper by Neeraj Nautiyal, Mobeen Ur Rehman, Rami Zeitun, and Xuan Vinh Vo:
“We investigate how socially responsible investment (SRI) funds respond to different oil-induced price shocks, using Ready’s (2018) approach. Using daily data for six SRI indices from March 8, 2016, to November 29, 2024, we apply wavelet coherence and nonlinear causality methods to analyze the time-frequency relationship between oil shocks and SRI fund performance across different market states. Our findings reveal that supply and risk shocks play a significant role in driving the co-movement between oil price dynamics and SRI funds’ behavior returns, particularly at medium and lower frequencies, respectively. Risk shocks exhibit a systemic influence, consistently dominating supply and demand shocks, especially in the pre-2021 period and during the COVID-19 pandemic, though their effects fizzle out in stable market conditions. Quantile causality estimates confirm the strong predictive power of risk shocks, particularly at lower quantiles. Our work presents practical implications for ethical investors, dealing with oil-related market risks.”
From a paper by Neeraj Nautiyal, Mobeen Ur Rehman, Rami Zeitun, and Xuan Vinh Vo:
“We investigate how socially responsible investment (SRI) funds respond to different oil-induced price shocks, using Ready’s (2018) approach. Using daily data for six SRI indices from March 8, 2016, to November 29, 2024, we apply wavelet coherence and nonlinear causality methods to analyze the time-frequency relationship between oil shocks and SRI fund performance across different market states.
Posted by at 12:22 PM
Labels: Energy & Climate Change
From a paper by Cars Hommes, and Sebastian Poledna:
“This study investigates the potential of agent-based modelling to forecast economic crises, addressing the failure of standard macroeconomic models to predict the 2008 financial crisis and capture crisis dynamics. While dynamic stochastic general equilibrium models have incorporated financial frictions, solving them typically requires linearisation around steady states, which suppresses the non-linear feedback loops through which crises emerge. Agent-based models avoid this limitation by numerically simulating heterogeneous agents, preserving non-linear dynamics without approximation. We develop such an agent-based model for the euro area and show that out-of-sample forecasts outperform benchmarks. We further demonstrate that the model can forecast economic crises without exogenous shocks and accurately reproduce crisis dynamics. The model endogenously predicts the onset of the Great Recession, explains the persistence of the sovereign debt crisis, and reproduces the sharp contraction and swift recovery of the COVID-19 recession. The findings suggest that preserving non-linear feedback loops is essential for crisis prediction.”
From a paper by Cars Hommes, and Sebastian Poledna:
“This study investigates the potential of agent-based modelling to forecast economic crises, addressing the failure of standard macroeconomic models to predict the 2008 financial crisis and capture crisis dynamics. While dynamic stochastic general equilibrium models have incorporated financial frictions, solving them typically requires linearisation around steady states, which suppresses the non-linear feedback loops through which crises emerge. Agent-based models avoid this limitation by numerically simulating heterogeneous agents,
Posted by at 12:21 PM
Labels: Forecasting Forum
Subscribe to: Posts