Andrey L. Vasnev

When several studies or other inputs estimate the same quantity, additional information need not increase precision once the inputs reveal heterogeneity that was not assumed in advance. We study this point in a Gaussian random‐effects model estimated by maximum likelihood. The framework separates within‐input noise from across‐input heterogeneity and, as a consequence, separates two uncertainty measures: the precision with which the common location is estimated and the variability of a new input. We show explicitly how conflicting inputs affect both measures, first in a transparent two‐input example and then with many inputs; we also consider relative input precisions and dependence across inputs. Prediction intervals themselves are not new, but their distinction from confidence intervals is central to the interpretation of the model. Applications to heterogeneous clinical trials and to a controlled experiment in finance show that ignoring either reported standard errors or dependence can materially change the assessment of uncertainty.

This paper examines the 2016-2023 literature on authentic assessment in higher education, building upon and validating Villarroel et al.'s (2018) framework. Our review reveals significant variations in how authors conceptualise authentic assessment across different contexts, employing diverse definitions, approaches, and frameworks. This heterogeneity challenges educators in identifying effective authentic assessment practices. We present an updated framework that synthesises findings from the past seven years and extends Villarroel et al.'s work. The study offers practical guidance for reconciling disciplinary differences to achieve a shared understanding of authentic assessment. The enhanced framework provides researchers and practitioners with common terminology, shared principles, and standardised codes for analysing and implementing authentic assessment. Additionally, it includes a practical checklist to support educators in redesigning assessments to be more authentic.

This paper proposes Random Factor Ensembles (RFE) for forecasting macroeconomic aggregates from high-dimensional predictor panels. Unlike global principal component (PC) regression, which suffers collinearity distortions under redundancy and weak cross-loadings (Ng & Boivin 2006), RFE applies principal component analysis locally within randomly drawn predictor subsets before averaging across ensemble members. This structured projection reduces the noise propagation inherent in raw random-subspace (RS) regression (Boot & Nibbering 2019) while preserving subspace diversity. Under factor-rich data-generating processes we establish that RFE attains strictly lower asymptotic mean squared forecast error (MSFE) than both global PC and RS methods. Monte Carlo evidence confirms robust bias-variance improvements across sparse and dense specifications. Applied to U.S. industrial production growth using 130 monthly FRED-MD predictors, RFE delivers statistically significant forecast error reductions relative to PC and RS benchmarks, implicitly up-weighting real-activity predictors at short horizons and inflation and credit-spread series at longer horizons without researcher pre-specification.

This paper proposes corrected forecast combinations when the original combined forecast errors are serially dependent. Motivated by the classic Bates and Granger (1969) example, we show that combined forecast errors can be strongly autocorrelated and that a simple correction--adding a fraction of the previous combined error to the next-period combined forecast--can deliver sizable improvements in forecast accuracy, often exceeding the original gains from combining. We formalize the approach within the conditional risk framework of Gibbs and Vasnev (2024), in which the combined error decomposes into a predictable component (measurable at the forecast origin) and an innovation. We then link this correction to efficient estimation of combination weights under time-series dependence via GLS, allowing joint estimation of weights and an error-covariance structure. Using the U.S. Survey of Professional Forecasters for major macroeconomic indices across various subsamples (including pre and post-2000, GFC, and COVID), we find that a parsimonious correction of the mean forecast with a coefficient around 0.5 is a robust starting point and often yields material improvements in forecast accuracy. For optimal-weight forecasts, the correction substantially mitigates the forecast combination puzzle by turning poorly performing out-of-sample optimal-weight combinations into competitive forecasts.

In Bayesian theory, the data together with the prior produce a posterior. We show that it is also possible to follow the opposite route, that is, to use data and posterior information (both of which are observable) to reveal the prior (which is not observable). We then apply the theory to equilibrium climate sensitivity as reported by the Intergovernmental Panel on Climate Change (IPCC) in an attempt to get some insights into the prior beliefs of the IPCC scientists.

Forecast combination -- the aggregation of individual forecasts from multiple experts or models -- is a proven approach to economic forecasting. To date, research on economic forecasting has concentrated on local combination methods, which handle separate but related forecasting tasks in isolation. Yet, it has been known for over two decades in the machine learning community that global methods, which exploit task-relatedness, can improve on local methods that ignore it. Motivated by the possibility for improvement, this paper introduces a framework for globally combining forecasts while being flexible to the level of task-relatedness. Through our framework, we develop global versions of several existing forecast combinations. To evaluate the efficacy of these new global forecast combinations, we conduct extensive comparisons using synthetic and real data. Our real data comparisons, which involve forecasts of core economic indicators in the Eurozone, provide empirical evidence that the accuracy of global combinations of economic forecasts can surpass local combinations.