Institutt for datavitenskap og analyse

Behavior Research Methods 58(107) Doi: https://doi.org/10.3758/s13428-026-02968-4

Testing goodness-of-fit and multi-group nested models in confirmatory factor analysis under non-normality is foundational in psychometrics and related fields. Recently, a penalized eigenvalue block averaging (pEBA) procedure was proposed for testing goodness-of-fit, showing promise in a restricted type I error control simulation study. In this study, we extend the simulation conditions to higher dimensions for latent and observed vectors and evaluate type I error control and power for many pEBA variants and traditional test statistics. All statistics are evaluated in four versions, by crossing the base statistic (ML or Browne’s RLS), and whether the asymptotic covariance matrix estimator was bias-corrected or not. We develop pEBA methods in the new setting of nested model comparison, accompanied by extensive Monte Carlo evaluation of their performance in weak invariance testing, including type I error control and power. The best-performing procedure for goodness-of-fit testing was pEBA with four blocks, based on the RLS statistic, using the asymptotic covariance matrix estimator without bias correction. For measurement invariance, pEBA with singleton blocks, using the standard ML statistic and the unbiased estimator for the asymptotic covariance matrix, performed best. The pEBA procedures are available in the newly developed R package semTests.

Handelshøyskolen BI

Building on recent advances in Natural Language Processing and modeling of sequences, we study how a multimodal Transformer-based deep learning architecture can be used for measurement and structural narrative attribution in macroeconomics. The framework we propose combines (news) text and (macroeconomic) time series information using cross-attention mechanisms, easily incorporates differences in data frequencies and reporting delays, and can be used together with Reinforcement Learning to produce structurally coherent summaries of high-frequency news flows. Applied and tested on both simulated and real-world data out-of-sample, the results we obtain are encouraging.

Bernoulli 32(1) s. 24-48 Doi: https://doi.org/10.3150/25-bej1902

This article surveys key aspects of ambit stochastics and remembers Ole E. Barndorff-Nielsen’s important contributions to the foundation and advancement of this new research field over the last two decades. It also highlights some of the emerging trends in ambit stochastics.

Journal of Commodity Markets 41 Doi: https://doi.org/10.1016/j.jcomm.2026.100541

This study examines short-to mid-term point forecasting of daily electricity prices, with particular emphasis on the role of renewable energy sources. We use data from the market zone corresponding to the Northern region of Italy, applying both time series and machine learning methodologies. The forecasts are evaluated for two individual years, 2019 and 2024. In 2019, traditional energy variables such as electricity load, natural gas prices, and imports, were the primary drivers of forecast accuracy. During this period, adding renewable energy production data offered negligible benefits, with solar and wind contributing only marginally. By contrast, in 2024, market volatility increased greatly due to geopolitical conflicts and increased renewable energy integration. Under these conditions, while solar and wind still added limited value, hydropower improved forecast accuracy substantially. The results suggest that the role of renewable energy sources in electricity price forecasting is growing. However, their predictive power is influenced by their market share and by their variability and predictability.

Stochastic Processes and their Applications 196 Doi: https://doi.org/10.1016/j.spa.2026.104922

There exist multiple regression applications in engineering, industry and medicine where the outcomes follow an adaptive experimental design in which the next measurement depends on the previous observations, so that the observations are not conditionally independent given the covariates. In this paper we present new asymptotic theory for such processes of dependent regression data. This includes stand-alone results for adaptive covariate processes, which, in particular, enable us to develop new likelihood theory for inference on the regression model parameters. Existing results asserting asymptotic normality of the maximum likelihood estimator require regularity conditions involving the second or third derivatives of the log-likelihood. Here we instead extend the theory of differentiability in quadratic mean (DQM) to the setting of adaptive designs, which requires strictly fewer regularity assumptions than the classical theory. In doing so, we discover a new DQM assumption, which we call summable differentiability in quadratic mean (S-DQM). As applications, we first verify asymptotic normality for two classical adaptive designs, namely the Bruceton 'up-and-down' design and the Robbins–Monro design. Next, we consider a more complicated problem, namely a Markovian version of the Langlie design.