Department of Economics

Handelshøyskolen BI

We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5% relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses. The results show that modeling the conditional distribution yields substantial gains for tail risk assessment, particularly during major oil market disruptions.

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.

Energy Economics 157 Doi: https://doi.org/10.1016/j.eneco.2026.109223

Energy Economics 156 Doi: https://doi.org/10.1016/j.eneco.2026.109208

Management science Doi: https://doi.org/10.1287/mnsc.2024.08149

We use a unique data set of individual housing transaction histories from Norway and a novel shift-share design to show that temporary changes in the transaction sequencing decisions of moving homeowners—whether to buy first and then sell or vice versa—cause temporary local increases in house prices and seller liquidity. Our findings are consistent with a simple theory of how transaction sequencing decisions impact house prices in a frictional housing market. We also provide the first causal estimate of the elasticity of house prices to market tightness, which is around 0.1. Overall, our findings highlight the importance of trading frictions and supply-demand imbalances for housing market dynamics. This paper was accepted by Bo Becker, finance. Funding: This work was supported by Norges Forskningsråd [Grant 274678: Frictions in the Housing Market]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08149 .