Department of Data Science and Analytics

Oxford Bulletin of Economics and Statistics Doi: https://doi.org/10.1111/obes.70137

arxiv.org

Warped Gaussian processes (GPs) handle non-Gaussian observations by mapping them into a latent standard GP via a parametric transformation called warping. Existing online variants either optimize the warping parameters periodically rather than sequentially, or sacrifice analytical tractability and computational efficiency for higher model expressivity. In this paper, we avoid these compromises. To achieve this, we first show that the gradient of the instantaneous negative log-likelihood of a warped GP admits an exact recursive computation. Based on this result, we propose a novel online method for warped GPs that jointly updates the latent GP moments and optimizes the warping parameters. Finally, we validate our method on an existing benchmark adapted to an online formulation.

Handelshøyskolen BI

Central banks publish projection paths, but these need not be optimal summaries of the information used to form them. We reframe conditional macroeconomic forecasting as an input problem: a fixed, pre-trained multivariate foundation time-series model reads the institution’s historical predictions and announced future paths alongside target histories, rather than imposing the path as a model-consistent restriction inside a locally estimated system. The mapping nests the Mincer–Zarnowitz and Granger–Ramanathan regressions as its parametric-linear special case. Reading just Norges Bank’s published path triple and target histories, the map cuts mean squared error against the Bank on inflation across horizons, and the nested combination regression puts the conditional weight on the map, not the path. A hard-conditioned VAR matched on the same future paths, but blind to the prediction record, is consistently outperformed on the rate and inflation, and the result survives the asymmetric-loss specifications that best rationalise the path. The VAR contrast replicates on Sweden and New Zealand; the institutional comparison only on New Zealand, with parity at best on Sweden. An institutional-learning regression rationalises the split: the Riksbank absorbs its recent misses more aggressively than Norges Bank and the RBNZ, leaving less residual signal to extract.

arXiv Doi: https://doi.org/10.48550/ARXIV.2606.22964

McKean-Vlasov-type stochastic differential equations (SDEs) are characterized by coefficients depending on both the state and the law of the solution. In this work, we focus on a class of such equations where the coefficients depend on a linear combination of the expected signature of the geometric p-rough path lift of its solution, with $p\in(2,3)$. After establishing the strong existence and uniqueness of a solution, we prove how such an equation can approximate a general class of path-dependent McKean-Vlasov SDEs. Finally, we consider the associated particle system and propagation of chaos is established.

International Conference on Machine Learning (ICML)