My research develops econometric and decision-theoretic methods for economic environments with complex dependence, structural instability, model uncertainty, and heterogeneous agents. A unifying objective is to study economic systems through the lenses of order, information, and complexity.
On the inferential side, I work on stochastic dominance, spanning, and efficient-set analysis, together with empirical likelihood and indirect inference for non-standard time-series and financial models. These approaches provide robust procedures under partial identification, heavy tails, and non-standard asymptotics.
A complementary strand focuses on prediction and model averaging under multiobjective and distributionally robust criteria. More recently, I study how persistence and predictability emerge from the geometry of interactions—network bottlenecks, curvature, entropy, spectral constraints, and topological structure—viewing them as mechanisms governing the transmission and aggregation of economic information.
Current work extends these ideas in two directions.
First, I introduce meta-learning layers that detect obstructions to dominance relations and reveal behavioral regularities, such as preferences over the positioning of sentiment-type information in texts and markets.
Second, I study hierarchical and higher-order preference structures, providing foundations for multi-level decision systems under limited rationality.
Overall, my goal is to design reliable statistical and decision procedures and to characterize the structural sources of economic complexity.
Martingale limit theory and stable laws
Resampling methods with tail information
Persistent homology for heavy-tailed data
Spanning and efficient sets
Asset pricing in incomplete markets
Bayesian and empirical likelihood inference under partial identification
Non-standard GARCH-type models
Infinite-dimensional moment inequalities
Multiobjective and distributionally robust forecast combination
Links between forecasting performance and complexity
Curvature, bottlenecks, and spectral constraints
Topological methods in financial and economic systems
Machine-learning layers that detect barriers to economic order
Information-theoretic measures of structural complexity
Recursive and multi-level decision systems
Foundations for higher-order expected utility
Large deviations under mixing
Non-stationary EGARCH
Financial bubble detection
High-dimensional covariance shrinkage
Structural breaks in environmental and economic time series