# Econometrics

17 papers with code • 1 benchmarks • 1 datasets

## Most implemented papers

# Multi-Paradigm Analysis of Thai Capital Market Linkages: Bivariate/Vine Copulas, Granger Causality, Network Centrality, and Graph Neural Network/Graph Embedding Approaches

Analytically thorough understanding of causal, probabilistic, and informational linkages amongst modern, highly-interconnected capital markets is fundamental to the promotion of capital-market innovation, efficiency, and resilience; whereupon innovative, efficient, and resilient capital markets are fundamental to the sustainable economic development of any nation and the robust financial stability of her economy.

# Convex Total Least Squares

The special case when all dependent and independent variables have the same level of uncorrelated Gaussian noise, known as ordinary TLS, can be solved by singular value decomposition (SVD).

# Spike-based causal inference for weight alignment

Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem.

# A Locally Adaptive Interpretable Regression

Therefore, LoAIR is a step towards bridging the gap between econometrics, statistics, and machine learning by improving the predictive ability of linear regression without depreciating its interpretability.

# On a computationally-scalable sparse formulation of the multidimensional and non-stationary maximum entropy principle

Data-driven modelling and computational predictions based on maximum entropy principle (MaxEnt-principle) aim at finding as-simple-as-possible - but not simpler then necessary - models that allow to avoid the data overfitting problem.

# Estimating Structural Target Functions using Machine Learning and Influence Functions

Within this framework, we propose two general learning algorithms that build on the idea of nonparametric plug-in bias removal via IFs: the 'IF-learner' which uses pseudo-outcomes motivated by uncentered IFs for regression in large samples and outputs entire target functions without confidence bands, and the 'Group-IF-learner', which outputs only approximations to a function but can give confidence estimates if sufficient information on coarsening mechanisms is available.

# Tail-risk protection: Machine Learning meets modern Econometrics

Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived.

# Estimation and Applications of Quantiles in Deep Binary Classification

We quantify the uncertainty of the class probabilities in terms of prediction intervals, and develop individualized confidence scores that can be used to decide whether a prediction is reliable or not at scoring time.

# Flexible Model Aggregation for Quantile Regression

Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions, or to model a diverse population without being overly reductive.

# Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?

In this paper we have updated the hypothesis testing framework by drawing upon modern computational power and classification models from machine learning.