Econometrics
Statistical methods for economic data, causal inference, and forecasting.
econ.EM ยท 119 papersWho Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure
2021 US inflation forecasts failed due to sample composition, not model misspecification; historical data adjustments and experience-based priors improve accuracy.
Generalized Autoregressive Multivariate Models: From Binary to Poisson
This paper introduces a GARCH-type framework for binary time series, demonstrating how their aggregates converge to Poisson autoregressions.
Sandpile Economics: Theory, Identification, and Evidence
Sandpile Economics explains how evolving production networks' geometric fragility leads to disproportionate economic crises, using Forman-Ricci curvature.
Root-$n$ Asymptotically Normal Maximum Score Estimation
This paper presents a method for maximum score estimation that achieves root-$n$ asymptotic normality using strictly concave surrogate score functions.
Is Productivity Advantage of Cities Really Down To Mean and Variance?
This paper validates a key assumption in urban economics, showing city productivity gains stem from agglomeration, not just firm selection.
Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
Causal Diffusion Model (CDM) predicts full counterfactual outcome distributions in longitudinal data, outperforming state-of-the-art methods.
Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach
This paper introduces a Quantile Bayesian VAR (QBVAR) to forecast oil prices across the conditional distribution, improving tail risk assessment.
Emulating Stepped-Wedge Cluster Randomized Trials to Evaluate Health Policies and Interventions
This paper proposes emulating stepped-wedge cluster randomized trials in observational studies to improve the design, reporting, and causal inference of health policy evaluations.
Causal Graphs for Conditional Parallel Trends
ฮ-SWIGs, a new causal graph framework, enable reasoning about Conditional Parallel Trends in Difference-in-Differences designs with time-varying covariates.
A Bayes-Factor-Guided Approach to Post-Double Selection with Bootstrapped Multiple Imputation
This paper introduces a Bayes-factor-guided sequential evidence aggregation method for robust variable selection in bootstrapped and imputed datasets.
Distributional Change in Ordinal Data with Missing Observations: Minimal Mobility and Partial Identification
This paper introduces a framework using optimal transport and partial identification to analyze distributional changes in ordinal data with missing observations.
Latent community paths in VAR-type models via dynamic directed spectral co-clustering
This paper introduces a dynamic network framework using directed spectral co-clustering to uncover latent community paths and directional roles in VAR-type models.
A Diagnostics-First Composite Index for Macro-Financial Resilience to Socioeconomic Challenges: The Gondauri Index with Benchmarking and Scenario Evidence
The Gondauri Index (GI) provides a diagnostics-first composite framework to benchmark macro-financial resilience across economies on a 0-100 scale.
Partial Identification of Policy-Relevant Treatment Effects with Instrumental Variables via Optimal Transport
This paper uses optimal transport to derive sharper bounds for policy-relevant treatment effects, improving identification with instrumental variables.
Shock, Communication, and Yield Curve Repricing: A Two-Step Empirical Framework for Copom Events in Brazil
This paper proposes a two-step framework to analyze how shocks and Copom communication reprice the Brazilian DI yield curve.
Average Marginal Effects in One-Step Partially Linear Instrumental Regressions
This paper introduces a novel RKHS-based method for estimating and inferring average marginal effects in partially linear instrumental regressions.
Knowledge Compounding: An Empirical Economic Analysis of Self-Evolving Knowledge Wikis under the Agentic ROI Framework
This paper introduces 'knowledge compounding' in LLM agents, showing how persistent knowledge layers drastically reduce token costs compared to traditional RAG.
Learning Preferences from Conjoint Data: A Structural Deep Learning Approach
This paper introduces a structural deep learning method for conjoint data to uncover rich preference heterogeneity often missed by traditional approaches.
A Strict Gap Between Relaxed and Partition-Constrained Spectral Compression in a Six-State Lumpable Markov Chain
This paper demonstrates a strict gap in spectral compression, showing relaxed orthonormal frames outperform partition-constrained methods in a six-state Markov chain.
Training Neural Networks Embedded in Dynamic Discrete Choice Models
New UFXP/OUFXP estimators enable training neural networks in dynamic discrete choice models by avoiding large linear systems, improving flexibility.
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