arXiv subjects

Vasilis Sarafidis

Publications and source records attributed to Vasilis Sarafidis.

10 recordsLinked to original sources

arXiv2026

Model Selection in High-Dimensional Linear Regression using Boosting with Multiple Testing

This paper studies model selection in high dimensional regression settings. We propose a Boosting with Multiple Testing (BMT) approach designed to address proxy signal contamination in environments with strongly correlated regressors, including settings driven by latent common factors. At each stage, at most one regressor is selected conditional on those already included, using a multiple testing threshold for the remaining candidates. Under stated dependence, detectability and design conditions, BMT recovers the exact true model with probability approaching one. A separate extension permits growing signal counts under additional restrictions. For fixed numbers of signals and proxies, we also establish recovery of an approximating model containing all true signals and no noise variables under a separate set of conditions. Following exact selection, fixed dimensional least squares has the oracle asymptotic distribution under the stated moment conditions. A theoretical comparison identifies designs and tuning sequences for which BMT is consistent while the Lasso is not. In the main Monte Carlo comparison, BMT has the highest median Matthews correlation coefficient and exact recovery rate and the lowest median coefficient root mean squared error among the compared specifications. Applications to U.S. emissions and inflation show that BMT combines sparse specifications with favourable forecast performance, including the lowest emissions forecasting errors in the reported comparisons and the best one quarter ahead forecasts in the baseline inflation comparison.

econ.EM↗

arXiv2026

Leverage Dynamics in Product-Market Networks

We examine whether corporate leverage persistence reflects only firms' own adjustment frictions or also the past financing choices of economically connected firms. We combine annual firm fundamentals with time-varying text-based product-market networks and estimate a heterogeneous dynamic partial-adjustment model with interactive effects using defactored instrumental variables. The estimator accommodates time- varying network weights and spatial dependence in firm-specific coefficients. Network-weighted lagged leverage is positive and statistically significant, with a mean coefficient representing approximately 45% of the sum of the own and network lag coefficients for book leverage. Accounting for network dependence reduces the persistence attributed to firms' own financing histories and increases the implied own-firm speed of adjustment. Firm-specific estimates reveal significant product-market clustering in own-leverage persistence and in responses to several leverage determinants, showing that heterogeneity in financing behaviour is systematically related to product-market proximity. The evolution of product-market connections matters quantitatively for network dependence: holding the network fixed produces a substantially larger network coefficient and considerably larger indirect effects than using annual network weights. Network dependence is therefore an economically important component of leverage dynamics, while its measurement depends on both the specification of economic connections and heterogeneity in firm behaviour.

econ.GN↗

arXiv2026

The Anatomy of Commodity Risk: Micro, Market, and Economy-Wide Sources

We study the anatomy of commodity risk by distinguishing micro, market-level, and economy-wide sources. We develop a two-stage "divide-and-conquer" framework that allows sensitivities to these risk sources to vary across commodities while treating economy-wide risk as latent. The first stage uses defactored instrumental-variable estimation to recover commodity-specific sensitivities to micro and market conditions. The second combines principal components with high-dimensional variable selection to identify an observable representation of macro-financial risk. We then construct Risk Intensity Indices (RIIs), which combine estimated sensitivities with prevailing risk conditions to quantify the relative importance of each risk source on a common scale. Market risk is the largest component on average, accounting for about two fifths of total risk intensity and more than half for energy commodities. Risk intensity is also highly concentrated across individual commodities: the top 20% account for approximately half of micro and market risk intensity, whereas macro risk is more broadly dispersed. The composition of risk varies substantially across sectors and over time, with market risk becoming particularly prominent during episodes of commodity-market stress. Micro and market RIIs also contain information about future volatility and absolute returns. These findings provide investors, risk managers, and policymakers with a diagnostic of where commodity risk is concentrated, which risk layers are most important, and how their importance changes over time. More broadly, our divide-and-conquer framework provides a flexible approach to decomposing layered risk in settings where common risk is latent.

econ.EM↗

arXiv2026

IV Estimation of Heterogeneous Spatial Dynamic Panel Models with Interactive Effects

This paper develops a Mean Group Instrumental Variables (MGIV) approach for spatial dynamic panel data models with interactive effects, under large N and T asymptotics. Unlike existing approaches that typically impose slope-parameter homogeneity, MGIV accommodates cross-sectional heterogeneity in slope coefficients. The proposed estimator is linear, making it computationally efficient and robust. Furthermore, it enables asymptotically valid inferences without requiring bias correction. The Monte Carlo experiments indicate strong finite-sample performance of the MGIV approach across various sample sizes and parameter configurations. The practical utility of the methodology is illustrated through an application to regional economic growth in Europe. Our results provide evidence of conditional convergence in regional growth dynamics. Spillovers are shown to play a dominant role, particularly for investment rates where nearly four-fifths of the total effect on GDP per capita growth originates from neighboring regions.

econ.EM↗

arXiv2026

Nonlinear Boosting with Multiple Testing in High-Dimensional Generalised Linear Models with Binary Responses

This paper proposes a nonlinear boosting with multiple testing (BMT) approach to variable selection in high-dimensional generalised linear models with binary responses. At each stage of the BMT procedure, the model is updated by adding only the most significant covariate, conditional on those already selected in previous stages, while taking into account the multiple testing nature of the problem. It is shown that, under the stated conditions, the BMT procedure selects all covariates whose true coefficients are nonzero, and no other covariates, with probability tending to one. Furthermore, the procedure enjoys an oracle property, in the sense that the post-BMT maximum likelihood estimator of the parameters of the model is asymptotically equivalent to an oracle estimator that knows the correct sparse model in advance. Monte Carlo experiments demonstrate that BMT outperforms competing methods, delivering high covariate-selection accuracy and low parameter estimation error. An empirical example illustrates that BMT delivers a predictive model for the probability that U.S. inflation exceeds a given threshold over a 12-month horizon which has very good out-of-sample performance.

econ.EM↗

arXiv2026

Estimation and Inference for Latent Dual Networks Using High-Dimensional IV Screening

We develop a novel methodology for estimation and inference in high-dimensional panel network models with latent dual structures. The framework allows outcomes to be affected simultaneously by positive and negative interaction channels, accommodating settings in which some interactions reinforce outcomes while others generate competition and displacement effects. The proposed method identifies and estimates the network directly from the structural model using observed data without the need to pre-specify the network. Network recovery is achieved through a sequential instrumental-variable screening procedure. We establish exact support recovery and oracle-equivalent post-selection inference. An application to U.S. corporate leverage data reveals the coexistence of reinforcing and displacement interactions in firms' financial decisions.

econ.EM↗

arXiv2026

Network Effects in Corporate Emissions: Evidence from a Data-Dependent Spatial Panel Model

We study spillover effects in corporate toxic emissions using a heterogeneous panel network of U.S. industrial facilities from 2000-2023. Rather than imposing a network structure a priori, we uncover an unobserved web of influence directly from the data using recent advances in high-dimensional network econometrics. Indirect effects transmitted through the estimated network account for about 28% of the total impact of key firm balance-sheet characteristics. By contrast, distance-based networks generate no statistically discernible spillovers, while a priori firm- or industry-based networks substantially overstate within-group spillins relative to the data-driven network. These findings show that who is linked to whom, and with what strength, matters critically for assessing systemic environmental risk and for designing targeted regulation. Methodologically, the paper provides a flexible framework for quantifying facility-level emissions spillovers and their consequences in financial and policy settings.

econ.GN↗

arXiv2026

Chasing Opportunity: Spillovers and Drivers of U.S. State Population Growth

We study the drivers and spatial diffusion of U.S. state population growth using a dynamic spatial model for 49 states, 1965-2017. Methodologically, we recover the spatial network structure from the data, rather than imposing it a priori via contiguity or distance, and combine this with an IV estimator that permits heterogeneous slopes and interactive fixed effects. This unified design delivers consistent estimation and inference in a flexible spatial panel model with endogenous regressors, a data-inferred network structure, and pervasive cross-state dependence. To our knowledge, it is the first estimation framework in spatial econometrics to combine all three elements within a single setting. Empirically, population growth exhibits broad yet heterogeneous conditional convergence: about three-quarters of states converge, while a small high-growth group mildly diverges. Effects of the core drivers, amenities, labour income, migration frictions, are stable across various network specifications. On the other hand, the productivity effect emerges only when the network is estimated from the data. Spatial spillovers are sizable, with indirect effects roughly one-third of total impacts, and diffusion extending beyond contiguous neighbours.

econ.EM↗

arXiv2025

Heterogeneous Exposures to Systematic and Idiosyncratic Risk across Crypto Assets: A Divide-and-Conquer Approach

This paper analyzes realized return behavior across a broad set of crypto assets by estimating heterogeneous exposures to idiosyncratic and systematic risk. A key challenge arises from the latent nature of broader economy-wide risk sources: macro-financial proxies are unavailable at high-frequencies, while the abundance of low-frequency candidates offers limited guidance on empirical relevance. To address this, we develop a two-stage ``divide-and-conquer'' approach. The first stage estimates exposures to high-frequency idiosyncratic and market risk only, using asset-level IV regressions. The second stage identifies latent economy-wide factors by extracting the leading principal component from the model residuals and mapping it to lower-frequency macro-financial uncertainty and sentiment-based indicators via high-dimensional variable selection. Structured patterns of heterogeneity in exposures are uncovered using Mean Group estimators across asset categories. The method is applied to a broad sample of crypto assets, covering more than 80% of total market capitalization. We document short-term mean reversion and significant average exposures to idiosyncratic volatility and illiquidity. Green and DeFi assets are, on average, more exposed to market-level and economy-wide risk than their non-Green and non-DeFi counterparts. By contrast, stablecoins are less exposed to idiosyncratic, market-level, and economy-wide risk factors relative to non-stablecoins. At a conceptual level, our study develops a coherent framework for isolating distinct layers of risk in crypto markets. Empirically, it sheds light on how return sensitivities vary across digital asset categories -- insights that are important for both portfolio design and regulatory oversight.

econ.EM↗

arXiv2025

Residual Income Valuation and Stock Returns: Evidence from a Value-to-Price Investment Strategy

We hypothesize that portfolio sorts based on the V/P ratio generate excess returns and consist of companies that are undervalued for prolonged periods. Results, for the US market show that high V/P portfolios outperform low V/P portfolios across horizons extending from one to three years. The V/P ratio is positively correlated to future stock returns after controlling for firm characteristics, which are well known risk proxies. Findings also indicate that profitability and investment add explanatory power to the Fama and French three factor model and for stocks with V/P ratio close to 1. However, these factors cannot explain all variation in excess returns especially for years two and three and for stocks with high V/P ratio. Finally, portfolios with the highest V/P stocks select companies that are significantly mispriced relative to their equity (investment) and profitability growth persistence in the future.

econ.EM↗