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on Econometrics |
| By: | A. Monta\~n\'es; E. Ruiz |
| Abstract: | Dynamic Factor Models (DFMs) are popular to reduce dimensionality being customary in the empirical analysis of large systems of macroeconomic and/or financial variables. In this context, the common underlying factors and their loadings are often extracted using Principal Components (PC), which are consistent and asymptotically normal under very general conditions. Consequently, inference on the factor loadings, which is crucial for the correct interpretation of the underlying factors, is often based on their asymptotic distribution with the limit covariance matrix of the loadings consistently estimated using HAC estimators. In this paper, we analyse the performance of the finite sample asymptotic approximation when constructing confidence intervals and testing about estimated PC loadings. We show that this approximation is seriously affected when the cross-sectional dimension is not large enough. We propose using HAR inference and a subsampling procedure to correct the MSE of the loadings to take into account the uncertainty associated with the estimation of the covariance matrix and of the factors, respectively. The relevance of the results is illustrated in an empirical analysis of economic convergence among the US states. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.12568 |
| By: | Haokun Lu; Xiaojun Song |
| Abstract: | We propose a nonparametric integrated conditional moment (ICM) test for treatment effect heterogeneity across subpopulations defined by a given covariate subvector. Under unconfoundedness, the null is recast as a conditional moment restriction based on a Neyman-orthogonal score, which reduces the first-order sensitivity of the empirical process to nuisance parameter estimation. The test statistics are constructed as continuous functionals of a marked empirical process. We establish a uniform feasible-to-oracle approximation and derive the asymptotic properties of these test statistics under the null and fixed alternatives. We further show that the test has nontrivial power against local alternatives converging to the null at the $n^{-1/2}$ rate, and develop an easy-to-implement multiplier bootstrap for feasible inference. We also develop extensions to tests of parametric CATE specifications and to settings with endogenous treatment and a binary instrument. Finally, we apply the proposed testing approach to study whether the effect of maternal smoking during pregnancy on infant birth weight varies with maternal age. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.12622 |
| By: | Gonzalez-Casasus, Oriol; Schorfheide, Frank |
| Abstract: | VARs are often estimated with Bayesian techniques to cope with model dimensionality. The posterior means define a class of shrinkage estimators, indexed by hyperparameters that determine the relative weight on maximum likelihood estimates and prior means. In a Bayesian setting, it is natural to choose these hyperparameters by maximizing the marginal data density. However, this is undesirable if the VAR is misspecified. In this paper, we derive asymptotically unbiased estimates of the multi-step forecasting risk and the impulse response estimation risk to determine hyperparameters in settings where the VAR is (potentially) misspecified. The proposed criteria can be used to jointly select the optimal shrinkage hyperparameter, VAR lag length, and to choose among different types of multi-step-ahead predictors; or among IRF estimates based on VARs and local projections. The selection approach is illustrated in a Monte Carlo study and an empirical application. |
| Keywords: | Forecasting; Local projections; Model misspecification; Shrinkage estimation |
| JEL: | C11 C32 C52 C53 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19915 |
| By: | Andrii Babii; Luca Barbaglia; Eric Ghysels; Jonas Striaukas |
| Abstract: | This paper develops the asymptotic theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks. We consider a factor-augmented sparse-group LASSO estimator that combines MIDAS aggregation with latent factors. The estimator can take advantage of the mixed-frequency group structure in the time-series dimension. Theory shows that it can outperform the standard LASSO estimator both for prediction and estimation while allowing for cross-sectional dependence. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.06368 |
| By: | Takahiro Hoshino (Department of Economics, Keio University); Kazuhiko Shinoda (Department of Economics, Nagoya University); Taisuke Otsu (Department of Economics, London School of Economics and Political Science) |
| Abstract: | This paper develops a role-reversed auxiliary-calibration framework for identifying average and conditional treatment effects when treatment selection may depend directly on both potential outcomes. The framework uses two side-specific auxiliary measurements: a baseline-side measurement Q and a response-side measurement S. Their roles are reversed across the two potential-outcome means: Q calibrates treatment selection and S represents the outcome for E[Y1], whereas S calibrates selection and Q represents the outcome for E[Y0]. Unlike proximal causal inference or shadow-variable methods, the proposed approach targets generalized Roy selection on potential outcomes rather than adjustment for a common latent confounder. We establish identification of average, conditional, subgroup, and restricted-time treatment effects without recovering the joint distribution of (Y1, Y0). The resulting calibrated orthogonal moment is twin-pair doubly robust: within each treatment arm, either the selection calibrator or the adjoint outcome representer is sufficient for valid estimation. When both nuisance functions are estimated, first-order bias reduces to the product of their estimation errors, yielding product-rate robustness and supporting cross-fitted inference. Monte Carlo experiments illustrate the transition from accidental strong ignorability to selection on gains, showing that the proposed estimator reproduces the standard AIPW benchmark under the former while remaining accurate under the latter, where latent-confounder and armwise shadow-variable methods fail. The methodology is further illustrated using a full-counterfactual benchmark based on the Beat AML ex vivo drug-response resource and an observational study of ESBL bloodstream infection, in which the estimated treatment effect agrees in direction with randomized-trial evidence. |
| Keywords: | role-reversed auxiliary calibration; selection on gains; generalized Roy model; auxiliary measurements; causal inference; restricted mean survival time |
| JEL: | C26 |
| Date: | 2026–07–05 |
| URL: | https://d.repec.org/n?u=RePEc:keo:dpaper:dp2026-013 |
| By: | Ulrich Hounyo |
| Abstract: | Conventional heteroskedasticity diagnostics ask whether the conditional variance of the regression disturbance varies with covariates. This paper asks a different question: when does that variation matter for inference on the estimand of interest? The paper develops a contrast-specific theory characterizing when covariance perturbations are inferentially relevant. We show that, for any linear contrast $a'\beta$ in a linear regression, the difference between the heteroskedasticity-robust variance and the pooled fixed-design variance is governed by the empirical covariance between conditional error variance and a contrast-specific leverage score. Thus, heteroskedasticity may be present in the model yet first-order irrelevant for a particular coefficient or linear combination. Conversely, modest heteroskedasticity may have a large inferential effect if it is concentrated on observations that are highly informative for the contrast of interest. We characterize the effect exactly through a heteroskedasticity relevance ratio and a standard-error inflation factor, relate the result to pairs and residual bootstrap procedures, and extend the decomposition to general covariance structures, where off-diagonal dependence contributes a separate contrast-specific term. The results provide a unified way to understand why robust, clustered, and bootstrap standard errors can differ across coefficients in the same regression. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.03331 |
| By: | Stanis{\l}aw M. S. Halkiewicz |
| Abstract: | We characterize the asymptotic behavior of conventional variance estimators in linear regression with high-dimensional fixed effects under a drift in which both the proportional fixed-effect dimension $\rho_n = d_{K_n}/n \to \rho \in [0, 1)$ and the residual treatment variance $\tau_n^2 = nQ_{K_n} \to \tau^2 \in (0, \infty]$ are non-degenerate. Three findings emerge. First, under strict exogeneity and conditional homoskedasticity, the Cattaneo--Jansson--Newey-corrected $t$-statistic is asymptotically exact for any $\tau^2 > 0$: there is no Stock--Yogo-style threshold in $\tau^2$. Second, the Eicker--White HC0 estimator is biased downward by a fixed factor $(1-\rho)$, producing over-rejection that grows with saturation. Third, HC3 over-corrects in the opposite direction by a factor $1/(1-\rho)$. The leave-one-out estimator (HC2) removes the first-order leverage distortion and is asymptotically exact under homoskedasticity or design-balanced heteroskedasticity; under general heteroskedasticity with non-uniform leverage, HC2 retains an additional bias of order $\rho|\mu - \omega^2|$ that we characterize. An empirical application to Piotroski F-Score returns in CEE markets illustrates the predicted variance hierarchy in real data. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.05215 |
| By: | Jonas E. Arias; Juan F. Rubio-Ramirez; Daniel F. Waggoner |
| Abstract: | The results of nearly 100 prominent studies in empirical macroeconomics have been called into question by Baumeister and Hamilton (2018). We show that their concern about distributional asymmetry for a typical question of interest under a uniform prior with respect to the Haar measure is actually driven by an unacknowledged sign restriction. We also demonstrate that such a prior induces symmetric prior distributions over individual impulse responses conditional on the reduced-form parameters, or more generally when the prior over the reduced-form covariance matrix rules out correlation among the residuals, as in the typical implementation of the Minnesota prior. Furthermore, we provide a theory for avoiding the pitfalls of Baumeister and Hamilton’s critique. Key to our theory is a proposition establishing that any restriction can be decomposed into three types: scale, label, and economic. We use this theory to develop an algorithm for inference based on the unit modulus normalization that tackles a practical problem commonly faced by users of Bayesian SVAR methods. |
| Keywords: | structural vector autoregressions; unit modulus normalization |
| JEL: | C11 C32 |
| Date: | 2026–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:103578 |
| By: | Marcelo J. Moreira; Mahrad Sharifvaghefi |
| Abstract: | The conditional likelihood ratio (CLR) test is a valuable tool for inference under weak identification, with appealing theoretical properties in both linear and non-linear settings. Its implementation nevertheless requires minimizing a non-convex objective function, a difficulty long recognized even in the linear IV setting. While grid-based methods that provide a practical approximation may perform well in particular designs, such procedures do not guarantee that the resulting test preserves the theoretical properties of the CLR test uniformly across a class of data-generating processes. This paper examines the implementation challenges and their consequences for test size and power. In the linear IV settings, we contrast the grid-based method with the polynomial approach of Moreira, Newey, and Sharifvaghefi(2024), which guarantees global minimization and aligns computation with the theoretical properties of the CLR test. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.04380 |
| By: | Parush Arora (Ashoka University); Rohan Wagle (Ashoka University) |
| Abstract: | In staggered difference-in-differences (DiD) designs, units enter treatment at different calendar times, so the treatment effect is not a single number but a set of Cohort- Average Treatment effects on the Treated (CATTs), one per cohort-time cell. Estimating every CATT as its own parameter, as the standard fully flexible estimator does, is unbiased but inefficient when some of these effects are in fact equal, whereas pooling them all into a single two-way fixed effects (TWFE) coefficient is efficient but biased whenever the heterogeneity is genuine. We frame the choice between these extremes as a partition-selection problem on the cohort-time cells and address it with a Dirichlet Process (DP) mixture prior on the CATTs. The model favors parsimonious groupings without fixing their number, and a collapsed Gibbs sampler delivers point estimates, credible intervals that marginalize the unknown partition, and co-clustering probabilities for every pair of CATTs. With the error variance held fixed, the model’s maximum a posteriori (MAP) partition reduces to an ℓ0-penalized regression, connecting the Bayesian formulation to the homogeneity-pursuit literature. In a calibrated simulation, the model cuts the sampling variance of the cohort-time effects by 26–52% relative to the fully flexible estimator, without the pooled estimator’s bias, provided the distinct effects are separated enough to be recovered, and the posterior delivers near-nominal confidence-interval coverage by averaging over the unknown partition. In two applications the method recovers a precision-improving partial-homogeneity structure in one, where the cohort-time effects are genuinely heterogeneous, and reports that full pooling is adequate in the other, where they are not. |
| Date: | 2026–07–31 |
| URL: | https://d.repec.org/n?u=RePEc:ash:wpaper:166 |
| By: | Neele Balke; Stephane Bonhomme; Thibaut Lamadon |
| Abstract: | Latent-variable models are central to economics but often entail intractable integration. Variational inference (VI), widely used in machine learning, turns this integration into tractable, differentiable optimization by replacing the likelihood with a variational objective. However, guarantees of recovering the true parameters remain limited when the variational family is insufficiently flexible -- a key obstacle to the adoption of VI in economics. We first evaluate VI in models of earnings dynamics and show that the choice of variational posterior is crucial. We then introduce indirect variational inference (IVI), which treats VI as an auxiliary model and corrects the bias induced by the variational approximation. IVI retains much of VI's tractability because it does not require computing the likelihood. We apply these methods to models allowing for nonlinear persistence, non-Gaussian and serially correlated transitory shocks, and latent heterogeneity. Across simulated and empirical applications, flexible variational families combined with IVI deliver reliable estimates. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.15168 |
| By: | Jeziorski, Przemyslaw; Leng, Dingzhe; Seiler, Stephan |
| Abstract: | We study the estimation of causal treatment effects on demand when treatment is randomly assigned but prices adjust in response to treatment. We show that regressions of demand on treatment or on treatment and price lead to biased estimates of the direct treatment effect. The bias in both cases depends on the correlation of price with treatment and points in the same direction. In most cases including an endogenous price control reduces bias but does not remove it. We show how to test whether bias from an endogenous price response arises and how to recover an unbiased treatment effect (holding price constant) using a price instrument. We apply our approach to the estimation of the impact of feature advertising across several product categories using supermarket scanner data and show that the bias when not instrumenting for price can be substantial. |
| Keywords: | Endogeneity |
| JEL: | C26 C31 D12 M31 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20252 |
| By: | Arturas Juodis; George Kapetanios; Vasilis Sarafidis |
| Abstract: | 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. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.13862 |
| By: | Roy Cerqueti; Marco Ventura |
| Abstract: | This paper addresses the problem of running variable manipulation in Regression Discontinuity Designs. Leveraging the observation that manipulation often alters the density balance around the cutoff, we detect these structural imbalances using Benford's Law -a natural statistical regularity widely applied in fraud detection. Our framework serves as a vital precautionary safeguard alongside traditional McCrary-type tests. It eliminates researcher-chosen parameters that can skew outcomes, while delivering a deeper diagnostic breakdown of the density's behavior. Crucially, whereas the classic McCrary test can overlook systemic imbalances due to its rigid symmetric setup, our method separates the data into directional components. This allows researchers to pinpoint the exact origin of a deviation and spot hidden manipulation that standard frameworks fail to capture. To achieve this, we introduce an innovative method for selecting a bandwidth consistent with BL, and construct two distinct, complementary tests using threshold values adapted from Nigrini (2012) that successfully transition the law's application from digits to probabilities. Empirical applications confirm the enhanced protective value of this diagnostic framework. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.13564 |
| By: | Jung Hyub Lee |
| Abstract: | Empirical studies often observe outcomes only for selected units, and treatment may change who is observed. This paper studies prediction in randomized studies with one-sided selection. Standard prediction intervals can fail because treated selected observations are not the same group as selected controls. The paper asks how to predict missing treated outcomes and individual treatment effects for always-observed units. The proposed conformalized Lee procedure uses treated selected observations to train and check any prediction rule, then adjusts the cutoff using the observed treatment-control selection gap. For selected controls, the missing treated-outcome interval is shifted by the observed untreated outcome to produce an individual treatment-effect interval. The method provides reliable coverage without requiring the prediction rule to be correctly specified. The key result shows that the proposed adjustment uses the exact amount of uncertainty implied by the monotone selection logic of Lee [2009]. In simulations, ordinary conformal prediction demonstrates a lower coverage rate under selection-induced distribution shift, while the Lee-adjusted methods achieve the desired coverage rate. The results show that the proposed selection correction method can support reliable counterfactual prediction, while retaining practical implementation with modern prediction tools. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.02898 |
| By: | Takahiro (Department of Economics, Keio University); Kazuhiko Shinoda (Department of Economics, Nagoya University); Taisuke Otsu (Department of Economics, London School of Economics) |
| Abstract: | This paper develops an auxiliary-measurement approach to identifying average treatment effects in generalized Roy environments where treatment choice may depend directly on potential outcomes. Identification is formulated as a primal–dual inverse problem. A latent selection-odds representer anchors a causally correct element in an observed calibration set, which may be nonunique. An adjoint outcome representer certifies that the target mean is invariant over that set, so identification does not require point identification of the calibrating function itself. This separation yields a trichotomy between non-invariance, irregular identification, and regular orthogonal-moment representation, according to the position of the outcome signal in the adjoint range. The same geometry delivers an orthogonal estimating equation and an exact product-bias identity, supporting sieve GMM and cross-fitted estimation. Simulations illustrate regular, weak, and failed range regimes. An application to retirement and cognition in the Health and Retirement Study shows that specifications restricted to observed adjustment and those allowing selection on gains yield materially different estimates, illustrating the framework’s empirical content under maintained calibration and adjoint-representation assumptions. |
| Keywords: | Roy model; selection on gains; average treatment effect; auxiliary measurements; inverse problems; sieve GMM; orthogonal moments |
| JEL: | C14 C21 C26 C36 J24 |
| Date: | 2026–06–10 |
| URL: | https://d.repec.org/n?u=RePEc:keo:dpaper:dp2026-012 |
| By: | Kamil Makie{\l}a |
| Abstract: | The paper investigates Bayesian Model Averaging and Selection (BMA/S) under non-standard stochastic assumptions, focusing on stochastic frontier analysis (SFA). We propose fast, reliable procedures for inference in the normal-exponential stochastic frontier model and examine whether accounting for asymmetric disturbances affects model averaging and/or selection outcomes relative to the conventional Gaussian-error BMA/S. Particular attention is given to moderate-dimensional covariate selection problems typical in SFA applications. We demonstrate that, with appropriate search strategies and parallelization techniques, exhaustive model search can be computationally feasible and, in some cases, more practical than stochastic search alternatives. A Monte Carlo simulation study is used to compare the proposed SF-BMA/S procedure with standard Gaussian-error BMA/S under varying levels of inefficiency-to-noise ratio and signal strength with respect to the data generating process. The results show that accounting for stochastic frontier structures may affect posterior inference and model averaging outcomes, especially in scenarios where efficiency analysis is most sensible. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.14274 |
| By: | Giuseppe Cavaliere; Luca Fanelli; Marco Mazzali |
| Abstract: | We propose a novel econometric methodology for Structural Vector Autoregressions with external instruments (`proxy-SVARs' or `SVAR-IVs') in panel data characterized by strong cross-sectional dependence, dynamic heterogeneity, and limited availability of direct external instruments for the shocks of interest. For each unit, we specify a Factor-Augmented proxy-SVAR (`proxy-FA-SVAR') that incorporates factors summarizing cross-sectional information from the non-policy variables of the system. The effects of the policy shocks are then recovered indirectly by estimating unit-specific policy reaction functions through a Minimum Distance approach. Identification relies on global instruments for the non-policy shocks; that is, proxies common to all units in the panel, internally constructed from a separate SVAR estimated on factors for the policy and non-policy variables. These global instruments can be complemented with local (idiosyncratic) instruments constructed from auxiliary unit-level SVARs. Their joint use renders the proxy-FA-SVARs overidentified and therefore statistically testable. We illustrate the methodology by estimating government spending multipliers for Italian NUTS-2 regions using annual data. The global and local instruments for the regional output shocks are obtained from Blanchard-Perotti-type SVARs. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.13879 |
| By: | Vod Vilfort |
| Abstract: | Researchers often conduct inference on weighted estimands, defined as weighted averages of group-level effects. Example settings include event studies with cohort-level effects and experiments with site-level effects. Under heterogeneous effects, different weighting schemes yield estimands with distinct empirical and policy interpretations, leading to ambiguity and disagreement over the choice of weights. I establish bounds on differences between weighted estimands and confidence bounds on effect heterogeneity, which I use to construct estimators that minimize worst-case bias and confidence intervals that are uniformly valid over classes of weighted estimands. I apply these methods to an event study in Lakdawala, Nakasone, and Kho (2023), which studies the effects of school-based internet access on test scores. I find that results are robust to broad classes of weights. I then apply the methods to Tennessee's Project STAR experiment and find that results are sensitive to small departures from baseline weights. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.07524 |
| By: | Xinxian Chen; Peter Reinhard Hansen; Chen Tong |
| Abstract: | We propose the Split-Session Cluster GARCH model for heavy-tailed multivariate dependence among asset returns decomposed into overnight and intraday components. The model uses convolution-$t$ distributions to allow tail behavior to differ across clusters defined by trading sessions and, within each session, by economic sectors. It also accommodates block-structured conditional correlation matrices, preserving parsimony and scalability in high-dimensional settings. The resulting likelihood remains tractable and yields a score-driven specification for dynamic correlations. We apply the model to U.S. equity returns in six-asset and 100-asset applications. The results reveal pronounced tail heterogeneity between overnight and intraday returns. Model comparisons show that session-specific tail parameters substantially improve fit relative to a common multivariate-$t$ specification, while sector-level tail partitioning delivers additional gains concentrated mainly in the overnight component. In the 100-asset application, asset-level tail heterogeneity delivers the strongest out-of-sample likelihood and global minimum-variance (GMV) portfolio performance. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.03669 |
| By: | Tae-Hwy Lee (Department of Economics, University of California Riverside); Dingli Wang (University of California, Riverside) |
| Abstract: | Risk managers often work with a fitted forecasting model they cannot replace even when its tail forecasts need adjustment. We propose a median-anchored rule that multiplies the distances from the fitted median to Value-at-Risk (VaR) and Expected Shortfall (ES) by a common positive multiplier. The rule preserves VaR--ES ordering, and minimizing VaR check loss gives a closed-form weighted-quantile estimator. We establish consistency, give an asymptotic distribution under high-level conditions accounting for baseline estimation, and derive a VaR coverage-error bound at the forecast origin. When the same multiplier correctly adjusts both VaR and ES, the adjusted pair has lower conditional zero-homogeneous Fissler--Ziegel (FZ0) risk at that origin. Monte Carlo experiments examine this condition and several forms of misspecification. In rolling S&P~500 forecasts the adjustment lowers FZ0 loss in all four GARCH cells, each pairwise significant, with the largest and most robust gain, 10.4%, for Gaussian GARCH at the 1% tail; the 5% gains do not survive the most conservative family-wise adjustments. A quantile-regression baseline marks the boundary: when the fitted tail is already flexible, one multiplier adds little. Filtered historical simulation quantifies what standardized residuals and conditional scales add when available. |
| Keywords: | Backtesting; Elicitability; Fissler--Ziegel Score; Forecast Evaluation; Model Risk; Quantile Regression |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202604 |
| By: | Gong, Xiaodong (University of Canberra); Freyens, Benoit (University of Canberra) |
| Abstract: | We propose a new approach to identifying peer effects that addresses the reflection problem by using the timing of decisions. The approach combines panel data with the ordering of decisions to separate peer influence from simultaneity. Variation in the order of decisions and in exposure to peers provides the basis for identification, allowing estimation of peer effects in settings where standard approaches are not informative. We illustrate the method using a multi-level panel of more than 2, 000 judicial rulings over a 16-year period, where cases are randomly assigned to judges. The data allow us to implement the approach and examine the sources of peer influence. We find heterogeneous and asymmetric responses, with both the magnitude and direction of effects differing across types. |
| Keywords: | peer effects, reflection problem, judicial decisions |
| JEL: | C18 C23 C81 D85 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18808 |
| By: | Hauzenberger, Niko; Marcellino, Massimiliano; Pfarrhofer, Michael; Stelzer, Anna |
| Abstract: | We develop Bayesian machine learning methods for mixed data sampling (MIDAS) regressions. This involves handling frequency mismatches and specifying functional relationships between many predictors and the dependent variable. We use Gaussian processes (GPs) and compress the input space with structured and unstructured MI-DAS variants. This yields several versions of GP-MIDAS with distinct properties and implications, which we evaluate in short-horizon now- and forecasting exercises with both simulated data and data on quarterly US output growth and inflation in the GDP deflator. Our proposed framework leverages macroeconomic Big Data in a computationally efficient way and offers gains in predictive accuracy along several dimensions. |
| JEL: | C11 C22 C53 E31 E37 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19965 |
| By: | Daniele Angelini |
| Abstract: | Testing self-similarity in fractional processes from a single observed trajectory is difficult under long-range dependence, because the associated Kolmogorov--Smirnov (KS) statistic undergoes a phase transition when $H>1/2$. In this regime, the classical limit collapses to a non-functional absolute Gaussian law and finite-sample convergence becomes severely distorted. This paper introduces a regime-adaptive KS/GL--KS framework based on the discrete Gr\"{u}nwald--Letnikov (GL) fractional derivative. The GL filter removes the low-frequency long-memory singularity while preserving the finite-dimensional $H$-self-similarity needed for distributional identification. We derive the filtered empirical-process limit, prove consistency and local asymptotic behavior of the resulting Hurst estimator, and validate the method through Monte Carlo simulations. Financial applications to realized volatility and equity index prices show how the procedure detects rough volatility and persistent, anti-persistent, or efficient market states. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.27932 |
| By: | O’Neill, Eoghan; Velasco, Sofia |
| Abstract: | The question of how oil supply news shocks transmit to real activity, financial conditions, and regional labor markets is back at the center of the macroeconomic research agenda. To answer this question, we introduce the Factor Bayesian Additive Regression Tree (FABART) model, a nonlinear factor-augmented vector autoregression model, and apply it to a large U.S. macro-financial dataset with externally identified oil supply news shocks. The framework combines a large macro-financial information set with a flexible nonparametric measurement equation, allowing nonlinear transmission to emerge from the data rather than being imposed through a pre-specified functional form. We find that adverse oil supply news shocks generate stronger and more persistent contractions in real activity than the expansions associated with favorable shocks of comparable magnitude, with especially pronounced differences in industrial production, financial variables, and equity prices. Employment responses are highly heterogeneous across U.S. states, with substantially stronger contractions in manufacturing-intensive regions, while energy-producing states display partially offsetting dynamics following adverse oil supply news shocks. Across shock magnitudes, nonlinearities arise mainly between very small and moderate oil-price movements: small shocks generate weak and imprecisely estimated responses, while moderate shocks already produce economically meaningful effects on industrial production and regional employment. Larger shocks do not systematically generateproportionally stronger responses across variables and shock signs. JEL Classification: C11, C32, E32, Q43 |
| Keywords: | dynamic factor model, non-parametric techniques, nonlinear models, oil price shocks |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263265 |