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on Econometrics |
| By: | Aditya Ghosh; Guido Imbens; Stefan Wager |
| Abstract: | Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that the widely used approaches to building confidence intervals in regression discontinuity designs often exhibit suboptimal behavior in practice. We propose a new estimator, the partially linear regression discontinuity (PLRD) estimator that, in set of a simulation studies carefully calibrated to twelve high-profile applications of regression discontinuity designs, has substantially lower estimation error than available comparison methods. Throughout our experiments, the confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD. Our simulation study serves as a general template for how new econometric methods can be credibly evaluated relative to the existing alternatives by constructing simulation designs that generate synthetic data indistinguishable from the original data using the Wasserstein generative adversarial network methodology. |
| JEL: | C01 C14 C20 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35699 |
| By: | Kaicheng Chen; Antonio F. Galvao; Seunghwa Rho; Timothy J. Vogelsang; Jungmo Yoon |
| Abstract: | This paper develops fixed-smoothing (fixed-b, fixed-K) inference methods for time-series quantile regression that are robust to heteroskedasticity and autocorrelation. Our approach is uniformly valid over quantile levels and accounts for dependence both over time and across quantiles. It enables the construction of uniform confidence bands, Wald, and Sup-t tests for joint hypotheses, and tests of shape restrictions, providing a unified framework for assessing heterogeneity in quantile effects. A key challenge is that, under weak dependence, uniform inference for quantile regression processes is generally non-pivotal because the limiting distributions depend on the long-run covariance structure across quantiles. To address this issue, we develop two complementary approaches. The uniform-in-$\tau$ method estimates the covariance structure and simulates the non-pivotal limiting distribution. For certain tests involving a finite collection of quantile levels, the stack-Wald method delivers pivotal fixed-smoothing inference. We establish the asymptotic validity of both approaches. Simulation results show that the proposed methods substantially improve size control relative to existing HAC-based procedures while maintaining good power. An application to predictive quantile regressions for stock returns reveals substantial heterogeneity in predictive effects across both quantiles and forecast horizons. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.05883 |
| By: | Elie Tamer; Christopher D. Walker |
| Abstract: | This paper proposes a nonparametric Bayesian inference framework for partially identified discrete response models. The key observation is that these models map a reduced-form conditional choice probability to an identified set. Consequently, nonparametric Bayesian inference for the conditional probability mass function leads to Bayesian inference for the identified set. The inference framework nests conditional moment inequalities and linear systems with unknown coefficients as special cases. Importantly, our proposal does not require converting conditional moments into unconditional moments or discretizing covariates. We show that the posterior is consistent for the true identified set when the model is correctly specified, show that the posterior can consistently detect model misspecification, and show posterior consistency for a pseudo-identified set that is valid under misspecification. We also verify the assumptions for a class of priors based on Gaussian processes that we use to implement our proposal. These priors offer similar flexibility to frequentist partial identification methods, and are computationally attractive because posterior sampling can be performed in closed-form. We also show that many of the ideas in this paper extend to continuous responses and aggregated discrete responses (e.g., market shares). |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.25814 |
| By: | Duong Trinh |
| Abstract: | This paper develops a new econometric framework to identify and estimate policy-relevant causal effects in contexts with endogenous selection into treatment and spillovers within single large networks or spatial settings. Conventional causal inference methods relying on either unconfoundedness or no-interference assumptions are generally inadequate in these scenarios. We introduce a Spillover Roy model that jointly models endogenous treatment selection and potential outcomes while allowing spillovers through a low-dimensional exposure mapping of neighbors' treatments. The model captures heterogeneous treatment responses across levels of latent resistance to treatment and neighborhood exposure. Within this framework, we define policy-relevant direct, spillover, and total effects under feasible policy changes and show that the total effect decomposes into a direct component from policy-induced participation and a spillover component from policy-induced changes in neighborhood treatment exposure. For estimation and inference, we develop a Bayesian data-augmentation algorithm with parameter expansion that enables efficient posterior computation and coherent uncertainty quantification for heterogeneous causal effects and policy counterfactuals. An application to the U.S. Opportunity Zones program finds positive direct effects on housing development but limited spillover benefits, while counterfactual policy analysis reveals diminishing returns from program expansion. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.25720 |
| By: | Lennard Ma{\ss}mann; Karolina Gliszczy\'nska-Schroeder |
| Abstract: | Heavy-tailed and skewed outcomes are common in the randomized experiments and observational studies used to estimate heterogeneous treatment effects, yet the mean-squared-error criterion that guides splitting in honest causal trees is sensitive to the extreme values they generate. Building on the causal forest framework (Athey and Imbens, 2016; Wager and Athey, 2018), we introduce the Median Squared Deviation (MSD) criterion, which replaces the leafwise difference in means in the honest splitting objective with the Hodges--Lehmann location estimator while leaving honest leaf estimation and forest inference unchanged. Two further median-based rules, the Median Absolute Deviation (MAD) and the Least Median of Squares (LMS), serve as robust baselines. We evaluate the criteria in a simulation study covering precision, bias, and confidence interval coverage. MSD restricts its robustness to split selection and lowers the error of conditional average treatment effect estimates under heavy-tailed and skewed outcomes. Further, we re-visit two empirical applications: the first analyzes the electoral effects of a Mexican conditional cash transfer program on precinct-level observations, while the second application studies antiretroviral treatments in HIV-positive adults. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.07888 |
| By: | Dou, Baojun; He, Jing; Tiwari, Sudhir; Yao, Qiwei |
| Abstract: | Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily trading volume curve of one asset, and each row captures synchronized 5-minute volume intervals across multiple assets. While traditional matrix time series focus mainly on temporal evolution, our approach incorporates both spatial and temporal dynamics, enabling simultaneous analysis of interactions across multiple dimensions. The inherent endogeneity in spatio-temporal autoregressive models renders ordinary least squares estimation inconsistent. To overcome this difficulty while simultaneously estimating two distinct weight matrices with banded structure, we develop an iterated generalized Yule-Walker estimator by adapting a generalized method of moments framework based on Yule-Walker equations. Moreover, unlike conventional models that employ a single bandwidth parameter, the dual-bandwidth specification in our framework requires a new two-step, ratio-based sequential estimation procedure. |
| Keywords: | bandedcoefficient matrices;iterative least squares estimation;Yule-Walker equation;intraday volume curve;percentage of volume (POV) execution strategy |
| JEL: | C1 |
| Date: | 2026–11–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:140645 |
| By: | Florian Huber; Gary Koop; Christian Matthes |
| Abstract: | Heterogeneous-agent New Keynesian (HANK) models characterize how entire cross-sectional distributions respond to structural shocks. Traditional representative-agent models are routinely disciplined by impulse responses from aggregate vector autoregressions (VARs). HANK models have no comparable established empirical benchmark because they make predictions not only about aggregates, but also about distributions of micro-level data. We propose a Bayesian benchmark that jointly models macroeconomic aggregates and several marginal distributions from repeated cross sections, including distributions observed in different surveys. Our approach can use both standard structural VAR identification approaches on macroeconomic aggregates and identification restrictions imposed on micro-level data. The model delivers a joint posterior of the distributional effects of shocks, without the need for household panel data or a separate first-stage density estimate. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.06827 |
| By: | Shiyao Liu; Junni L. Zhang |
| Abstract: | Recent work encourages political scientists to move from post-only toward within-subject designs for improved precision from repeated measurements. We formalize a potential-outcomes framework for two-period within-subject designs that allows for unequal allocation and heterogeneous treatment and carryover effects. We characterize the pooled estimator and evaluate the carryover test used to justify pooling. We find: first, pooling identifies the average treatment effect only when the gap in the average carryover effects is zero across the two treatment sequences. The unit-clustered standard error for the pooled estimator is identical to its design-based counterpart. Second, under mild conditions, the carryover test has strictly less power than the average-treatment-effect test with post-only data. The resulting two-step procedure, which pools only after a nonrejected test, produces confidence intervals that typically undercover. When the gap is zero, undercoverage occurs if and only if pooling is more efficient than post-only analysis, precisely when the within-subject design is worthwhile. When the gap is nonzero, undercoverage is typical unless the gap or sample size is large. Third, we derive a sensitivity analysis and find published conclusions robust to plausible carryover gaps. We therefore endorse within-subject designs but recommend justifying a zero carryover gap substantively and reporting sensitivity to departures. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.26606 |
| By: | Alex Bernstein; Lisa R. Goldberg; Nicholas Gunther; Alec N. Kercheval; Tian Lan; Yian Lin; Dayi Yao |
| Abstract: | In a statistical factor model, principal components (or eigenvectors) of a sample covariance matrix serve as estimates of {\it principal directions}, the true drivers of co-movement of a collection of observed variables. We write the often substantial error in these estimates as a sum of two interpretable terms, which we show have almost sure asymptotic limits as the number of variables grows with sample size bounded. This scenario is commonplace in financial economics, genomics, machine learning and signal processing. {\it Out-of-subspace error} measures the distance from an estimate to the subspace spanned by population factor exposures. It can be expressed in terms of data, providing an estimable floor for error. {\it In-subspace error} arises from the fixed sample size of the latent factor returns and cannot be estimated from data alone. We illustrate our error analysis with a three-factor simulation of the US public equity market, showing the dependence of the magnitude of the error and its components on dimension and sample size. In that simulation, out-of-subspace error dominates. Researchers who rely on principal component analysis to estimate factor models can use our results to quantify errors in model-based predictions and attributions. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.20550 |
| By: | Frédérqiue Bec; Heino Bohn Nielsen (CY Cergy Paris Université, THEMA) |
| Abstract: | Forecast error variance decompositions (FEVDs) are widely used to assess the contribution of structural shocks in vector autoregressions. However, many variables of interest are nonlinear functions of underlying variables, rendering the standard linear FEVD incomplete. We develop a framework for variance decomposition of nonlinear forecast targets in terms of the Shapley value decomposition and compare it with more conventional approaches based on Taylor expansions. We illustrate that nonlinear interaction effects can account for components of forecast uncertainty that are not fully captured by Taylor approximations. As a result, approximation-based FEVD may substantially distort the picture of forecast uncertainty and the attribution of variance across shocks. |
| Keywords: | Vector Autoregression; Nonlinear Forecast Error Variance Decomposition; Shapley Shares; Generalized Shapley Shares; Interaction Terms. |
| JEL: | C32 C13 E44 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ema:worpap:2026-09 |
| By: | Federico A. Bugni; Federico Crippa; Daniel Restrepo |
| Abstract: | We propose the first manipulation test designed for boundary discontinuity designs (BDDs) with general boundary shapes. A BDD is a multidimensional extension of the regression discontinuity design (RDD) in which treatment assignment is determined by whether the multidimensional running variable crosses a lower-dimensional boundary set. The test avoids multivariate density estimation and builds on the observation that, in the absence of manipulation, observations near the boundary should be approximately evenly split between treatment and control within arbitrary groups defined by their projections onto the boundary. We test this implication using a collection of binomial balance tests on observations near the boundary, with groups formed by k-means clustering. We establish the asymptotic validity of the test under suitable regularity conditions. We also evaluate finite-sample performance through Monte Carlo simulations and illustrate the test in three empirical applications. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.00350 |
| By: | Luca A. Pennacchio (Johannes Gutenberg University, Germany) |
| Abstract: | Many empirical macroeconomic questions rely on filtering non-stationary data to extract a stationary, business-cycle-like component. Common practice is to apply a linear filter with a standard passband, although economically relevant cycles may lie outside this passband. The resulting signal-extraction error likely affects subsequent regression estimates. This paper proposes a Continuous Wavelet Transform-informed filter that uses the CWT scalogram to identify statistically significant passbands in raw, non-stationary data relative to a researcher-specified null model. These passbands are supplied to a flexible Butterworth bandpass filter to extract a denoised, stationary cycle. Simulations show that the method improves signal extraction for periodic cycles and recovers statistically informative variation in stochastic-cycle settings missed by commonly used BK, HP, and Hamilton filters, while performing comparably or better in terms of correlation and RMSE. Applications demonstrate its use for cycle extraction, seasonal adjustment, and frequency-dependent regression analysis. |
| Keywords: | Business cycles, Signal extraction, Continuous Wavelet Transform, Bandpass filtering, Spectral analysis |
| Date: | 2026–08–31 |
| URL: | https://d.repec.org/n?u=RePEc:jgu:wpaper:2607 |
| By: | Marcelo Fernandes; Vitor Henriques; Eduardo Fonseca Mendes |
| Abstract: | We establish the consistency and asymptotic normality of a two-step estimator of conditional expectiles in the context of conditional scale models. We first estimate the conditional variance parameters by quasi-maximum likelihood and then compute the unconditional expectile of the innovations using the empirical distribution of the standardized residuals. We show how replacing true innovations with standardized residuals affects the asymptotic variances of both conditional and unconditional expectile estimators. Finally, our empirical analysis reveals that conditional expectiles assess tail risk in cryptomarkets in a more robust manner than traditional quantile-based risk measures, such as value at risk and expected shortfall. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.02673 |
| By: | Michael Stanley Smith; Lin Deng |
| Abstract: | Multivariate longitudinal data may exhibit non-Gaussian margins, nonlinear dynamics, and response vectors with composition that varies across waves. To account for these features, we introduce a vector drawable vine (VD-vine) copula that extends conventional drawable vine copulas from scalar to vector-valued nodes. Here, the response vector at each wave forms a multivariate marginal, and serial dependence is captured through a sequence of linking vector copulas. We establish that the VD-vine is itself a vector copula and reduces to a conventional drawable vine for scalar nodes. Recursive forward and backward conditional transports are derived that enable efficient likelihood evaluation and predictive simulation, with parsimonious reductions under finite-order Markov and stationary restrictions. Unconstrained parameterizations for Gaussian and FGM linking vector copulas, flexible multivariate marginals, and Bayesian variational inference provide a practical implementation. Simulations show improved predictive accuracy when the marginals are asymmetric and serial dependence is multivariate, with little loss under a correctly specified Gaussian panel vector autoregression. In an eight-wave Australian panel of 1, 093 individuals with varying response vectors, the full VD-vine delivers the best cross-validated distributional forecasts among the models considered, establishing the benefit of capturing asymmetry and nonlinear dependence. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.19547 |
| By: | Yujie Hou; Xinbing Kong; Yalin Wang; Bin Wu |
| Abstract: | We develop an expected shortfall factor model (ESFM) to estimate and price common variation in the severity of lower-tail losses in large panels of asset returns. Mean factor models describe common variation in average returns, while quantile factor models describe common movements in tail thresholds. ESFM instead captures common variation in the average severity of losses below those thresholds. The model combines observed risk exposures with latent common factors. We estimate ESFM using an orthogonalized two-step procedure under which first-stage quantile estimation error has no first-order effect on the ES coefficient estimates. We establish nonasymptotic error bounds for the ES coefficients, a finite-sample Gaussian approximation, and consistent selection of the number of latent factors. Applied to a large panel of equities, ESFM uncovers common factors that react sharply to market stress and contain information not captured by mean and quantile factors. Average returns increase across portfolios sorted on ESFM exposure; high-minus-low portfolios earn annualized returns of 8.0%--11.7% and Fama--French five-factor alphas of 10.3%--15.0%. These spreads remain positive and statistically significant after conditioning separately and jointly on mean- and quantile-factor exposures. Tail-by-tail spanning tests show that ESFM factors retain significant alphas after controlling for standard traded factors and the corresponding mean and quantile factors. Adding ESFM to these benchmark factor sets increases the maximum attainable Sharpe ratio. These findings identify common loss severity as a distinct and priced dimension of downside risk. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.10587 |
| By: | Shoki Okubo |
| Abstract: | Graphical causal inference supplies a complete theory of efficient covariate adjustment for the average treatment effect: one adjustment set, computable from the graph, is optimal under every compatible distribution. We show that this is a property of the average treatment effect's inverse-prevalence weights, not of causal estimands in general. For the average treatment effect on the treated we index the efficiency bound by the adjustment set and derive exact identities for its change under treatment-side and outcome-side extensions of a valid set. Covariates that predict only the treated-arm outcome are exactly efficiency-neutral, and covariates that predict the control-arm outcome can strictly increase the bound when the propensity is below one half -- a reversal of the supplementation lemma whose source is an arithmetic-geometric-mean inequality that holds for the average treatment effect and fails for the treated-population estimand. A construction with two faithful distributions on one graph proves that no graphical optimality criterion exists for the treated-population estimand; under no effect modification the ATE-optimal set is nonetheless optimal among the graphically valid sets, with an exact expression for its advantage. The results extend to weighted average treatment effects with propensity-dependent weights, yielding symmetric thresholds for overlap weights, an estimand-drift phenomenon under instrument adjustment, and a characterization of constant weights as the only smooth positive weights for which outcome-side supplementation never increases the bound. Simulations and the LaLonde data provide illustrations. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.11222 |
| By: | Masahiro Kato |
| Abstract: | We comment on the optimally-transported generalized method of moments (OTGMM) estimator proposed by Schennach & Starck (2026a) and give counterexamples to Theorems 2-6 under their stated assumptions. First, the assumptions used in the small-error analysis are insufficient for consistency in Theorem 2 and asymptotic normality in Theorem 3. Next, we consider the large-error analysis, in which Theorem 4 states that the OTGMM estimator is equivalent to a GMM estimator with modified moments. We show that in a scalar model, Theorem 4 selects a value that differs from the unique OTGMM minimizer and violates the OTGMM sample moment restriction. In an overidentified model satisfying the assumptions used in Theorems 5 and 6, the first component of the Lagrange multiplier has different probability limits under the OTGMM estimator and the GMM estimator with modified moments. Under misspecification, the population value selected by OTGMM depends on the transport metric and on which variables may be adjusted. We give a sufficient condition under which solutions of the modified moment equations also solve the original constrained problem at a given parameter value, and separate conditions for consistency and asymptotic normality of the OTGMM estimator. We also show that Assumption 16 does not imply the matrix bound used in the supplemental proofs and replace Assumption 16 with a matrix condition that yields the bound. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.20260 |
| By: | Yukun Ma; Manu Navjeevan; Bogdan Salahub |
| Abstract: | Estimating the first stage of an instrumental variables (IV) model with the least absolute shrinkage and selection operator (LASSO) requires choosing a dictionary of technical instruments and a penalty level. First-order asymptotic theory offers no guidance on these choices, as any consistent implementation yields a structural parameter estimator with the same limiting distribution. In finite samples, however, these choices can have a substantial impact on the resulting structural parameter estimate. Working in a model with a single endogenous regressor and homoskedastic Gaussian errors, we use first- and second-order Stein identities to derive the approximate mean squared error (AMSE) of the instrumental-variables LASSO (IV-LASSO) estimator, which can be consistently estimated and used to rank a prespecified list of dictionary-penalty candidates. The AMSE reveals a bias-variance trade-off: more complex first-stage fits better approximate the conditional mean of the endogenous variable but are also more correlated with the structural errors, with complexity measured by the degrees of freedom of the LASSO fit. The weight on this bias rises with the endogeneity of the regressor, a quantity that neither plug-in nor cross-validation penalty rules take into account. Despite the AMSE being derived in a Gaussian model, penalty selection by minimizing the feasible AMSE criterion delivers up to a one-third lower mean squared error compared to cross-validation and plug-in penalty rules in Gaussian and non-Gaussian simulation designs calibrated to the data of Gilchrist and Sands (2016). |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.07033 |
| By: | Ayla Jungbluth (Ruhr-University Bochum); Johannes Lederer (University of Hamburg); Simon Trimborn (University of Amsterdam) |
| Abstract: | Modeling the joint distribution of extreme values in high-dimensional financial time series is challenging because extremes are sparse and locally extreme observations are not necessarily extreme relative to their full marginal distribution. To address this, we introduce a time-dependent network Hüsler-Reiss model in which market-informed adjacency matrices determine how strongly observations contribute to the estimation. We propose binary and weighted specifications, including the Joint Extremes Adjacency Matrix (JEAM) which combines information about individual extremeness with historical patterns of joint extreme movements. In the forecasting evaluation part, covering one-minute stock returns from three sectors of the S&P 100, JEAM achieves the best out-of-sample log scores for both tail directions; improving scores by 12.5-13.6% in the lower tail and 11.4-14.9% in the upper tail. The results show that incorporating market-informed network structures in the estimation, improves forecast evaluation of extremes across time series. |
| JEL: | C53 C58 G17 |
| Date: | 2026–09–13 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260070 |
| By: | Simon Donker van Heel (Erasmus University Rotterdam); Neil Shephard (Harvard University) |
| Abstract: | We develop a filter for time series, defined at each time t as the minimizer of a discounted convex combination of observed and expected losses. The filter can be estimated by simulation to an arbitrary level of accuracy in O(1) flops at each time point t and can be run for all values t=1, ..., T in parallel. These methods are applied to robustly compute a preaveraged price process from the more than 1.5 million trades made on a single financial asset in a single day where the noise's variance is infinite. It yields a flat ''volatility signature'' plot, down to the 1 second level, so the microstructure noise no longer biases the volatility estimate. This is not true when linear methods are employed. |
| Keywords: | Filtering; High frequency finance; Loss function; M-estimator; Volatility |
| Date: | 2026–09–13 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260068 |
| By: | Stuart, Bryan (Federal Reserve Bank of Philadelphia); Taylor, Evan (University of Arizona) |
| Abstract: | We develop a framework that allows researchers to describe a wide class of quasi-experimental treatment effect estimates using direct analogs of the features that make a randomized controlled trial transparent. The outcome weight measures how much each observation's outcome contributes to an estimate, and its sign defines effective treatment and control groups. We show estimates take a Wald form, equal to the weighted outcome difference between these groups divided by an effective first stage that quantifies the identifying treatment contrast. Our framework also allows researchers to assess covariate balance, examine how differences throughout the outcome distribution contribute to mean impacts, and identify influential observations. The treatment effect weight--the outcome weight times treatment--instead measures how much each observation's unobserved treatment effect contributes to an estimate, and exactly decomposes an estimate into subgroup-specific components. When the implied weighting of an estimate is undesirable, we show how to construct alternative estimates that satisfy researcher-specified criteria. We illustrate the framework with analyses of democracy and growth, Chinese import competition, and returns to schooling. |
| Keywords: | treatment effect estimation, transparency |
| JEL: | C13 C81 C82 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18903 |
| By: | Gerton Rongen (Vrije Universiteit Amsterdam); Peter Lanjouw (Vrije Universiteit Amsterdam); Chris Elbers (Vrije Universiteit Amsterdam) |
| Abstract: | This paper proposes a Bayesian-inspired refinement to the synthetic panel method introduced by Dang et al. (2014), yielding point estimates of poverty transitions and other income mobility metrics. It uses bootstrap samples of household income (and consumption expenditure) correlation coefficients observed in available panel surveys to proxy for correlations over time in countries where no panel surveys are available. This allows more precise estimation of poverty dynamics based on repeated cross-sectional surveys. We present results in the form of a band of two standard deviations (both plus and minus) around the point estimate. A validation analysis using actual Tanzanian panel data shows that, with few exceptions, these bands overlap with the true estimates’ confidence intervals. Subsequent application to Malaysian poverty dynamics over the period 2004-2022 illustrates how the method can be employed. The resulting four standard deviation bands are much narrower than intervals based on the original, conservative, synthetic panel bound estimates, in particular for conditional probabilities, such as the chance of escaping poverty. Hence, the results of this approach yield more relevant policy information. |
| JEL: | I32 O15 |
| Date: | 2026–09–06 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260066 |
| By: | Mojtaba Eslami |
| Abstract: | In staggered treatment-adoption designs, later-treated units are valid controls for an earlier-treated cohort only until their own treatment begins, so the admissible donor set contracts with event time. Fixing the donor pool at the longest horizon discards temporarily eligible donors, whereas re-estimating synthetic-control weights independently at each horizon can make the counterfactual unstable as donor composition changes. We propose Risk-Set Transported Synthetic Control with Difference-in-Differences Adjustment (RT-SC-DiD). For each cohort and event-time horizon, the estimator fits weights on the currently untreated donors while shrinking them toward a transported reference that reallocates the weight of exiting donors to similar surviving donors. A DiD baseline correction removes persistent level differences. We characterize distortion from horizon-by-horizon reoptimization, derive the loading change induced by naive deletion and renormalization, and give a conditional recursive bound for error propagation under an explicitly assumed regularity condition on the transport map. We also introduce donor-support diagnostics and a donor-only placebo procedure for selecting the transport penalty. In an 80-replication pilot comparison and a separate 40-replication-per-value sensitivity analysis, intermediate transport regularization reduces average RMSE relative to independent horizon-specific estimation and strong anchoring. This evidence supports the method's bias-variance motivation but is not a proved guarantee. RT-SC-DiD is intended for settings where later-treated units provide useful short-horizon information and donor support contracts materially over time. Existing staggered synthetic-control and synthetic difference-in-differences methods do not, to our knowledge, explicitly regularize within-cohort weight sequences toward transported references as risk sets contract. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.20264 |
| By: | Bastien Buchwalter; Francis X. Diebold; Kamil Yilmaz |
| Abstract: | We extend the clustered connectedness framework of Buchwalter, Diebold and Yilmaz (2026) in two complementary directions that improve the robustness and interpretability of cross-cluster connectedness. First, we develop a diagnostic for residual ordering sensitivity by characterizing the distribution of cluster-level net connectedness across all admissible identification orderings and, in particular, by pairing first- and last-position orderings while holding fixed the relative ordering of all other clusters. Second, we introduce a dedicated cluster of control variables to absorb variation associated with observed common macro-financial factors while preserving the computational scalability of the clustered framework. The control cluster is fixed first, and bank innovations are residualized with respect to it before the remaining bank clusters are permuted and orthogonalized as usual, leaving the number of admissible bank-cluster identification strategies unchanged. Under the maintained recursive assumption that control-cluster innovations are contemporaneously exogenous to bank-cluster innovations, the remaining cross-cluster connectedness among the bank clusters can be interpreted as bank-to-bank transmission net of those observed common-factor shocks. We apply the methodology to seventy-one global banks grouped into seven regional clusters over 2003--2024. The treatment of common macro-financial factors materially affects both system-wide cross-group connectedness and cluster-level net positions. Placing the controls in a dedicated first cluster also substantially reduces paired first-versus-last ordering sensitivity across all seven bank clusters, with especially large reductions for the United States and the European clusters. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.05792 |
| By: | Jihwan Woo |
| Abstract: | Coupled feedback networks are often monitored channel by channel even though cross-channel paths alter both stability margins and transmitted disturbances. We study identification of a structured feedback matrix L_t = Phi diag(gamma_t) in an output-only setting: no commanded, probing, or reference input exists -- only temporally separated outputs and the scheduling gains gamma_t are observed, while the coupling response Phi and the clearing-window inputs are not. Identification rests jointly on the persistent excitation of the observed pre-window output and on two structural features separating coupling from confounds: the known time variation of the gains, which modulates the closed-loop response in a predictable pattern, and a partial-reversal moment by which a known fraction of transient displacement is corrected in a subsequent window. We give a hierarchy of results: exact local identification of the coupling under a Jacobian rank condition on the gain regimes; a first-order interaction estimator whose identification strength is the minimum eigenvalue of the residualized interaction information matrix (provably unidentified under constant gains); and a characterization of the estimand as a resolvent sensitivity -- the right object for screening transmitted disturbances and a first-stage input to spectral-margin recovery -- with sqrt(T) asymptotics for the first-order estimator, a cross-identification theorem mapping each varying gain to exactly identified resolvent rows and columns, and bootstrap validity under consistent selection; the implemented heuristic's empirical coverage (90% at nominal 95%) quantifies the remaining gap. Simulations verify sharpness of the rank condition and quantify benchmark failures under confounding. A case study on leveraged-fund rebalancing feedback, where daily fund disclosures play the role of the known gains, illustrates the method on real data. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.25844 |
| By: | Manuel Naviglio; Fabrizio Lillo |
| Abstract: | Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and state-dependent mechanisms. Linear models are interpretable but may miss these effects, while deep neural networks improve forecasting at the cost of transparency. We use neural networks as tools for structural discovery rather than only for prediction. A deep feed-forward network is trained on high-frequency returns and signed volumes for large- and small-tick stocks and compared with a linear VAR benchmark. The neural network improves predictive performance, especially for returns, revealing nonlinear dependencies beyond the linear specification. Using Shapley-based explainability, we show that the dominant contributions are concentrated at the most recent lags. Model-implied responses are consistent with conditional averages reconstructed from the data. Unlike empirical averages, however, the neural-network decomposition isolates individual regressor contributions to the aggregate dependence. Lagged signed volume generates sign-preserving and saturating effects, consistent with nonlinear price impact and order-flow persistence. Lagged returns act as state variables: when the previous trade does not move the price, the model predicts continuation in the direction of past order flow, whereas non-zero returns generate attenuation or reversal. Building on these findings, we introduce a parsimonious SHAP-inspired nonlinear parametric model. It reproduces the main return-volume dependencies, outperforms the linear VAR benchmark, and achieves performance comparable to the neural network. A multi-lag extension captures residual longer-memory effects while preserving interpretability. Overall, explainability offers a route from black-box prediction to economically meaningful parametric models of price and trade dynamics. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.06085 |
| By: | Kennedy Titus Kayaki; Kyungsub Lee |
| Abstract: | We introduce ALM-GARCH, an asymmetric long-memory GARCH model in which positive and negative innovations enter conditional variance with different injection amplitudes and kernel offsets. These departures define testable level and memory channels relative to a nested symmetric benchmark. Positive Harris recurrence holds for interior configurations under a Foster-Lyapunov condition. Across five equity indices and Bitcoin, joint symmetry is rejected throughout, driven primarily by the level channel. The memory channel is supported for the Nikkei 225, KOSPI, and Bitcoin but is weakly identified when the positive branch is nearly inactive. Out-of-sample performance is broadly comparable to standard benchmarks. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.06422 |