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on Econometrics |
| By: | Victor Chernozhukov; Ben Deaner; Ying Gao; Jerry A. Hausman; Whitney Newey |
| Abstract: | This paper develops linear estimators for structural and causal parameters in nonparametric, nonseparable models using panel data. These models incorporate unobserved, time-varying, individual heterogeneity, which may be correlated with the regressors. Estimation is based on an approximation of the nonseparable model by a linear sieve specification with individual specific parameters. Effects of interest are estimated by a bias corrected average of individual ridge regressions. We demonstrate how this approach can be applied to estimate causal effects, counterfactual consumer welfare, and averages of individual taxable income elasticities. We show that the proposed estimator has an empirical Bayes interpretation and possesses a number of other useful properties. We formulate Large-T asymptotics that can accommodate discrete regressors and which bypass partial identification in this case. We employ the methods to estimate average equivalent variation and deadweight loss for potential price increases using data on grocery purchases. |
| JEL: | C1 C14 C23 C26 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33325 |
| By: | Liying Yang; Yi Qian; Hui Xie |
| Abstract: | Endogeneity is a primary concern when evaluating causal effects using observational panel data. While unit-specific intercepts control for unobserved time-invariant confounders, dependence between (i) regressors (e.g., marketing mix strategy of interests) and the current error term (regressor endogeneity) and/or between (ii) regressors and heterogeneous slope coefficients (slope endogeneity) can introduce significant estimation bias, resulting in misleading inference. This paper proposes a two-stage copula endogeneity correction mean group (2sCOPE-MG) estimator for panel data models, simultaneously addressing both endogeneity concerns. We generalize the IV-free copula control function, employing a general location Gaussian copula that effectively captures the panel structure. The heterogeneous coefficients are treated as unit-specific fixed parameters without distributional assumptions. Consequently, the 2sCOPE-MG estimator allows for arbitrary dependence structure between heterogeneous coefficients and regressors. Unlike Haschka (2022), 2sCOPE-MG requires neither a normal error distribution nor a Gaussian copula regressor-error dependence structure and is more robust, easier to implement, and capable of addressing slope endogeneity. The 2sCOPE-MG estimator is extended to dynamic panels, where intertemporal dependence in the outcome process can be suitably captured. We study its asymptotic properties and provide an analytical variance formula for inference without the need to bootstrap. For short dynamic panels, a Jackknife bias-corrected 2sCOPE-MG estimator is provided to ensure unbiased inference. The usage of the 2sCOPE-MG estimator is demonstrated by Monte Carlo simulations and a marketing mix response application across 21 categories to account for regressor and slope endogeneities in store-panel sales data. |
| JEL: | C10 C5 L16 L2 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33265 |
| By: | Chunrong Ai; Jiawei Shan |
| Abstract: | One approach to estimating the average treatment effect in binary treatment with unmeasured confounding is the proximal causal inference, which assumes the availability of outcome and treatment confounding proxies. The key identifying result relies on the existence of a so-called bridge function. A parametric specification of the bridge function is usually postulated and estimated using standard techniques. The estimated bridge function is then plugged in to estimate the average treatment effect. This approach may have two efficiency losses. First, the bridge function may not be efficiently estimated since it solves an integral equation. Second, the sequential procedure may fail to account for the correlation between the two steps. This paper proposes to approximate the integral equation with increasing moment restrictions and jointly estimate the bridge function and the average treatment effect. Under sufficient conditions, we show that the proposed estimator is efficient. To assist implementation, we propose a data-driven procedure for selecting the tuning parameter (i.e., number of moment restrictions). Simulation studies reveal that the proposed method performs well in finite samples, and application to the right heart catheterization dataset from the SUPPORT study demonstrates its practical value. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.02214 |
| By: | Jungbin Hwang (University of Connecticut); Yixiao Sun (University of California, San Diego) |
| Abstract: | This paper develops asymptotic F and t tests for nonlinear cointegrated re-gression, where regressors are asymptotically homogeneous transformations of I(1) processes. These transformations encompass a broad class of functions, includ-ing distribution-like functions, logarithmic functions, and asymptotically polynomial functions. Our asymptotic F and t test theory covers both the case with exogenous regressors and the case with endogenous regressors. For the exogenous case, we con-struct a novel set of basis functions for series long-run variance estimation, effectively accounting for parameter estimation uncertainty. For the endogenous case, we extend the transformed-augmented OLS approach developed for linear cointegrated settings. Monte Carlo simulations show that our asymptotic F and t tests outperform compet-ing tests, including the asymptotic chi-square test based on the fully modified OLS estimator and the non-standard fixed-b test based on the integrated modified OLS estimator. Furthermore, our theory extends to cases where the processes driving regressors are nonstationary, fractionally integrated processes. |
| Keywords: | F test and F distribution, nonlinear cointegrating regression, unit root, t test and t distribution, fractional integration |
| JEL: | C12 C13 C32 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:uct:uconnp:2025-01 |
| By: | Lu, Zhentong (Bank of Canada); Shimizu, Kenichi (University of Alberta, Department of Economics) |
| Abstract: | We propose a new approach to estimating the random coefficient logit demand model for differentiated products when the vector of market-product level shocks is sparse. Assuming sparsity, we establish nonparametric identification of the distribution of random coefficients and demand shocks under mild conditions. Then we develop a Bayesian procedure, which exploits the sparsity structure using shrinkage priors, to conduct inference about the model parameters and counterfactual quantities. Comparing to the standard BLP (Berry, Levinsohn, & Pakes, 1995) method, our approach does not require demand inversion or instrumental variables (IVs), thus provides a compelling alternative when IVs are not available or their validity is questionable. Monte Carlo simulations validate our theoretical findings and demonstrate the effectiveness of our approach, while empirical applications reveal evidence of sparse demand shocks in well-known datasets. |
| Keywords: | Demand Estimation; Sparsity; Bayesian Inference; Shrinkage Prior |
| JEL: | C10 C30 D10 L00 |
| Date: | 2025–01–16 |
| URL: | https://d.repec.org/n?u=RePEc:ris:albaec:2025_001 |
| By: | M. Hashem Pesaran; Yimeng Xie |
| Abstract: | In a recent paper Juodis and Reese (2022) (JR) show that the application of the CD test proposed by Pesaran (2004) to residuals from panels with latent factors results in over-rejection. They propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This paper considers the same problem but from a different perspective, and shows that the standard CD test remains valid if the latent factors are weak in the sense the strength is less than half. In the case where latent factors are strong, we propose a bias-corrected version, CD*, which is shown to be asymptotically standard normal under the null of error cross-sectional independence and have power against network type alternatives. This result is shown to hold for pure latent factor models as well as for panel regression models with latent factors. The case where the errors are serially correlated is also considered. Small sample properties of the CD* test are investigated by Monte Carlo experiments and are shown to have the correct size for strong and weak factors as well as for Gaussian and non-Gaussian errors. In contrast, it is found that JR.s test tends to over-reject in the case of panels with non-Gaussian errors, and has low power against spatial network alternatives. In an empirical application, using the CD* test, it is shown that there remains spatial error dependence in a panel data model for real house price changes across 377 Metropolitan Statistical Areas in the U.S., even after the effects of latent factors are filtered out. |
| Keywords: | latent factor models, strong and weak factors, error cross-sectional independence, spatial and network alternatives, size and power |
| JEL: | C18 C23 C55 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11470 |
| By: | Akanksha Negi; Didier Nibbering |
| Abstract: | This paper proposes a class of methods for identifying and estimating dynamic treatment effects when outcomes depend on the entire treatment path and treatment histories are only partially observed. We advocate for the approach which we refer to as `robust' that identifies path-dependent treatment effects for different mover subpopulations under misspecification of any one of three models involved (outcome, propensity score, or missing data models). Our approach can handle fixed, absorbing, sequential, or simultaneous treatment regimes where missing treatment histories may obfuscate identification of causal effects. Numerical experiments demonstrate how the proposed estimator compares to traditional complete-case methods. We find that the missingness-adjusted estimates have negligible bias compared to their complete-case counterparts. As an illustration, we apply the proposed class of adjustment methods to estimate dynamic effects of COVID-19 on voter turnout in the 2022 U.S. general elections. We find that counties that experienced above-average number of cases in 2020 and 2021 had a statistically significant reduction in voter turnout compared to those that did not. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.04853 |
| By: | Gianluca Cubadda; Francesco Giancaterini; Stefano Grassi |
| Abstract: | This paper proposes a Sequential Monte Carlo approach for the Bayesian estimation of mixed causal and noncausal models. Unlike previous Bayesian estimation methods developed for these models, Sequential Monte Carlo offers extensive parallelization opportunities, significantly reducing estimation time and mitigating the risk of becoming trapped in local minima, a common issue in noncausal processes. Simulation studies demonstrate the strong ability of the algorithm to produce accurate estimates and correctly identify the process. In particular, we propose a novel identification methodology that leverages the Marginal Data Density and the Bayesian Information Criterion. Unlike previous studies, this methodology determines not only the causal and noncausal polynomial orders but also the error term distribution that best fits the data. Finally, Sequential Monte Carlo is applied to a bivariate process containing S$\&$P Europe 350 ESG Index and Brent crude oil prices. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.03945 |
| By: | Kreye, Tom Jannik |
| Abstract: | In this paper, tests for fractional cointegration that allow for structural breaks in the long-run equilibrium are proposed. Traditional cointegration tests cannot handle shifts in fractional cointegration relationships, a limitation addressed here by allowing for a time-dependent memory parameter for the cointegration error. The tests are implemented by taking the extremum of a residual-based fractional cointegration test applied to different subsamples of the data. The subsampling procedures include sample splits, incremental samples, and rolling samples. A fairly general cointegration model is assumed, where the observed series and the cointegration error are fractionally integrated processes. Under the alternative hypothesis, the tests converge to the supremum of a chi-squared distribution. A Monte Carlo simulation is used to evaluate the finite sample performance of the tests. |
| Keywords: | Fractional Cointegration, Long Memory, Monte Carlo, Persistence Breaks, Structural Breaks, Subsample Analysis |
| JEL: | C12 C32 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:han:dpaper:dp-733 |
| By: | Pedro Isaac Chavez-Lopez (Bank of Mexico); Tae-Hwy Lee (Department of Economics, University of California Riverside) |
| Abstract: | We propose a factor model for quantile regression using quantile-covariance(qcov), which will be called the Quantile-Covariance Three-Pass Regression Filter (Qcov3PRF). Inspired by the Three-Pass Regression Filter (3PRF, Kelly and Pruitt, 2015), our method selects the relevant factors from a large set of predictors to forecast the conditional quantile of a target variable. The measure qcov is implied by the first order condition from a univariate linear quantile regression. Our approach differs from the Partial Quantile Regression (PQR, Giglio et al., 2016) as Qcov3PRF successfully allows the estimation of more than one relevant factor using qcov. In particular, qcov permits us to run time series least squares regressions of each regressor on a set of transformations of the variables,  indexed for a specific quantile of the forecast target, known as proxies that only depend on the relevant factors. This is not possible to be executed using quantile regressions as regressing each predictor on the proxies refers to the conditional quantile of the predictor and not the quantile corresponding to the target. As a consequence of running a quantile regression of the target or proxy on each predictor, only one factor is recovered with PQR. By capturing the correct number of the relevant factors, the Qcov3PRF forecasts are consistent and asymptotically normal when both time and cross sectional dimensions become large. Our simulations show that Qcov3PRF exhibits good finite sample properties compared to alternative methods. Finally, three applications are presented: forecasting the Industrial Production Growth, forecasting the Real GDP growth, and forecasting the global temperature change index. |
| Keywords: | Quantile regression; factor models; quantile-covariance |
| JEL: | C1 C5 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202501 |
| By: | Xiaoqian Wang; Rob J Hyndman |
| Abstract: | We consider the problem of constructing distribution-free prediction intervals for multi-step​ time series forecasting, with a focus on the temporal dependencies inherent in multi-step forecast​ errors. We establish that the optimal h-step-ahead forecast errors exhibit serial correlation​ up to lag (h-1) under a general non-stationary autoregressive data generating process. To​ leverage these properties, we propose the Autocorrelated Multi-step Conformal Prediction(AcMCP) method, which effectively incorporates autocorrelations in multi-step forecast errors, resulting in more statistically efficient prediction intervals. This method ensures theoretical​ long-run coverage guarantees for multi-step prediction intervals, though we note that increasedf​ orecasting horizons may exacerbate deviations from the target coverage, particularly in the​ context of limited sample sizes. Additionally, we extend several easy-to-implement conformal prediction methods, originally designed for single-step forecasting, to accommodate multi-step scenarios. Through empirical evaluations, including simulations and applications to data, we demonstrate that AcMCP achieves coverage that closely aligns with the target within local windows, while providing adaptive prediction intervals that effectively respond to varying conditions. |
| Keywords: | Conformal Prediction, Coverage Guarantee, Distribution-Free Inference, Exchangeability, Weighted Quantile Estimate |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2024-20 |
| By: | Niousha Bagheri; Milad Ghasri; Michael Barlow |
| Abstract: | This paper introduces a framework for capturing stochasticity of choice probabilities in neural networks, derived from and fully consistent with the Random Utility Maximization (RUM) theory, referred to as RUM-NN. Neural network models show remarkable performance compared with statistical models; however, they are often criticized for their lack of transparency and interoperability. The proposed RUM-NN is introduced in both linear and nonlinear structures. The linear RUM-NN retains the interpretability and identifiability of traditional econometric discrete choice models while using neural network-based estimation techniques. The nonlinear RUM-NN extends the model's flexibility and predictive capabilities to capture nonlinear relationships between variables within utility functions. Additionally, the RUM-NN allows for the implementation of various parametric distributions for unobserved error components in the utility function and captures correlations among error terms. The performance of RUM-NN in parameter recovery and prediction accuracy is rigorously evaluated using synthetic datasets through Monte Carlo experiments. Additionally, RUM-NN is evaluated on the Swissmetro and the London Passenger Mode Choice (LPMC) datasets with different sets of distribution assumptions for the error component. The results demonstrate that RUM-NN under a linear utility structure and IID Gumbel error terms can replicate the performance of the Multinomial Logit (MNL) model, but relaxing those constraints leads to superior performance for both Swissmetro and LPMC datasets. By introducing a novel estimation approach aligned with statistical theories, this study empowers econometricians to harness the advantages of neural network models. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.05221 |
| By: | Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr |
| Abstract: | We propose a multiscale approach to time series autoregression, in which linear regressors for the process in question include features of its own path that live on multiple timescales. We take these multiscale features to be the recent averages of the process over multiple timescales, whose number or spans are not known to the analyst and are estimated from the data via a change-point detection technique. The resulting construction, termed Adaptive Multiscale AutoRegression (AMAR) enables adaptive regularisation of linear autoregressions of large orders. The AMAR model is designed to offer simplicity and interpretability on the one hand, and modelling flexibility on the other. Our theory permits the longest timescale to increase with the sample size. A simulation study is presented to show the usefulness of our approach. Some possible extensions are also discussed, including the Adaptive Multiscale Vector AutoRegressive model (AMVAR) for multivariate time series, which demonstrates promising performance in the data example on UK and US unemployment rates. The R package amar (Baranowski et al., 2022) provides an efficient implementation of the AMAR framework. |
| Keywords: | multiscale modelling; regularised autoregression; piecewise-constant approximation; time series |
| JEL: | C1 |
| Date: | 2024–10–23 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126054 |
| By: | Yangzhuoran Fin Yang; Rob J Hyndman; George Athanasopoulos; Anastasios Panagiotelis |
| Abstract: | We propose a novel forecast linear augmented projection (FLAP) method that can reduce the forecasterror variance of any multivariate forecast. The method first constructs new component series whichare linear combinations of the original series. Forecasts are then generated for both the original andcomponent series. Finally, the full vector of forecasts is projected onto a linear subspace where theconstraints implied by the combination weights hold. We show that the projection using the originalforecast error covariance matrix will result in improved forecasts. Notably, the new forecast errorvariance of each series is non-increasing with the number of components, and mild conditions are established for which it is strictly decreasing. It is also shown that the proposed method achieves maximum forecast error variance reduction among linear projection methods. We demonstrateour proposed method with an estimated covariance matrix using simulations and two empirical applications based on Australian tourism and FRED-MD data. In all cases,  forecasts are improved. Notably, using FLAP with Principal Component Analysis (PCA) to construct the new series leads tosubstantial forecast error variance reduction. |
| Keywords: | Forecasting; Hierarchical time series; Grouped time series; Linear forecast reconciliation; Integer programming |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2024-13 |
| By: | Abbas, Yasser; Daouia, Abdelaati; Nemouchi, Boutheina; Stupfler, Gilles |
| Abstract: | Expectiles have received increasing attention as coherent and elicitable market risk measure. Their estimation from heavy-tailed data in an extreme value framework has been studied using solely the Weissman extrapolation method. We challenge this dominance by developing the theory of two classes of semiparametric Generalized Pareto estimators that make more efficient use of tail observations by incorporating the location, scale and shape extreme value parameters: the first class relies on asymmetric least squares estimation, while the second is based on extreme quantile estimation. A comparison with simulated and real data shows the superiority of our proposals for real-valued profit-loss distributions. |
| Keywords: | Expectile, Extreme risk, Generalized Pareto model, Heavy tails, Semiparametric; extrapolation |
| JEL: | C13 C14 C18 C53 C58 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130105 |
| By: | Youngjin Hong; In Kyung Kim; Kyoo il Kim |
| Abstract: | Understanding firm conduct is crucial for industrial organization and antitrust policy. In this article, we develop a testing procedure based on the Rivers and Vuong non-nested model selection framework. Unlike existing methods that require estimating the demand and supply system, our approach compares the model fit of two first-stage price regressions. Through an extensive Monte Carlo study, we demonstrate that our test performs comparably to, or outperforms, existing methods in detecting collusion across various collusive scenarios. The results are robust to model misspecification, alternative functional forms for instruments, and data limitations. By simplifying the diagnosis of firm behavior, our method provides an efficient tool for researchers and regulators to assess industry conduct. Additionally, our approach offers a practical guideline for enhancing the strength of BLP-style instruments in demand estimation: once collusion is detected, researchers are advised to incorporate the product characteristics of colluding partners into own-firm instruments while excluding them from other-firm instruments. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.05022 |
| By: | Nuwani K Palihawadana; Rob J Hyndman; Xiaoqian Wang |
| Abstract: | Forecasting often involves high-dimensional predictors which have nonlinear relationships with theoutcome of interest. Nonparametric additive index models can capture these relationships, while addressing the curse of dimensionality. This paper introduces a new algorithm, Sparse Multiple Index(SMI) Modelling, tailored for estimating high-dimensional nonparametric/semi-parametric additive index models, while limiting the number of parameters to estimate, by optimising predictor selectionand predictor grouping. The SMI Modelling algorithm uses an iterative approach based on mixed integer programming to solve an ℓ0-regularised nonlinear least squares optimisation problem withlinear constraints. We demonstrate the performance of the proposed algorithm through a simulationstudy, along with two empirical applications to forecast heat-related daily mortality and daily solarintensity. |
| Keywords: | Additive Index Models; Variable Selection; Dimension Reduction; Predictor Grouping; Mixed Integer Programming |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2024-16 |
| By: | Roy Cerqueti (Department of Social and Economic Sciences, Sapienza University of Rome, Italy & GRANEM, University of Angers, France); Paolo Maranzano (Department Economics, Management and Statistics); Raffaele Mattera (Department of Social and Economic Sciences, Sapienza University of Rome, Italy) |
| Abstract: | In this paper, we present an extension of the spatially-clustered linear regression models, namely, the spatially-clustered spatial autoregression (SCSAR) model, to deal with spatial heterogeneity issues in clustering procedures. In particular, we extend classical spatial econometrics models, such as the spatial autoregressive model, the spatial error model, and the spatially-lagged model, by allowing the regression coefficients to be spatially varying according to a cluster-wise structure. Cluster memberships and regression coefficients are jointly estimated through a penalized maximum likelihood algorithm which encourages neighboring units to belong to the same spatial cluster with shared regression coefficients. Motivated by the increase of observed values of the Gini index for the agricultural production in Europe between 2010 and 2020, the proposed methodology is employed to assess the presence of local spatial spillovers on the market concentration index for the European regions in the last decade. Empirical findings support the hypothesis of fragmentation of the European agricultural market, as the regions can be well represented by a clustering structure partitioning the continent into three-groups, roughly approximated by a division among Western, North Central and Southeastern regions. Also, we detect heterogeneous local effects induced by the selected explanatory variables on the regional market concentration. In particular, we find that variables associated with social, territorial and economic relevance of the agricultural sector seem to act differently throughout the spatial dimension, across the clusters and with respect to the pooled model, and temporal dimension. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.15874 |
| By: | Christopher P. Chambers; Yusufcan Masatlioglu; Christopher Turansick |
| Abstract: | People are influenced by their peers when making decisions. In this paper, we study the linear-in-means model which is the standard empirical model of peer effects. As data on the underlying social network is often difficult to come by, we focus on data that only captures an agent's choices. Under exogenous agent participation variation, we study two questions. We first develop a revealed preference style test for the linear-in-means model. We then study the identification properties of the linear-in-means model. With sufficient participation variation, we show how an analyst is able to recover the underlying network structure and social influence parameters from choice data. Our identification result holds when we allow the social network to vary across contexts. To recover predictive power, we consider a refinement which allows us to extrapolate the underlying network structure across groups and provide a test of this version of the model. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.02609 |
| By: | Tseng, Yen-hsuan |
| Abstract: | Accurate network data are essential in fields such as economics, finance, sociology, epidemiology, and computer science. However, real-world constraints often prevent researchers from collect- ing a complete adjacency matrix, compelling them to rely on partial or aggregated information. One widespread example is Aggregated Relational Data (ARD), where respondents or institutions merely report the number of links they have to nodes possessing certain traits, rather than enu- merating all neighbors explicitly. This dissertation provides an in-depth examination of two major frameworks for reconstruct- ing networks from ARD: the Bayesian latent surface model and frequentist penalized regression ap- proaches. We supplement the original discussion with additional theoretical considerations on identifiability, consistency, and potential misreporting mechanisms. We also incorporate robust estimation techniques and references to privacy-preserving strategies such as differential privacy. By embedding nodes in a hyperspherical space, the Bayesian method captures geometric distance- based link formation, while the penalized regression approach casts unknown edges in a high- dimensional optimization problem, enabling scalability and the incorporation of covariates. Sim- ulations explore the effects of trait design, measurement error, and sample size. Real-world ap- plications illustrate the potential for partially observed networks in domains like financial risk, social recommendation systems, and epidemic contact tracing, complementing the original text with deeper investigations of large-scale inference challenges. Our aim is to show that even though ARD may be coarser than full adjacency data, it retains sub- stantial information about network structures, allowing reasonably accurate inference at scale. We conclude by discussing how adaptive trait selection, hybrid geometry-penalty methods, and privacy- aware data sharing can further advance this field. This enhanced treatment underscores the prac- tical relevance and theoretical rigor of ARD-based network inference. |
| Keywords: | Aggregated Relational Data (ARD) Network Inference Bayesian Latent Surface Model (BLSM) Penalized Regression Hyperspherical Embedding Differential Privacy Federated Learning Privacy-Preserving Networks Robust Estimation Misreporting in Networks High-Dimensional Optimization Sparse Networks Social Recommendation Systems Financial Interbank Networks Epidemic Contact Tracing |
| JEL: | C38 C55 C81 D85 |
| Date: | 2025–01–03 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123164 |
| By: | S. A. Adedayo |
| Abstract: | Characterizing cause-effect relationships in complex systems could be critical to understanding these systems. For many, Granger causality (GC) remains a computational tool of choice to identify causal relations in time series data. Like other causal discovery tools, GC has limitations and has been criticized as a non-causal framework. Here, we addressed one of the recurring criticisms of GC by endowing it with proper causal interpretation. This was achieved by analyzing GC from Reichenbach's Common Cause Principles (RCCPs) and causal Bayesian networks (CBNs) lenses. We showed theoretically and graphically that this reformulation endowed GC with a proper causal interpretation under certain assumptions and achieved satisfactory results on simulation. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.02672 |
| By: | H. Spencer Banzhaf |
| Abstract: | The Random Utility Model (RUM) is a workhorse model for valuing new products or changes in public goods. But RUMs have been faulted along two lines. First, for including idiosyncratic errors that imply unreasonably high values for new alternatives and unrealistic substitution patterns. Second, for involving strong restrictions on functional forms for utility. This paper shows how, instead, starting with a revealed preference framework, one can partially identify nonparametrically the answers to policy questions about discrete alternatives. When the Generalized Axiom of Revealed Preference (GARP) is satisfied, the approach weakly identifies a pure characteristics model. When GARP is violated, it recasts the RUM errors as departures from GARP (critical cost efficiency), to be minimized using a minimum-distance criterion. This perspective provides an alternative avenue for nonparametric identification of discrete choice models. The paper illustrates the approach by estimating bounds on the values of ecological improvements in the Southern Appalachian Mountains using survey data. |
| JEL: | C14 C25 C61 D12 Q51 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33225 |
| By: | Xiaoqian Wang; Rob J Hyndman; Shanika Wickramasuriya |
| Abstract: | Forecast reconciliation ensures forecasts of time series in a hierarchy adhere to aggregation constraints, enabling aligned decision making. While forecast reconciliation can enhance overall accuracy in hierarchical or grouped structures, the most substantial improvements occur  in series with initially poor-performing base forecasts. Nevertheless, certain series may experience deteriorations in reconciled forecasts. In practical settings, series in a structure often exhibit poor base forecasts due to model  misspecification or low forecastability. To prevent their negative impact, we propose two categories of forecast reconciliation methods that incorporate time series selection based on  out-of-sample and in-sample information, respectively. These methods keep “poor†base forecasts unused in forming reconciled forecasts, while adjusting weights allocated to the remaining series accordingly when generating bottom-level reconciled forecasts. Additionally, our methods ameliorate disparities stemming from varied estimates of the base forecast error covariance matrix, alleviating challenges associated with estimator selection. Empirical evaluations through two simulation studies and applications using Australian labour force and domestic tourism data demonstrate improved forecast accuracy, particularly evident in higher aggregation levels, longer forecast horizons, and cases involving model misspecification. |
| Keywords: | Forecasting; Hierarchical Time Series; Grouped Time Series; Linear Forecast Reconciliation; Integer Programming |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2024-5 |
| By: | Bryzgalova, Svetlana; Huang, Jiantao; Julliard, Christian |
| Abstract: | We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a (potentially misspecified) stand-alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification—if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors and find that the BMA-SDF outperforms existing models in- and out-of-sample. |
| JEL: | C11 C52 G12 C50 |
| Date: | 2023–02–28 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126151 |
| By: | Joshua D. Angrist; Bruno Ferman; Carol Gao; Peter Hull; Otavio L. Tecchio; Robert W. Yeh |
| Abstract: | The ISCHEMIA Trial randomly assigned patients with ischemic heart disease to an invasive treatment strategy centered on revascularization with a control group assigned non-invasive medical therapy. As is common in such ``strategy trials, '' many participants assigned to treatment remained untreated while many assigned to control crossed over into treatment. Intention-to-treat (ITT) analyses of strategy trials preserve randomization-based comparisons, but ITT effects are diluted by non-compliance. Conventional per-protocol analyses that condition on treatment received are likely biased by discarding random assignment. In trials where compliance choices are made shortly after assignment, instrumental variables (IV) methods solve both problems -- recovering an undiluted average causal effect of treatment for treated subjects who comply with trial protocol. In ISCHEMIA, however, some controls were revascularized as long as five years after random assignment. This paper extends the IV framework for strategy trials, allowing for such dynamic non-random compliance behavior. IV estimates of long-run revascularization effects on quality of life are markedly larger than previously reported ITT and per-protocol estimates. We also show how to estimate complier characteristics in a dynamic-treatment setting. These estimates reveal increasing selection bias in naive time-varying per-protocol estimates of revascularization effects. Compliers have baseline health similar to that of the study population, while control-group crossovers are far sicker. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.01623 |