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on Econometrics |
| By: | Oriol Gonzalez-Casasus (University of Pennsylvania); Frank Schorfheide (University of Pennsylvania CEPR, PIER, NBER) |
| 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, Hyperparameter Selection, Local Projections, Misspecification, Multi-step Estimation, Shrinkage Estimators, Vector Autoregressions |
| JEL: | C11 C32 C52 C53 |
| Date: | 2025–02–05 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:25-003 |
| By: | Hiroaki Kaido; Yi Zhang |
| Abstract: | A growing number of empirical models exhibit set-valued predictions. This paper develops a tractable inference method with finite-sample validity for such models. The proposed procedure uses a robust version of the universal inference framework by Wasserman et al. (2020) and avoids using moment selection tuning parameters, resampling, or simulations. The method is designed for constructing confidence intervals for counterfactual objects and other functionals of the underlying parameter. It can be used in applications that involve model incompleteness, discrete and continuous covariates, and parameters containing nuisance components. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.17973 |
| By: | Jiti Gao; Fei Liu; Bin Peng; Yayi Yan |
| Abstract: | In this paper, we define an underlying data generating process that allows for different magnitudes of cross-sectional dependence, along with time series autocorrelation. This is achieved via high-dimensional moving average processes of infinite order (HDMA($\infty$)). Our setup and investigation integrates and enhances homogenous and heterogeneous panel data estimation and testing in a unified way. To study HDMA($\infty$), we extend the Beveridge-Nelson decomposition to a high-dimensional time series setting, and derive a complete toolkit set. We exam homogeneity versus heterogeneity using Gaussian approximation, a prevalent technique for establishing uniform inference. For post-testing inference, we derive central limit theorems through Edgeworth expansions for both homogenous and heterogeneous settings. Additionally, we showcase the practical relevance of the established asymptotic properties by revisiting the common correlated effects (CCE) estimators, and a classic nonstationary panel data process. Finally, we verify our theoretical findings via extensive numerical studies using both simulated and real datasets. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.03019 |
| By: | Man Fung Leung; Kin Wai Chan; Xiaofeng Shao |
| Abstract: | Online learning has gained popularity in recent years due to the urgent need to analyse large-scale streaming data, which can be collected in perpetuity and serially dependent. This motivates us to develop the online generalized method of moments (OGMM), an explicitly updated estimation and inference framework in the time series setting. The OGMM inherits many properties of offline GMM, such as its broad applicability to many problems in econometrics and statistics, natural accommodation for over-identification, and achievement of semiparametric efficiency under temporal dependence. As an online method, the key gain relative to offline GMM is the vast improvement in time complexity and memory requirement. Building on the OGMM framework, we propose improved versions of online Sargan--Hansen and structural stability tests following recent work in econometrics and statistics. Through Monte Carlo simulations, we observe encouraging finite-sample performance in online instrumental variables regression, online over-identifying restrictions test, online quantile regression, and online anomaly detection. Interesting applications of OGMM to stochastic volatility modelling and inertial sensor calibration are presented to demonstrate the effectiveness of OGMM. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00751 |
| By: | Chun Pong Lau |
| Abstract: | This paper develops procedures to combine clusters for the approximate randomization test proposed by Canay, Romano, and Shaikh (2017). Their test can be used to conduct inference with a small number of clusters and imposes weak requirements on the correlation structure. However, their test requires the target parameter to be identified within each cluster. A leading example where this requirement fails to hold is when a variable has no variation within clusters. For instance, this happens in difference-in-differences designs because the treatment variable equals zero in the control clusters. Under this scenario, combining control and treated clusters can solve the identification problem, and the test remains valid. However, there is an arbitrariness in how the clusters are combined. In this paper, I develop computationally efficient procedures to combine clusters when this identification requirement does not hold. Clusters are combined to maximize local asymptotic power. The simulation study and empirical application show that the procedures to combine clusters perform well in various settings. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.03865 |
| By: | Toshiki Tsuda; Yanchun Jin; Ryo Okui |
| Abstract: | This paper presents a method for constructing uniform confidence bands for the marginal treatment effect function. Our approach visualizes statistical uncertainty, facilitating inferences about the function's shape. We derive a Gaussian approximation for a local quadratic estimator, enabling computationally inexpensive construction of these bands. Monte Carlo simulations demonstrate that our bands provide the desired coverage and are less conservative than those based on the Gumbel approximation. An empirical illustration is included. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.17455 |
| By: | Martin Bruns; Helmut Lütkepohl |
| Abstract: | In conventional proxy VAR analysis, the shocks of interest are identified by external instruments. This is typically accomplished by considering the covariance of the instruments and the reduced-form residuals. Alternatively, the instruments may be internalized by augmenting the VAR process by the instruments or proxies. These alternative identification methods are compared and it is shown that the resulting shocks obtained with the alternative approaches differ in general. Conditions are provided under which their impulse responses are nevertheless identical. If the conditions are satisfied, identification of the shocks is ensured without further assumptions. Empirical examples illustrate the results and the virtue of using the identification conditions derived in this study. |
| Keywords: | Structural vector autoregression, proxy VAR, augmented VAR, fundamental shocks, invertible VAR |
| JEL: | C32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2108 |
| By: | Sadegh Shirani; Yuwei Luo; William Overman; Ruoxuan Xiong; Mohsen Bayati |
| Abstract: | In experimental settings with network interference, a unit's treatment can influence outcomes of other units, challenging both causal effect estimation and its validation. Classic validation approaches fail as outcomes are only observable under one treatment scenario and exhibit complex correlation patterns due to interference. To address these challenges, we introduce a new framework enabling cross-validation for counterfactual estimation. At its core is our distribution-preserving network bootstrap method -- a theoretically-grounded approach inspired by approximate message passing. This method creates multiple subpopulations while preserving the underlying distribution of network effects. We extend recent causal message-passing developments by incorporating heterogeneous unit-level characteristics and varying local interactions, ensuring reliable finite-sample performance through non-asymptotic analysis. We also develop and publicly release a comprehensive benchmark toolbox with diverse experimental environments, from networks of interacting AI agents to opinion formation in real-world communities and ride-sharing applications. These environments provide known ground truth values while maintaining realistic complexities, enabling systematic examination of causal inference methods. Extensive evaluation across these environments demonstrates our method's robustness to diverse forms of network interference. Our work provides researchers with both a practical estimation framework and a standardized platform for testing future methodological developments. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.01106 |
| By: | Wu, Guojun; Song, Ge; Lv, Xiaoxiang; Luo, Shikai; Shi, Chengchun; Zhu, Hongtu |
| Abstract: | There is a growing interest in developing methods to estimate individualized treatment effects (ITEs) for various real-world applications, such as e-commerce and public health. This paper presents a novel architecture, called DNet, to infer distributional ITEs. DNet can learn the entire outcome distribution for each treatment, whereas most existing methods primarily focus on the conditional average treatment effect and ignore the conditional variance around its expectation. Additionally, our method excels in settings with heavy-tailed outcomes and outperforms state-of-the-art methods in extensive experiments on benchmark and real-world datasets. DNet has also been successfully deployed in a widely used mobile app with millions of daily active users. |
| Keywords: | uplift modeling; causal inference; quantile regression |
| JEL: | C1 |
| Date: | 2023–08–04 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:122895 |
| By: | Sho Miyaji |
| Abstract: | In this paper, we formalize a triple instrumented difference-in-differences (DID-IV). In this design, a triple Wald-DID estimand, which divides the difference-in-difference-in-differences (DDD) estimand of the outcome by the DDD estimand of the treatment, captures the local average treatment effect on the treated. The identifying assumptions mainly comprise a monotonicity assumption, and the common acceleration assumptions in the treatment and the outcome. We extend the canonical triple DID-IV design to staggered instrument cases. We also describe the estimation and inference in this design in practice. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.14405 |
| By: | Cantone, Giulio Giacomo; Tomaselli, Venera |
| Abstract: | Multiverse analysis is a paradigm for estimation of the uncertainty regarding the veracity of a scientific claim, through a systemic not random sampling of a massive set of specifications of a model, which is the multiverse. Specifications, once fit on a sample, result in statistics. Observation of the variability of result statistics across groups of specifications is considered useful for checking the robustness of the claim or for better understanding its premises. However, the assumptions behind these procedures are not explicit and not always univocal: generally, the proprieties of a multiversal sample hold uniformly only for non-parametric assumptions. A new formal categorisation of the analytical choices in modelling is proposed. It helps to make the assumption of the multiverse more transparent and to check the parametric assumption. These theories are applied to the panel dataset. The analytical process is documented from the design of the hypothesis to the computation of the distribution of estimates for the same generalised linear effect. The analysis highlights the sensitivity of the model to the estimation of fixed covariates in the panel and how these results are so sensitive to this decision to twist the estimates of the linear effect. In the conclusion, the theory of multiversal sampling is related to the debate on how to weigh a multiverse. |
| Date: | 2023–05–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:metaar:ukvw7_v1 |
| By: | Zhouyu Shen; Dacheng Xiu |
| Abstract: | In high-dimensional regressions with low signal-to-noise ratios, we assess the predictive performance of several prevalent machine learning methods. Theoretical insights show Ridge regression's superiority in exploiting weak signals, surpassing a zero benchmark. In contrast, Lasso fails to exceed this baseline, indicating its learning limitations. Simulations reveal that Random Forest generally outperforms Gradient Boosted Regression Trees when signals are weak. Moreover, Neural Networks with l2-regularization excel in capturing nonlinear functions of weak signals. Our empirical analysis across six economic datasets suggests that the weakness of signals, not necessarily the absence of sparsity, may be Lasso's major limitation in economic predictions. |
| JEL: | C45 C52 C53 C55 C58 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33421 |
| By: | Oliver De Jonghe; Daniel Lewis |
| Abstract: | We propose a new model in which relationship-specific supply and demand shocks are non-parametrically identified in bipartite data under mild assumptions. For example, separate heterogeneous supply shocks are identified for each firm to which a bank lends. We show that a simple estimator is consistent, derive its limiting distribution, and illustrate its performance in simulations. Using these methods, we identify the heterogeneous distributions of supply and demand shocks for thousands of banks and firms in 11 European countries using the Anacredit dataset. Our estimates characterize how both quantity and price elasticities, and thus supply and demand curves, have changed in those 11 markets in recent years. The shock distributions exhibit within-firm/bank heterogeneity that is not well-explained by conventional fixed effects approaches, which only capture between-firm/bank heterogeneity. This unexplained heterogeneity correlates strongly with economically meaningful relationship-level characteristics and macroeconomic policy measures. These results have important implications for policy, identification assumptions in empirical work, and modeling exercises. |
| Date: | 2025–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:08/25 |
| By: | Matteo Mogliani (BANQUE DE FRANCE); Florens Odendahl (BANCO DE ESPAÑA AND CEMFI) |
| Abstract: | The common choice of using a direct forecasting scheme implies that the individual predictions ignore information on their cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on direct density forecasts, predictive objects that are functions of several horizons (e.g. when constructing annual-average growth rates from quarter-on-quarter growth rates). To address this issue we propose using copulas to combine the individual h-step-ahead predictive distributions into one joint predictive distribution. Our method is particularly appealing to those for whom changing the direct forecasting specification is too costly. We use a Monte Carlo study to demonstrate that our approach leads to a better approximation of the true density than an approach that ignores the potential dependence. We show the superior performance of our method using several empirical examples, where we construct (i) quarterly forecasts using month-on-month direct forecasts, (ii) annual-average forecasts using monthly year-on-year direct forecasts, and (iii) annual-average forecasts using quarter-on-quarter direct forecasts. |
| Keywords: | joint predictive distribution, frequency transformation, path forecasts, cross-horizon dependence |
| JEL: | C53 C32 E37 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2511 |