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on Econometrics |
| By: | Dzemski, Andreas (Department of Economics, School of Business, Economics and Law, Göteborg University); Okui, Ryo (Faculty of Economics, the University of Tokyo); Wang, Wenjie (Division of Economics, School of Social Sciences, Nanyang Technological University) |
| Abstract: | Significant treatment effects are often emphasized when interpreting and summarizing empirical findings in studies that estimate multiple, possibly many, treatment effects. Under this kind of selective reporting, conventional treatment effect estimates may be biased and their corresponding confidence intervals may undercover the true effect sizes. We propose new estimators and confidence intervals that provide valid inferences on the effect sizes of the significant effects after multiple hypothesis testing. Our methods are based on the principle of selective conditional inference and complement a wide range of tests, including step-up tests and bootstrap-based step-down tests. Our approach is scalable, allowing us to study an application with over 370 estimated effects. We justify our procedure for asymptotically normal treatment effect estimators. We provide two empirical examples that demonstrate bias correction and confidence interval adjustments for significant effects. The magnitude and direction of the bias correction depend on the correlation structure of the estimated effects and whether the interpretation of the significant effects depends on the (in)significance of other effects. |
| Keywords: | Multiple hypothesis testing; post-selection inference; conditional inference; bias correction |
| JEL: | C12 C52 |
| Date: | 2025–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:gunwpe:0852 |
| By: | Ali Mehrabani (Department of Economics, University of Kansas, Lawrence, KS 66045) |
| Abstract: | Two stein-like shrinkage estimators are introduced to modify the 2SLS and the LIML estimators for coefficients of a single equation in a simultaneous system of equations. The proposed estimators are weighted averages of the 2SLS/LIML estimators and the OLS estimator. The shrinkage weight depends on the Wu-Hausman misspecification test statistic which evaluates the null of exogeneity against the alternative hypothesis of endogeneity. The approximate finite sample bias, mean squared errors, and density functions of the Stein-like shrinkage estimators are obtained using small-disturbance approximations. The dominance conditions of the Stein-like shrinkage estimators over the 2SLS/LIML estimator under the mean squared error and the concentration probability are obtained. The proposed method is further illustrated by simulation studies which demonstrate the good finite sample performance of the method, and is also applied to an empirical application of returns to education. |
| Keywords: | Stein-like Estimator; Small-Disturbance Approximations; Simultaneous Equation Models; OLS; 2SLS; LIML. |
| JEL: | C13 C26 C52 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202414 |
| By: | Helmut Lütkepohl; Till Strohsal |
| Abstract: | This paper analyzes possibly time-varying shock transmission in structural vector autoregressive (VAR) models when the reduced-form VAR coefficients are time-invariant and the shocks are identified through non-Gaussianity. To check for possible time-variation in the impulse responses, we propose Wald tests for two situations: (1) homoskedastic and (2) heteroskedastic structural shocks. For the latter case, the challenge is to ensure that the test does not indicate time-varying impulse responses if the changes are due only to changes in the variances of the shocks. To illustrate the usefulness of the tests, they are applied to an empirical model of the crude oil market. They support time-varying shock transmission reflected in impulse response functions that change over time. |
| Keywords: | Structural vector autoregression, independent component analysis, non-Gaussian shocks, structural break tests, heteroskedasticity |
| JEL: | C32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2110 |
| By: | Lutz Kilian |
| Abstract: | Structural impulse response functions may be estimated based on priors about the parameters of the structural VAR presentation. Even when such priors appear seemingly reasonable, they may imply an unintentionally informative prior for the structural impulse responses. Rather than pretending that the posterior of the impulse responses does not depend on this prior, the proposal in this paper is to verify that the prior distribution of the vector of impulse responses of interest is not unintentionally informative. Moreover, if the impulse response prior is intentionally informative, this point must be conveyed, so the reader can properly evaluate the reported conclusions. This paper discusses easy-to-use diagnostic tools that help practitioners address these concerns. |
| Keywords: | VAR; prior; posterior; impulse response; inference |
| JEL: | C11 C32 C52 Q43 |
| Date: | 2025–02–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:99620 |
| By: | Yuying Sun (School of Economics and Management, University of Chinese Academy of Sciences and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China); Shaoxin Hong (Center for Economic Research, Shandong University, Jinan, Shandong 250100, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA) |
| Abstract: | This paper proposes a novel state-varying model averaging prediction for varyingcoefficient models that accounts for parameter uncertainty and model misspecification. We develop a leave-h-out state-dependent forward-validation criterion to select state-varying combination weights. It is shown that the proposed averaging prediction is asymptotically optimal in the sense of achieving the lowest possible out-of-sample prediction risk in a class of model averaging estimators. This complements existing model averaging methods that primarily focus on minimizing the in-sample squared error loss. Besides, when the set of candidate models includes correctly specified models, the proposed approach asymptotically assigns full weight to these models. Furthermore, the proposed approach is flexible and encompasses special cases including ultra-high dimensional models as well as state-varying factor-augmented regression models. Simulation studies and empirical applications highlight the merits of the proposed averaging prediction relative to other existing model averaging and model selection methods. |
| Keywords: | Asymptotic optimality; Varying-coefficient models; Forward-validation; Model averaging; Weight convergence |
| JEL: | C2 C13 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202507 |
| By: | Kirill Borusyak; Peter Hull; Mauricio Caceres Bravo |
| Abstract: | We develop a new approach to estimating flexible demand models with exogenous supply-side shocks. Our approach avoids conventional assumptions of exogenous product characteristics, putting no restrictions on product entry, despite using instrumental variables that incorporate characteristic variation. The proposed instruments are model-predicted responses of endogenous variables to the exogenous shocks, recentered to avoid bias from endogenous characteristics. We illustrate the approach in a series of Monte Carlo simulations. |
| Date: | 2025–04–07 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:10/25 |
| By: | Abe, Makoto (Faculty of Economics, The University of Tokyo) |
| Abstract: | In consumer research and psychological experiments, subjects' states (attitudes) are manipulated by means of stimulus treatment in order to examine the effects of the subjects' states (attitudes) on the target variable. The interest here is not the effect of the treatment (stimulus) itself, but the effect on the target variable of the difference in state produced as a result of the treatment. Therefore, a manipulation check is usually performed to establish the validity of the experimental design, i.e., whether the stimulus produced the intended difference in state. When the manipulation-check variable (state) is directly associated with the target variable, one encounters the problem of confounding that affects both variables. To eliminate this problem, randomized controlled trials (RCTs) are used, but two weaknesses exist: first, only a discrete, binary effect of the presence or absence of an treatment on the target variable can be uncovered. Second, the incompleteness of the experimental design, in which the state induced by the treatment (stimulus) varies from subject to subject, resulting in different effects on the target variable, cannot be taken into account. In this study, we propose an approach that can correctly estimate the effect, which relates the manipulation-check variable to the target variable, even when unobserved confounding factors are present. By accounting for imperfections in the experimental design, the effect of the state variable becomes statistically more efficient than the effect of the experimental approach. The simulation analysis confirms that, for the same sample size, our instrumental variable approach is more significant than the usual experimental approach. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:tky:jseres:2025cj312 |
| By: | Klinenberg, Danny; Berman, Eli; Klor, Esteban |
| Abstract: | Many applied economic studies aim to estimate strategic behavior through reaction curves. Examples include two-sided conflicts, or economic trade wars, and algorithmic pricing between firms. Analysis is usually performed at a prespecified time interval, such as days, weeks, months, or years, using a vector autoregression (VAR). Yet sides may respond within a day to one action, but wait a month after another. If data is recorded in arbitrary time intervals, then the researcher may mistake waiting to act for inaction. We analytically show that VAR analyses do not recover true reaction curves if the timing of reaction is not accurately recorded. This misspecification can cause the sign of the VAR coefficient to reverse and misspecified standard errors leading to erroneous inference. We discuss an alternative structural approach rooted in game theory to estimate reaction curves and investigate its usefulness in a Monte Carlo simulation. |
| Keywords: | Social and Behavioral Sciences, conflict, game theory, response time, econometrics, vector autoregression, reaction curve |
| Date: | 2025–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:globco:qt50087695 |