|
on Econometrics |
| By: | Charles Shaw |
| Abstract: | The double/debiased machine learning (DML) framework has become a cornerstone of modern causal inference, allowing researchers to utilise flexible machine learning models for the estimation of nuisance functions without introducing first-order bias into the final parameter estimate. However, the choice of machine learning model for the nuisance functions is often treated as a minor implementation detail. In this paper, we argue that this choice can have a profound impact on the substantive conclusions of the analysis. We demonstrate this by presenting and comparing two distinct Distributional Instrumental Variable Local Average Treatment Effect (D-IV-LATE) estimators. The first estimator leverages standard machine learning models like Random Forests for nuisance function estimation, while the second is a novel estimator employing Kolmogorov-Arnold Networks (KANs). We establish the asymptotic properties of these estimators and evaluate their performance through Monte Carlo simulations. An empirical application analysing the distributional effects of 401(k) participation on net financial assets reveals that the choice of machine learning model for nuisance functions can significantly alter substantive conclusions, with the KAN-based estimator suggesting more complex treatment effect heterogeneity. These findings underscore a critical "caveat emptor". The selection of nuisance function estimators is not a mere implementation detail. Instead, it is a pivotal choice that can profoundly impact research outcomes in causal inference. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.12765 |
| By: | David Kohns; Tibor Szendrei |
| Abstract: | Crossing of fitted conditional quantiles is a prevalent problem for quantile regression models. We propose a new Bayesian modelling framework that penalises multiple quantile regression functions toward the desired non-crossing space. We achieve this by estimating multiple quantiles jointly with a prior on variation across quantiles, a fused shrinkage prior with quantile adaptivity. The posterior is derived from a decision-theoretic general Bayes perspective, whose form yields a natural state-space interpretation aligned with Time-Varying Parameter (TVP) models. Taken together our approach leads to a Quantile- Varying Parameter (QVP) model, for which we develop efficient sampling algorithms. We demonstrate that our proposed modelling framework provides superior parameter recovery and predictive performance compared to competing Bayesian and frequentist quantile regression estimators in simulated experiments and a real-data application to multivariate quantile estimation in macroeconomics. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13257 |
| By: | Doko Tchatoka, Firmin; Wang, Wenjie |
| Abstract: | Pretesting for exogeneity has become a routine in many empirical applications involving instrumental variables (IVs) to decide whether the ordinary least squares or IV-based method is appropriate. Guggenberger (2010a) shows that the second-stage test - based on the outcome of a Durbin-Wu-Hausman type pretest for exogeneity in the first stage - has extreme size distortion with asymptotic size equal to 1 when the standard asymptotic critical values are used, even under strong identification and conditional homoskedasticity. In this paper, we make the following contributions. First, we show that both conditional and unconditional on the data, standard wild bootstrap procedures are invalid for the two-stage testing and therefore are not viable solutions to such size-distortion problem. Second, we propose an identification-robust two-stage test statistic that switches between the OLS-based and the weak-IV-robust statistics. Third, we develop a size-adjusted wild bootstrap approach for our two-stage test that integrates specific wild bootstrap critical values with an appropriate size-adjustment method. We establish uniform validity of this procedure under conditional heteroskedasticity or clustering in the sense that the resulting tests achieve correct asymptotic size no matter the identification is strong or weak. |
| Keywords: | DWH Pretest; Shrinkage; Instrumental Variable; Asymptotic Size; Wild Bootstrap; Bonferroni-based Size-correction; Clustering. |
| JEL: | C12 C21 C26 |
| Date: | 2025–05–05 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125017 |
| By: | Jin Seo Cho (Yonsei University) |
| Abstract: | The current study investigates testing the mixture hypothesis of Poisson regression models using the likelihood ratio (LR) test. The motivation of the mixture hypothesis stems from the unobserved heterogeneity, and the null hypothesis of interest is that there is no unobserved heterogeneity in the data. Due to the nonstandard conditions described in the text, the LR test does not weakly converge to the standard chi-squared random variable under the null hypothesis. We derive its null limit distribution as a functional of the Hermite Gaussian process. Furthermore, we introduce a methodology to obtain the asymptotic critical values consistently. Finally, we conduct Monte Carlo experiments and compare the power of the LR test with the specification test developed by Lee (1986). |
| Keywords: | Mixture of Poisson Regression Models; Likelihood Ratio Test; Asymptotic Null Distribution; Gaussian Process. |
| JEL: | C12 C22 C32 C52 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-254 |
| By: | Susan Athey; Raj Chetty; Guido Imbens |
| Abstract: | Researchers increasingly have access to two types of data: (i) large observational datasets where treatment (e.g., class size) is not randomized but several primary outcomes (e.g., graduation rates) and secondary outcomes (e.g., test scores) are observed and (ii) experimental data in which treatment is randomized but only secondary outcomes are observed. We develop a new method to estimate treatment effects on primary outcomes in such settings. We use the difference between the secondary outcome and its predicted value based on the experimental treatment effect to measure selection bias in the observational data. Controlling for this estimate of selection bias yields an unbiased estimate of the treatment effect on the primary outcome under a new assumption that we term "latent unconfoundedness, " which requires that the same confounders affect the primary and secondary outcomes. Latent unconfoundedness weakens the assumptions underlying commonly used surrogate estimators. We apply our estimator to identify the effect of third grade class size on students’ outcomes. Estimated impacts on test scores using OLS regressions in observational school district data have the opposite sign of estimates from the Tennessee STAR experiment. In contrast, selection-corrected estimates in the observational data replicate the experimental estimates. Our estimator reveals that reducing class sizes by 25% increases high school graduation rates by 0.7 percentage points. Controlling for observables does not change the OLS estimates, demonstrating that experimental selection correction can remove biases that cannot be addressed with standard controls. |
| JEL: | C14 C21 C52 I26 J0 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33817 |
| By: | Ninon Moreau-Kastler (EU Tax Observatory, Paris School of Economics) |
| Abstract: | I propose a counterfactual approach to measure proportional treatment effects for staggered multiplicative difference-in-differences (DiD) models with Poisson Pseudo-Maximum Likelihood (PPML). Two-way fixed effect (TWFE) linear estimators do not recover DiD estimates in the presence of a staggered treatment. I show that the wrong comparisons problem extends to TWFE PPML. I provide evidence that robust estimators for the linear case do not naturally extend to PPML, as aggregation of lower-level effects is challenging in the non-linear case. In these settings, my proposed estimator recovers a quantity analogous to that in the canonical 2-by-2 TWFE PPML model: the percent change of the average. |
| Keywords: | PPML; difference-in-differences; ratio-of-ratios; staggered treatment |
| JEL: | C21 C23 F14 H26 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:dbp:wpaper:031 |
| By: | Victor Chernozhukov; Christian Hansen; Lingwei Kong; Weining Wang |
| Abstract: | Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about the potential degree of misspecification via a prior distribution and adopt a quasi-Bayesian approach for performing inference on structural parameters. We provide quasi-posterior concentration results, verify that quasi-posteriors can be used to obtain approximately optimal Bayesian decision rules under the maintained prior structure over misspecification, and provide a form of frequentist coverage results. We illustrate the approach through empirical examples where we obtain informative inference for structural objects allowing for substantial relaxations of the requirement that moment conditions hold exactly. |
| Date: | 2025–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:14/25 |
| By: | Christian Gourieroux; Quinlan Lee |
| Abstract: | We explore the issues of identification for nonlinear Impulse Response Functions in nonlinear dynamic models and discuss the settings in which the problem can be mitigated. In particular, we introduce the nonlinear autoregressive representation with Gaussian innovations and characterize the identified set. This set arises from the multiplicity of nonlinear innovations and transformations which leave invariant the standard normal density. We then discuss possible identifying restrictions, such as non-Gaussianity of independent sources, or identifiable parameters by means of learning algorithms, and the possibility of identification in nonlinear dynamic factor models when the underlying latent factors have different dynamics. We also explain how these identification results depend ultimately on the set of series under consideration. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13531 |
| By: | Michael Balzer |
| Abstract: | Researchers in urban and regional studies increasingly deal with spatial data that reflects geographic location and spatial relationships. As a framework for dealing with the unique nature of spatial data, various spatial regression models have been introduced. In this article, a novel model-based gradient boosting algorithm for spatial regression models with autoregressive disturbances is proposed. Due to the modular nature, the approach provides an alternative estimation procedure which is feasible even in high-dimensional settings where established quasi-maximum likelihood or generalized method of moments estimators do not yield unique solutions. The approach additionally enables data-driven variable and model selection in low- as well as high-dimensional settings. Since the bias-variance trade-off is also controlled in the algorithm, implicit regularization is imposed which improves prediction accuracy on out-of-sample spatial data. Detailed simulation studies regarding the performance of estimation, prediction and variable selection in low- and high-dimensional settings confirm proper functionality of the proposed methodology. To illustrative the functionality of the model-based gradient boosting algorithm, a case study is presented where the life expectancy in German districts is modeled incorporating a potential spatial dependence structure. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13682 |
| By: | Yasumasa Matsuda; Rei Iwafuchi |
| Abstract: | This paper proposes a novel framework for modeling time series of probability density functions by extending auto-regressive moving average(ARMA) models to density-valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0, 1]d is approximated by a B-spline mixture representation. Through generalized logit and softmax mappings, the space of density functions is transformed into an unconstrained Euclidean space, enabling the application of classical time series techniques. We define ARMA-type dynamics in the transformed space. Estimation is carried out via least squares for density-valued AR models and Whittle likelihood for ARMA models, with asymptotic normality derived under the joint divergence of the time horizon and basis dimension. The proposed methodology is applied to spatio-temporal human population data in Tokyo, where meaningful temporal structures in the distributional dynamics are successfully captured. |
| Date: | 2025–06–23 |
| URL: | https://d.repec.org/n?u=RePEc:toh:dssraa:146 |
| By: | Astill, Sam; Magdalinos, Tassos; Taylor, AM Robert |
| Abstract: | We address the sensitivity of asset return predictability tests to the initial conditions of predictors. The IVX test of Kostakis et al. (2015, Review of Financial Studies) assumes asymptotically negligible initial conditions, which we show can result in large power losses for strongly persistent predictors. We propose a modified test that initialises the instruments at estimates of the predictors’ initial conditions, enhancing robustness and detection power. Additionally, a hybrid test is introduced, combining the strengths of the original and modified tests to deliver robust performance across varying magnitude initial conditions. Empirical and simulation results demonstrate the effectiveness of these approaches in improving predictability testing. |
| Keywords: | predictive regression; returns; initial condition; unknown regressor persistence; instrumental variable; hybrid tests |
| Date: | 2025–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:esy:uefcwp:41209 |
| By: | Jin Seo Cho (Yonsei University) |
| Abstract: | The current study provides the Gaussian versions used to test for normal mixtures. These versions are highly practical as they can directly be used to simulate the asymptotic critical values of standard tests, for example the likelihood-ratio or Lagrange multiplier tests. We investigate testing for two normal mixtures: one having a single variance and two distinct means, and another having a single mean and two different variances. We derive the Gaussian versions for the two models by associating the score functions with the Hermite and generalized Laguerre polynomials, respectively. Additionally, we compare the performance of the likelihood-ratio and Lagrange multiplier tests using the asymptotic critical values. |
| Keywords: | Gaussian version; LR test; LM test; Hermite polynomial; Generalized Laguerre polynomial. |
| JEL: | C12 C46 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-248 |
| By: | Ulrich Doraszelski; Lixiong Li |
| Abstract: | We advance the proxy variable approach to production function estimation. We show that the invertibility assumption at its heart is testable. We characterize what goes wrong if invertibility fails and what can still be done. We show that rethinking how the estimation procedure is implemented either eliminates or mitigates the bias that arises if invertibility fails. Furthermore, we show how a modification of the procedure ensures Neyman orthogonality, enhancing efficiency and robustness by rendering the asymptotic distribution of the GMM estimator in the second step of the estimation procedure invariant to estimation noise from the first step. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13520 |
| By: | Heng Chen; John Tsang |
| Abstract: | We develop statistical inferences for a non-probability two-phase survey sample when relevant auxiliary information is available from a probability survey sample. To reduce selection bias and gain efficiency, both selection probabilities of Phase 1 and Phase 2 are estimated, and two-phase calibration is implemented. We discuss both analytical plug-in and pseudo-population bootstrap variance estimation methods that account for the effects of using estimated selection probabilities and calibrated weights. The proposed method is assessed by simulation studies and used to analyze a non-probability two phase payment survey. |
| Keywords: | Bank notes; Econometric and statistical methods |
| JEL: | C C8 C83 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:25-17 |
| By: | Jochmans, Koen |
| Abstract: | This paper is concerned with models for matched worker-firm data in the presence of both worker and firm heterogeneity. We show that models with complementarity and sorting can be nonparametrically identified from short panel data while treating both worker and firm heterogeneity as discrete random effects. This paradigm is different from the framework of Bonhomme, Lamadon and Manresa (2019), where identification results are derived under the assumption that worker effects are random but firm heterogeneity is observed. The latter assumption requires the ability to consistently assign firms to latent clusters, which may be challenging; at a minimum, it demands minimal firm size to grow without bound. Our setup is compatible with many theoretical specifications and our approach is constructive. Our identification results appear to be the first of its kind in the context of matched panel data problems. |
| Keywords: | bipartite graph; nonlinearity; panel data; sorting; unobserved heterogeneity |
| JEL: | C23 J31 J62 |
| Date: | 2025–06–26 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130607 |
| By: | Aristide Houndetoungan |
| Abstract: | I propose a flexible structural model to estimate peer effects across various quantiles of the peer outcome distribution. The model allows peers with low, intermediate, and high outcomes to exert distinct influences, thereby capturing more nuanced patterns of peer effects than standard approaches that are based on aggregate measures. I establish the existence and uniqueness of the Nash equilibrium and demonstrate that the model parameters can be estimated using a straightforward instrumental variable strategy. Applying the model to a range of outcomes that are commonly studied in the literature, I uncover diverse and rich patterns of peer influences that challenge assumptions inherent in standard models. These findings carry important policy implications: key player status in a network depends not only on network structure, but also on the distribution of outcomes within the population. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.12920 |
| By: | Li, Mengheng; Mendieta-Munoz, Ivan |
| Abstract: | We propose a factor correlated unobserved components (FCUC) model to analyze the sticky and flexible components of U.S. inflation. The proposed FCUC framework estimates trend inflation and component cycles in a flexible stochastic environment with time-varying volatility, factor loadings, and cross-frequency (trend-cycle) correlations, thus capturing how structural heterogeneity in price adjustment shapes the evolution of aggregate trend inflation over time. Using Bayesian estimation methods, we show that the FCUC model substantially reduces the uncertainty surrounding estimates of trend inflation and improves both point and density forecast accuracy. Our findings reveal that, particularly following the Global Financial Crisis and more markedly since the COVID-19 recession, transitory price shocks originating from flexible inflation have become a major driver of trend inflation, whereas sticky inflation explains only part of the variation. These results indicate that temporary price movements can have persistent effects, highlighting important policy implications regarding the cyclical dynamics of disaggregated inflation components amid evolving macroeconomic conditions. |
| Keywords: | trend inflation, sticky inflation, flexible inflation, stochastic volatility, dynamic factor model |
| JEL: | C32 C53 E37 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:320299 |
| By: | Quinlan Lee, Stephen Snudden |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:wlu:lcerpa:jc0157 |