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on Econometrics |
| By: | Antonio F. Galvao; Carlos Lamarche; Thomas Parker |
| Abstract: | Practical inference procedures for quantile regression models of panel data have been a pervasive concern in empirical work, and can be especially challenging when the panel is observed over many time periods and temporal dependence needs to be taken into account. In this paper, we propose a new bootstrap method that applies random weighting to a partition of the data -- partition-invariant weights are used in the bootstrap data generating process -- to conduct statistical inference for conditional quantiles in panel data that have significant time-series dependence. We demonstrate that the procedure is asymptotically valid for approximating the distribution of the fixed effects quantile regression estimator. The bootstrap procedure offers a viable alternative to existing resampling methods. Simulation studies show numerical evidence that the novel approach has accurate small sample behavior, and an empirical application illustrates its use. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.18494 |
| By: | Alessandro Casini (DEF, University of Rome "Tor Vergata"); Adam McCloskey (University of Colorado at Boulder) |
| Abstract: | We consider identification, estimation and inference in high-frequency event study regressions, which have been used widely in the recent macroeconomics, financial economics and political economy literatures. The high-frequency event study method regresses changes in an outcome variable on a measure of unexpected changes in a policy variable in a narrow time window around an event or a policy announcement (e.g., a 30-minute window around an FOMC announcement). We show that, contrary to popular belief, the narrow size of the window is not sufficient for identification. Rather, the population regression coefficient identifies a causal estimand when (i) the effect of the policy shock on the outcome does not depend on the other variables (separability) and (ii) the surprise component of the news or event dominates all other variables that are present in the event window (relative exogeneity). Technically, the latter condition requires the ratio between the variance of the policy shock and that of the other variables to be infinite in the event window. Under these conditions, we establish the causal meaning of the event study estimand corresponding to the regression coefficient and super-consistency of the event study estimator with rate of convergence faster than the parametric rate. We show the asymptotic normality of the estimator and propose bias-corrected inference. We also provide bounds on the worst-case bias and use them to quantify its impact on the worst-case coverage properties of confidence intervals, as well as to construct a bias-aware critical value. Notably, this standard linear regression estimator is robust to general forms of nonlinearity. We apply our results to Nakamura and Steinsson’s (2018a) analysis of the real economic effects of monetary policy, providing a simple empirical procedure to analyze the extent to which the standard event study estimator adequately estimates causal effects of interest. |
| Keywords: | Causal effects, Event study, High-frequency data, Identification |
| JEL: | C32 C51 |
| Date: | 2025–07–28 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:608 |
| By: | Kurisu, Daisuke; Otsu, Taisuke |
| Abstract: | There has been growing interest in statistical analysis of random objects taking values in a non-Euclidean metric space. One important class of such objects consists of data on manifolds. This article is concerned with inference on the Fréchet mean and related population objects on manifolds. We develop the concept of nonparametric likelihood for data on manifolds and propose general inference methods by adapting the theory of empirical likelihood. In addition to the basic asymptotic properties, such as Wilks’ theorem of the empirical likelihood statistic, we present several generalizations of the proposed methodology: two-sample testing, inference on the Fréchet variance, quasi-Bayesian inference, local Fréchet regression, and estimation of the Fréchet mean set. Simulation and real data examples illustrate the usefulness of the proposed methodology and its advantage against the conventional Wald test. |
| Keywords: | empirical likelihood; generalized Fréchet mean; manifold; smeariness |
| JEL: | J1 C1 |
| Date: | 2025–07–18 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128293 |
| By: | Daniele Massacci; Lucio Sarno; Lorenzo Trapani; Pierluigi Vallarino |
| Abstract: | We propose a methodology to construct tests for the null hypothesis that the pricing errors of a panel of asset returns are jointly equal to zero in a linear factor asset pricing model -- that is, the null of "zero alpha". We consider, as a leading example, a model with observable, tradable factors, but we also develop extensions to accommodate for non-tradable and latent factors. The test is based on equation-by-equation estimation, using a randomized version of the estimated alphas, which only requires rates of convergence. The distinct features of the proposed methodology are that it does not require the estimation of any covariance matrix, and that it allows for both N and T to pass to infinity, with the former possibly faster than the latter. Further, unlike extant approaches, the procedure can accommodate conditional heteroskedasticity, non-Gaussianity, and even strong cross-sectional dependence in the error terms. We also propose a de-randomized decision rule to choose in favor or against the correct specification of a linear factor pricing model. Monte Carlo simulations show that the test has satisfactory properties and it compares favorably to several existing tests. The usefulness of the testing procedure is illustrated through an application of linear factor pricing models to price the constituents of the S&P 500. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.17599 |
| By: | Xu Cheng (University of Pennsylvania); Sheng Chao Ho (Singapore Management University); Frank Schorfheide (University of Pennsylvania) |
| Abstract: | We propose an empirical Bayes estimator for two-way effects in linked data sets based on a novel prior that leverages patterns of assortative matching observed in the data. To capture limited mobility we model the bipartite graph associated with the matched data in an asymptotic framework where its Laplacian matrix has small eigenvalues that converge to zero. The prior hyperparameters that control the shrinkage are determined by minimizing an unbiased risk estimate. We show the proposed empirical Bayes estimator is asymptotically optimal in compound loss, despite the weak connectivity of the bipartite graph and the potential misspecification of the prior. We estimate teacher values-added from a linked North Carolina Education Research Data Center student-teacher data set |
| Keywords: | Bipartite Graphs, Compound Loss, Empirical Bayes Methods, Limited Mobility, Matched Data, Shrinkage Estimation, Teacher Value-Added, Two-Way Fixed Effects, Unbiased Risk Estimation, Weak Identification |
| JEL: | C11 C13 C23 C55 I21 |
| Date: | 2025–06–27 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:25-013 |
| By: | Simon Freyaldenhoven; Christian Hansen |
| Abstract: | We consider point estimation and inference for the treatment effect path of a policy. Examples include dynamic treatment effects in microeconomics, impulse response functions in macroeconomics, and event study paths in finance. We present two sets of plausible bounds to quantify and visualize the uncertainty associated with this object. Both plausible bounds are often substantially tighter than traditional confidence intervals, and can provide useful insights even when traditional (uniform) confidence bands appear uninformative. Our bounds can also lead to markedly different conclusions when there is significant correlation in the estimates, reflecting the fact that traditional confidence bands can be ineffective at visualizing the impact of such correlation. Our first set of bounds covers the average (or overall) effect rather than the entire treatment path. Our second set of bounds imposes data-driven smoothness restrictions on the treatment path. Post-selection Inference (Berk et al. [2013]) provides formal coverage guarantees for these bounds. The chosen restrictions also imply novel point estimates that perform well across our simulations. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.12014 |
| By: | Xu Cheng (University of Pennsylvania); Frank Schorfheide (University of Pennsylvania); Peng Shao (Auburn University) |
| Abstract: | This paper studies the estimation of multi-dimensional heterogeneous parameters in a nonlinear panel data model with endogeneity. These heterogeneous parameters are modeled with group patterns. Through estimating multiple memberships for each unit, the proposed method is robust to limited information from a subset of clusters: either due to sparse interactions of characteristics or weak identification of some combinations of heterogeneous parameters. We estimate the memberships along with the group specific and common parameters in a nonlinear GMM framework and derive their large sample properties. Finally, we apply this approach to the estimation of heterogeneous firm-level production functions parameters which are converted into markup estimates. |
| Keywords: | Clustering, GMM, K-means, Panel Data, Production Function Estimation |
| JEL: | C13 C23 D22 D24 E23 |
| Date: | 2025–06–19 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:25-014 |
| By: | Bo Hu; Joon Y. Park; Junhui Qian |
| Abstract: | This paper introduces a novel approach to investigate the dynamics of state distributions, which accommodate both cross-sectional distributions of repeated panels and intra-period distributions of a time series observed at high frequency. In our approach, densities of the state distributions are regarded as functional elements in a Hilbert space, and are assumed to follow a functional autoregressive model. We propose an estimator for the autoregressive operator, establish its consistency, and provide tools and asymptotics to analyze the forecast of state density and the moment dynamics of state distributions. We apply our methodology to study the time series of distributions of the GBP/USD exchange rate intra-month returns and the time series of cross-sectional distributions of the NYSE stocks monthly returns. Finally, we conduct simulations to evaluate the density forecasts based on our model. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.15763 |
| By: | Felix Reichel |
| Abstract: | In this paper, we investigate the impact of outliers on the statistical significance of coefficients in linear regression. We demonstrate, through numerical simulation using R, that a single outlier can cause an otherwise insignificant coefficient to appear statistically significant. We compare this with robust Huber regression, which reduces the effects of outliers. Afterwards, we approximate the influence of a single outlier on estimated regression coefficients and discuss common diagnostic statistics to detect influential observations in regression (e.g., studentized residuals). Furthermore, we relate this issue to the optional normality assumption in simple linear regression [14], required for exact finite-sample inference but asymptotically justified for large n by the Central Limit Theorem (CLT). We also address the general dangers of relying solely on p-values without performing adequate regression diagnostics. Finally, we provide a brief overview of regression methods and discuss how they relate to the assumptions of the Gauss-Markov theorem. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.10738 |
| By: | Anna Bykhovskaya; Vadim Gorin; Sasha Sodin |
| Abstract: | The paper analyzes four classical signal-plus-noise models: the factor model, spiked sample covariance matrices, the sum of a Wigner matrix and a low-rank perturbation, and canonical correlation analysis with low-rank dependencies. The objective is to construct confidence intervals for the signal strength that are uniformly valid across all regimes - strong, weak, and critical signals. We demonstrate that traditional Gaussian approximations fail in the critical regime. Instead, we introduce a universal transitional distribution that enables valid inference across the entire spectrum of signal strengths. The approach is illustrated through applications in macroeconomics and finance. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.18554 |
| By: | Matthias R. Fengler (University of St. Gallen - SEPS: Economics and Political Sciences; Swiss Finance Institute); Bruno Jäger (Eastern Switzerland University of Applied Sciences); Ostap Okhrin (Dresden University of Technology) |
| Abstract: | We study local change-point detection in variance using generalized likelihood ratio tests. Building on Suvorikova & Spokoiny (2017), we utilize the multiplier bootstrap to approximate the unknown, non-asymptotic distribution of the test statistic and introduce a multiplicative bias correction that improves upon the existing additive version. This proposed correction offers a clearer interpretation of the bootstrap estimators while significantly reducing computational costs. Simulation results demonstrate that our method performs comparably to the original approach. We apply it to the growth rates of U.S. inflation, industrial production, and Bitcoin returns. |
| Keywords: | generalized likelihood ratio test, multiplier bootstrap, local change point detection, economic and financial variance |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2560 |
| By: | Chen, Fangyi; Chen, Yunxiao; Ying, Zhiliang; Zhou, Kangjie |
| Abstract: | Summary: Recurrent event time data arise in many studies, including in biomedicine, public health, marketing and social media analysis. High-dimensional recurrent event data involving many event types and observations have become prevalent with advances in information technology. This article proposes a semiparametric dynamic factor model for the dimension reduction of high-dimensional recurrent event data. The proposed model imposes a low-dimensional structure on the mean intensity functions of the event types while allowing for dependencies. A nearly rate-optimal smoothing-based estimator is proposed. An information criterion that consistently selects the number of factors is also developed. Simulation studies demonstrate the effectiveness of these inference tools. The proposed method is applied to grocery shopping data, for which an interpretable factor structure is obtained. |
| Keywords: | counting process; factor analysis; marginal modelling; kernal smoothing; information criterion |
| JEL: | C1 |
| Date: | 2025–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127778 |
| By: | Xiaohong Chen (Yale University); Wayne Yuan Gao (University of Pennsylvania) |
| Abstract: | This paper studies the semiparametric estimation and inference of integral functionals on submanifolds, which arise naturally in a variety of econometric settings. For linear integral functionals on a regular submanifold, we show that the semiparametric plugin estimator attains the minimax-optimal convergence rate n-s/2s+d-m, where s is the Holder smoothness order of the underlying nonparametric function, d is the dimension of the first-stage nonparametric estimation, m is the dimension of the submanifold over which the integral is taken. This rate coincides with the standard minimax-optimal rate for a (d-m)-dimensional nonparametric estimation problem, illustrating that integration over the m-dimensional manifold effectively reduces the problemÕs dimensionality. We then provide a general asymptotic normality theorem for linear/nonlinear submanifold integrals, along with a consistent variance estimator. We provide simulation evidence in support of our theoretical results. |
| Date: | 2025–07–18 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2450 |
| By: | Bas Sanders |
| Abstract: | Economists use quantitative trade and spatial models to make counterfactual predictions. Because such predictions often inform policy decisions, it is important to communicate the uncertainty surrounding them. Three key challenges arise in this setting: the data are dyadic and exhibit complex dependence; the number of interacting units is typically small; and counterfactual predictions depend on the data in two distinct ways-through the estimation of structural parameters and through their role as inputs into the model's counterfactual equilibrium. I address these challenges by proposing a new Bayesian bootstrap procedure tailored to this context. The method is simple to implement and provides both finite-sample Bayesian and asymptotic frequentist guarantees. Revisiting the results in Waugh (2010), Caliendo and Parro (2015), and Artu\c{c} et al. (2010) illustrates the practical advantages of the approach. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.11967 |
| By: | McKenzie, David |
| Abstract: | This paper offers practical advice on how to improve statistical power in randomized experiments through choices and actions researchers can take at the design, implementation, and analysis stages. At the design stage, the choice of estimand, choice of treatment, and decisions that affect the residual variance and intra-cluster correlation can all affect power for a given sample size. At the implementation stage, researchers can boost power through increasing compliance with treatment, reducing attrition, and improving outcome measurement. At the analysis stage, power can be increased through using different test statistics or estimands, through the choice of control variables, and through incorporating informative priors in a Bayesian analysis. A key message is that it does not make sense to talk of “the” power of an experiment. A study can be well-powered for one outcome or estimand, but not others, and a fixed sample size can yield very different levels of power depending on researcher decisions. |
| Date: | 2025–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11176 |
| By: | Bas Donkers; Kamel Jedidi; Miłosz Kadziński; Mohammad Ghaderi |
| Abstract: | We introduce the Random Preference Model (RPM), a non-parametric and flexible discrete choice model. RPM is a rank-based stochastic choice model where choice options have multi-attribute representations. It takes preference orderings as the main primitive and models choices directly based on a distribution over partial or complete preference orderings over a finite set of alternatives. This enables it to capture context-dependent behaviors while maintaining adherence to the regularity axiom. In its output, it provides a full distribution over the entire preference parameter space, accounting for inferential uncertainty due to limited data. Each ranking is associated with a subspace of utility functions and assigned a probability mass based on the expected log-likelihood of those functions in explaining the observed choices. We propose a two-stage estimation method that separates the estimation of ranking-level probabilities from the inference of preference parameters variation for a given ranking, employing Monte Carlo integration with subspace-based sampling. To address the factorial complexity of the ranking space, we introduce scalable approximation strategies: restricting the support of RPM to a randomly sampled or orthogonal basis subset of rankings and using partial permutations (top-k lists). We demonstrate that RPM can effectively recover underlying preferences, even in the presence of data inconsistencies. The experimental evaluation based on real data confirms RPM variants consistently outperform multinomial logit (MNL) in both in-sample fit and holdout predictions across different training sizes, with support-restricted and basis-based variants achieving the best results under data scarcity. Overall, our findings demonstrate RPM's flexibility, robustness, and practical relevance for both predictive and explanatory modeling. |
| Keywords: | choice models, context-dependent preference, nonparametric modeling, random utility, rankings |
| JEL: | C35 C14 C15 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1502 |
| By: | Tomislav Ladika (University of Amsterdam); Elisa Pazaj (University of Amsterdam); Zacharias Sautner (University of Zurich - Department of Finance; Swiss Finance Institute; European Corporate Governance Institute (ECGI)) |
| Abstract: | A wide range of empirical techniques cannot accurately estimate causal effects of policy events due to anticipation bias-agents making decisions based on beliefs about future policy outcomes. We show how researchers can refine estimates to account for these beliefs, by integrating reduced-form and structural estimation around observed outcomes of a single policy change. We illustrate the importance and implementation of the approach by applying it to the Paris Agreement, which is frequently used to understand how agents respond to a policy event that increased climate regulatory risk. We document that anticipation can bias both the magnitude and sign of the Paris Agreement's average treatment effect on firm outcomes (estimated from a standard model such as a difference-in-differences regression). We offer concrete guidance on how to account for the divergence between causal and estimated effects. |
| Keywords: | Anticipation effects, reduced-form estimation, structural estimation, carbon tax, Climate finance |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2559 |
| By: | Papp, Tamás K. (Institute for Advanced Studies, Vienna) |
| Abstract: | We propose a flexible, extensible family of distributions for testing Markov Chain Monte Carlo implementations. Distributions are created by nesting simple transformations, which allow various shapes, includingmultiplemodes and fat tails. The resulting distributions can be sampled with high precision using quasi-random sequences, and have closed form (log) density and gradient at each point, making it possible to test gradient-based samplers without automatic differentiation. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:ihs:ihswps:number60 |
| By: | Marcell T. Kurbucz; Nikolaos Tzivanakis; Nilufer Sari Aslam; Adam M. Sykulski |
| Abstract: | Capturing nonlinear relationships without sacrificing interpretability remains a persistent challenge in regression modeling. We introduce SplitWise, a novel framework that enhances stepwise regression. It adaptively transforms numeric predictors into threshold-based binary features using shallow decision trees, but only when such transformations improve model fit, as assessed by the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC). This approach preserves the transparency of linear models while flexibly capturing nonlinear effects. Implemented as a user-friendly R package, SplitWise is evaluated on both synthetic and real-world datasets. The results show that it consistently produces more parsimonious and generalizable models than traditional stepwise and penalized regression techniques. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.15423 |
| By: | Roy Cequeti (GRANEM - Groupe de Recherche Angevin en Economie et Management - UA - Université d'Angers - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement, UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome]); Pierpaolo D’urso (UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome]); Raffaele Mattera (Università degli studi della Campania "Luigi Vanvitelli" = University of the Study of Campania Luigi Vanvitelli) |
| Abstract: | The paper discusses the problem of estimating group heterogeneous fixed-effect panel data models under the assumption of fuzzy clustering, that is each unit belongs to all the clusters with a membership degree. To enhance spatial clustering, a spatio-temporal approach is considered. An iterative procedure, alternating panel data estimation and spatio-temporal clustering of the residuals, is proposed. The proposed method can be of relevance to researchers interested in using fuzzy group fixed-effect methods, but want to leverage spatial dimension for clustering units. Two empirical examples, the first on cigarette consumption in the US states and the second on non-life insurance demand in Italy, are presented to illustrate the performance of the proposed approach. The spatial fuzzy GFE model reveals important regional differences in both the US cigarette consumption and non-life insurance determinants in Italy. In the case of the US, we found a distinction in two main clusters, East and West. For the Italy provinces data, we find a distinction in North and South clusters. Regarding the regression results, for cigarette consumption data, different from the previous studies, we find that the smuggling effect is significant only in east regions, thus suggesting localised impacts of bootlegging. In the context of Italian non-life insurance demand, we find that while population density explains insurance consumption in northern provinces, the trust issues in the south explain the lower insurance demand. |
| Keywords: | Longitudinal data, Clusterwise regression, Grouped fixed effect, Fuzzy approach, Heterogeneous coefficient, Spatial econometrics |
| Date: | 2025–01–03 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05109570 |
| By: | Xinyu Dai |
| Abstract: | In this study, I investigate the dynamic decision problem with a finite parameter space when the functional form of conditional expected rewards is misspecified. Traditional algorithms, such as Thompson Sampling, guarantee neither an $O(e^{-T})$ rate of posterior parameter concentration nor an $O(T^{-1})$ rate of average regret. However, under mild conditions, we can still achieve an exponential convergence rate of the parameter to a pseudo truth set, an extension of the pseudo truth parameter concept introduced by White (1982). I further characterize the necessary conditions for the convergence of the expected posterior within this pseudo-truth set. Simulations demonstrate that while the maximum a posteriori (MAP) estimate of the parameters fails to converge under misspecification, the algorithm's average regret remains relatively robust compared to the correctly specified case. These findings suggest opportunities to design simple yet robust algorithms that achieve desirable outcomes even in the presence of model misspecifications. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.14913 |
| By: | Connor Lennon; Edward Rubin; Glen Waddell |
| Abstract: | Machine learning (ML) primarily evolved to solve "prediction problems." The first stage of two-stage least squares (2SLS) is a prediction problem, suggesting potential gains from ML first-stage assistance. However, little guidance exists on when ML helps 2SLS$\unicode{x2014}$or when it hurts. We investigate the implications of inserting ML into 2SLS, decomposing the bias into three informative components. Mechanically, ML-in-2SLS procedures face issues common to prediction and causal-inference settings$\unicode{x2014}$and their interaction. Through simulation, we show linear ML methods (e.g., post-Lasso) work well, while nonlinear methods (e.g., random forests, neural nets) generate substantial bias in second-stage estimates$\unicode{x2014}$potentially exceeding the bias of endogenous OLS. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.13422 |
| By: | Roberto Casarin (Ca' Foscari University of Venice); Antonio Peruzzi (Ca' Foscari University of Venice); Davide Raggi (Ca' Foscari University of Venice) |
| Abstract: | We study a New Keynesian Phillips curve in which agents deviate from the rational expectation paradigm and forecast inflation using a simple, potentially misspecified autoregressive rule. Consistency criteria à la Hommes and Zhu (2014) between perceived and actual laws of motion of inflation might allow for multiple expectational equilibria. Unfortunately, multiple equilibria models pose challenges for empirical validation. This paper proposes a latent Markov chain process to dynamically separate such equilibria. Moreover, an original Bayesian inference approach based on hierarchical priors is introduced, which naturally offers the possibility of incorporating equilibrium-identifying constraints with various degrees of prior beliefs. Finally, an inference procedure is proposed to assess a posteriori the probability that the theoretical constraints are satisfied and to estimate the equilibrium changes over time. We show that common prior assumptions regarding structural parameters favor the separation of equilibria, thereby making the Bayesian inference a natural framework for Markov–switching Phillips curve models. Empirical evidence obtained from observed inflation, output gap, and the consensus expectations from the Survey of Professional Forecasters supports multiple equilibria, and we find evidence of temporal variation in over- and under-reaction patterns, which, to the best of our knowledge, have not been previously documented. Specifically, we observe that agents tend to underreact to shocks when inflation is high and persistent, whereas they behave substantially as fully informed forecasters when the inflation level is low and stable, i.e., after the mid–nineties. We also find that the model does not suffer from the missing disinflation puzzle during the Great Recession. |
| Keywords: | Bounded rationality; Markov Switching; Multiple equilibria; Under-reaction; Bayesian methods; Horseshoe hierarchical priors; Survey of Professional Forecasters |
| JEL: | C11 C24 E31 D84 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:10 |
| By: | Francesco Giancaterini; Alain Hecq; Joann Jasiak; Aryan Manafi Neyazi |
| Abstract: | This paper introduces a new approach to detect bubbles based on mixed causal and noncausal processes and their tail process representation during explosive episodes. Departing from traditional definitions of bubbles as nonstationary and temporarily explosive processes, we adopt a perspective in which prices are viewed as following a strictly stationary process, with the bubble considered an intrinsic component of its non-linear dynamics. We illustrate our approach on the phenomenon referred to as the "green bubble" in the field of renewable energy investment. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.14911 |
| By: | Christopher Parmeter; Artem Prokhorov; Valentin Zelenyuk |
| Abstract: | Big data and machine learning methods have become commonplace across economic milieus. One area that has not seen as much attention to these important topics yet is efficiency analysis. We show how the availability of big (wide) data can actually make detection of inefficiency more challenging. We then show how machine learning methods can be leveraged to adequately estimate the primitives of the frontier itself as well as inefficiency using the `post double LASSO' by deriving Neyman orthogonal moment conditions for this problem. Finally, an application is presented to illustrate key differences of the post-double LASSO compared to other approaches. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.14282 |
| By: | Pascal Michaillat |
| Abstract: | This paper develops a new algorithm for detecting US recessions in real time. The algorithm constructs millions of recession classifiers by combining unemployment and vacancy data to reduce detection noise. Classifiers are then selected to avoid both false negatives (missed recessions) and false positives (nonexistent recessions). The selected classifiers are therefore perfect, in that they identify all 15 historical recessions in the 1929–2021 training period without any false positives. By further selecting classifiers that lie on the high-precision segment of the anticipation-precision frontier, the algorithm optimizes early detection without sacrificing precision. On average, over 1929–2021, the classifier ensemble signals recessions 2.2 months after their true onset, with a standard deviation of detection errors of 1.9 months. Applied to May 2025 data, the classifier ensemble gives a 71% probability that the US economy is currently in recession. A placebo test and backtests confirm the algorithm’s reliability. The classifier ensembles trained on 1929–2004, 1929–1984, and 1929–1964 data in backtests give a current recession probability of 58%, 83%, and 25%, respectively. |
| JEL: | C52 E24 E32 J63 N12 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34015 |