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on Econometrics |
| By: | Xiyuan Liu (School of Economics and Management, Tshinghua University, Beijing, Beijing 100084, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Liangjun Su (School of Economics and Management, Tshinghua University, Beijing, Beijing 100084, China) |
| Abstract: | We study a novel time-varying (TV) factor-augmented (FA) forecasting model, where the forecast target is driven by a strict subset of all the latent factors driving the predictors. To consistently select the target-related factors and estimate the TV parameters simultaneously, we first obtain the unobserved common factors via the local principal component analysis. Next, we conduct a variable selection procedure via a time-varying weighted group least absolute shrinkage and selection operator to select relevant factors. The identification restrictions used in this paper permit asymptotically rotation-free estimation of both factors and loadings. The asymptotic properties, such as consistency, sparsity and the oracle property of these two-step estimators are established. Simulation studies demonstrate the excellent finite sample performance of the proposed estimators. In an empirical application to the U.S. macroeconomic dataset, we show that the penalized TV-FA forecasting model outperforms the conventional TV-FAVAR model in predicting certain key macroeconomic series |
| Keywords: | Factor-augmented forecasting models; Local-linear smoothing; Structural change; Weighted group LASSO, Time-varying modeling |
| JEL: | C13 C23 C33 C38 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202515 |
| By: | Lam, Clifford; Cen, Zetai |
| Abstract: | We introduce the matrix-valued time-varying Main Effects Factor Model (MEFM). MEFM is a generalization to the traditional matrix-valued factor model (FM). We give rigorous definitions of MEFM and its identifications, and propose estimators for the time-varying grand mean, row and column main effects, and the row and column factor loading matrices for the common component. Rates of convergence for different estimators are spelt out, with asymptotic normality shown. The core rank estimator for the common component is also proposed, with consistency of the estimators presented. As time series, the row and column main effects { α t } and { β t } can be non-stationary without affecting the estimation accuracy of our estimators. The number of main effects factors contributing to row or column main effects is also consistently estimated by our proposed estimators. We propose a test for testing if FM is sufficient against the alternative that MEFM is necessary, and demonstrate the power of such a test in various simulation settings. We also demonstrate numerically the accuracy of our estimators in extended simulation experiments. A set of NYC Taxi traffic data is analyzed and our test suggests that MEFM is indeed necessary for analyzing the data against a traditional FM. |
| Keywords: | large-scale dependent data; time-varying row and column effects; MEFM and FM interchange; sufficiency of FM over MEFM; Tucker decomposition |
| JEL: | J1 C1 |
| Date: | 2025–11–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129557 |
| By: | Gergely Ganics (BANCO DE ESPAÑA); Lluc Puig Codina (UNIVERSITY OF ALICANTE AND BANCO DE ESPAÑA) |
| Abstract: | We propose a simplified framework for evaluating conditional predictive densities based on the probability integral transform (PIT). The approach accommodates a wide range of estimation schemes, including expanding and rolling windows, and applies to both stationary and non-stationary processes. By treating the PIT as a primitive, our approach enables researchers to apply widely used tests in settings where their validity was previously uncertain. Monte Carlo simulations demonstrate favorable size and power properties of the tests. In an empirical application, we show that incorporating stochastic volatility into an unobserved components model is essential for generating correctly calibrated density forecasts of US industrial production growth at both monthly and quarterly frequencies. |
| Keywords: | predictive density, forecast evaluation, probability integral transform, Kolmogorov–Smirnov test, Cramér–von Mises test |
| JEL: | C22 C52 C53 |
| Date: | 2035–09 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2535 |
| By: | Hadi Elzayn; Jacob Goldin; Cameron Guage; Daniel E. Ho; Claire Morton |
| Abstract: | We study monotone ecological inference, a partial identification approach to ecological inference. The approach exploits information about one or both of the following conditional associations: (1) outcome differences between groups within the same neighborhood, and (2) outcomes differences within the same group across neighborhoods with different group compositions. We show how assumptions about the sign of these conditional associations, whether individually or in relation to one another, can yield informative sharp bounds in ecological inference settings. We illustrate our proposed approach using county-level data to study differences in Covid-19 vaccination rates among Republicans and Democrats in the United States. |
| JEL: | C1 C10 C13 C18 C21 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34285 |
| By: | Peter C. B. Phillips (Yale University) |
| Abstract: | Edgeworth expansions are developed for the finite sample distribution of the least squares estimator in a time series parametric first order autoregression with Hilbert space curves of cross section data. The main result extends to this functional data environment the Edgeworth expansion in the corresponding scalar time series AR(1). In doing so, the results show how function-valued cross section data, and hence general forms of cross section dependence, affect the finite sample distribution of the serial correlation coefficient. Autoregressions with functional fixed effect intercepts are included and the results therefore relate to dynamic panel autoregression with individual effects. The primary impact of the use of high-dimensional curved cross section data is to reduce the variation in scalar regression estimation and provide some improvement in the accuracy of the usual asymptotic approximation to the finite sample distribution. Limit results for the expansions under full cross section dependence matching the scalar time series case and independence matching the dynamic panel case are given as special cases. The findings are supported by numerical computations of the exact distributions and the approximations. |
| Date: | 2025–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2464 |
| By: | Camilo Umana Dajud |
| Abstract: | This paper documents a fundamental problem in applied gravity estimation: the variance of gravity estimates becomes prohibitively large when policy regressors are parse. I define sparse regressors as dummy variables that equal one in fewer than 100 to 500 observations depending on the setting; a common characteristic of trade policies such as free trade agreements. Through Monte Carlo simulations calibrated to match previously established data generating processes in the literature, I demonstrate that the variance of coefficient estimates is approximately inversely proportional to the number of treated observations, making reliable statistical inference impossible when policy variables are infrequent. This variance problem is distinct from well-known issues related to high-dimensional fixed effects and affects both OLS and PPML estimators regardless of specification complexity. The severity of this variance problem depends on the magnitude of the true underlying coefficient: the variance problem is severe and practically prohibitive for moderate coefficients (such as those typically found for many trade policy effects), but becomes negligible for large effects. To address this issue, I propose Ridge regularization as a practical solution that reduces estimate variance while introducing minimal bias. The main contribution however is not advocating for Ridge regularization, but rather highlighting that variance is often the dominant source of uncertainty in gravity estimation when dealing with sparse policy variables, underscoring fundamental limitations of gravity models for evaluating infrequent policies with moderate effect sizes. These findings have implications not only for the international trade literature but also for other fields that employ gravitytype specifications, including migration and macroeconomics. |
| Keywords: | Gravity Model;Variance;Ridge Regression;Trade Policy |
| JEL: | F10 F14 C23 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:cii:cepidt:2025-12 |
| By: | Boris Gershman; Tinatin Mumladze |
| Abstract: | Empirical research on culture and institutions in economics often relies on cross-cultural data to examine historical or contemporary variation in traits across ethnolinguistic groups. We argue that this work has not adequately addressed the problem of cultural non-independence due to common ancestry and show how phylogenetic regression, along with newly available global language trees, can be used to directly account for this issue. Our analysis focuses on Murdock's Ethnographic Atlas (EA), a widely used database of preindustrial societies, with broader implications for any cross-cultural study. First, we show that various economic, institutional, and cultural characteristics in the EA exhibit substantial phylogenetic signal - they tend to be more similar among societies with closer ancestral ties. Second, through simulations in a sample resembling the EA, we demonstrate that phylogenetic correlation leads to severe inefficiency of the standard OLS estimator and unacceptably high type I error rates, even when clustered standard errors are used. Phylogenetic generalized least squares (PGLS), exploiting the information on shared ancestry contained in language trees, improves estimation accuracy and enables reliable hypothesis testing. Third, we revisit some of the recently published results in a phylogenetic regression framework. In many specifications, PGLS estimates differ markedly from their OLS counterparts, indicating a smaller magnitude and weaker statistical significance of relevant coefficients. |
| Keywords: | Common ancestry, Cross-cultural analysis, Culture, Cultural non-independence, Ethnographic Atlas, Institutions, Phylogenetic comparative methods |
| JEL: | C10 O10 N30 Z12 Z13 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:amu:wpaper:2025-02 |
| By: | Simons, J. R.; Chen, Y.; Brunner, E.; French, E. |
| Abstract: | This paper estimates the stochastic process of how dementia incidence evolves over time. We proceed in two steps: first, we estimate a time trend for dementia using a multi-state Cox model. The multi-state model addresses problems of both interval censoring arising from infrequent measurement and also measurement error in dementia. Second, we feed the estimated mean and variance of the time trend into a Kalman filter to infer the population level dementia process. Using data from the English Longitudinal Study of Aging (ELSA), we find that dementia incidence is no longer declining in England. Furthermore, our forecast is that future incidence remains constant, although there is considerable uncertainty in this forecast. Our twostep estimation procedure has significant computational advantages by combining a multi-state model with a time series method. To account for the short sample that is available for dementia, we derive expressions for the Kalman filter’s convergence speed, size, and power to detect changes and conclude our estimator performs well even in short samples. |
| Keywords: | Dementia Incidence, Time Trends, Forecasting |
| Date: | 2025–09–09 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2563 |
| By: | Igor L. Kheifets (UNC Charlotte); Peter C. B. Phillips (Yale University) |
| Abstract: | Optimal estimation is explored in long run relations that are modeled within a semiparametric triangular multicointegrated system. In nonsingular cointegrated systems, where there is no multicointegration, optimal estimation is well understood (Phillips, 1991a). This paper establishes corresponding optimal results for singular systems, thereby accommodating a wide class of multicointegrated nonstationary time series with nonparametric transient dynamics. The optimality and sub-optimality of existing estimators are considered and new optimal estimators of both the cointegrating and multicointegrating coefficients are introduced that are based on spectral regression. |
| Date: | 2025–09–27 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2463 |
| By: | Gouriéroux, Christian; Monfort, Alain |
| Abstract: | The aim of this paper is to link the machine learning method of multilayer perceptron (MLP) neural network with the classical analysis of stochastic state space models. We consider a special class of state space models with multiple layers based on affine conditional Laplace transforms. This new class of Affine Feedforward Stochastic (AFS) neural network provides closed form recursive formulas for recursive filtering of the state variables of different layers. This approach is suitable for online inference by stochastic gradient ascent optimization and for recursive computation of scores such as backpropagation. The approach is extended to recurrent neural networks and identification issues are discussed. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130941 |
| By: | Daniel O. Beltran; Julio L. Ortiz |
| Abstract: | We explore differences in the dynamics of core inflation between Europe and North America using a Bayesian time series filter that decomposes the level of core inflation in the major advanced economies into regional, global, and country-specific components. We find a prominent role for both regional and global factors. Historically, the two regional components have at times diverged. Using reduced-form regressions, we examine the economic drivers behind the changes in our estimated global and regional components of U.S. core inflation, focusing on the post-pandemic inflation surge and subsequent pullback. The global component is associated with global supply frictions and past energy shocks. The North American regional component is associated with labor market tightness in the region. |
| Keywords: | Regional inflation; Dynamic Linear Model; Core inflation |
| JEL: | C11 C32 C53 E31 F00 |
| Date: | 2025–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgif:1421 |
| By: | Esther Devilliers (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Niklas Möhring (WUR - Wageningen University and Research [Wageningen]); Robert Finger (Agricultural economics and policy - ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]) |
| Abstract: | Low-input production systems aim at mitigating agriculture's environmental impact with a lower level of chemical inputs. However, comparing the performance of low-input systems to conventional ones, particularly in terms of productivity and yield, is challenging due to selection bias. First, we often lack observational data on low-input systems. Then, when available, the comparison between the two production systems is challenging due to potential endogeneity in input use and selection bias. To effectively develop policies promoting the adoption of low-input systems and assess their impact, for example, on pesticide use and yields, it is crucial to employ an econometric framework that addresses these issues. This article proposes an endogenous switching approach combined with control functions to tackle selection bias and input endogeneity simultaneously. Using unbalanced panel data on Swiss wheat production, which includes both low-input and conventional systems, our framework allows us to analyze the differentiated role of inputs as well as their price elasticity for both conventional and low-input farming systems. |
| Keywords: | Control Function, Endogenous Switching Regression, Pesticide Use, Low-input Production Systems |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04157545 |