Search Results - subject_exact:"Homoscedasticity"

Noncausal AR processes driven by causal GARCH volatility

Velasquez-Gaviria, Daniel; Zakoïan, Jean-Michel - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015604163

Multiple monetary policy shocks from daily data : a heteroskedasticity IV approach

Burri, Marc; Kaufmann, Daniel - 2026

We extend the heteroskedasticity IV estimator of Rigobon and Sack (2004) from one to multiple monetary policy shocks by imposing recursive zero restrictions on the impact matrix. Unlike high-frequency identification, the approach requires neither intraday tick data nor precise announcement...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015625606

Crisis-regime dynamic volatility spillovers in U.S. commodity markets : a Bayesian mixture-identified SVAR approach

Deng, Xinyan; Aruga, Kentaka; Tang, Chaofeng - 2026

Conventional VAR-based volatility spillover measures rely on homoskedasticity and single-Gaussian assumptions, limiting their ability to capture structural breaks and heterogeneous shocks during crises. This study develops a flexible framework to analyze volatility transmission in U.S. commodity...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015639153

Power law heteroskedasticity

Price, David J. - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015647094

A Bayesian Dirichlet autoregressive conditional heteroskedasticity model for forecasting currency shares

Katz, Harrison; Weiss, Robert E. - 2026

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015668703

Valid heteroskedasticity robust testing

Pötscher, Benedikt M.; Preinerstorfer, David - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015374599

A long short-term memory enhanced realized conditional heteroskedasticity model

Liu, Chen; Wang, Chao; Minh-Ngoc Tran; Kohn, Robert - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015192384

Testing for nonlinear cointegration under heteroskedasticity

Hanck, Christoph; Massing, Till Philipp Georg - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015196620

A penalization approach for estimating inefficiency in stochastic frontier panel models

Doko Tchatoka, Firmin; Söderberg, Magnus; Hakeem, … - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015420420

Uncertainty in heteroscedastic Bayesian model averaging

Jessup, Sébastien; Mailhot, Mélina; Pigeon, Mathieu - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015432031

Heteroscedasticity-aware stratified sampling to improve uplift modeling

Bokelmann, Björn; Lessmann, Stefan - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015433226

HAR inference for quantile regression in time series

Hwang, Jungbin; Valdés, Gonzalo - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015445619

Locally adaptive modeling of unconditional heteroskedasticity

Fengler, Matthias; Jäger, Bruno; Okhrin, Ostap - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015426963

Fractional probit with Cross-Sectional Volatility : bridging heteroskedastic probit and fractional response models

Songsak Sriboonchitta; Aree Wiboonpongse; Jittaporn … - 2025

This paper introduces a new econometric framework for modeling fractional outcomes bounded between zero and one. We propose the Fractional Probit with Cross-Sectional Volatility (FPCV), which specifies the conditional mean through a probit link and allows the conditional variance to depend on...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015562101

The robust F-statistic as a test for weak instruments

Windmeijer, Frank - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015556393

Is U.S. real output growth non-normal? : a tale of time-varying location and scale

Demetrescu, Matei; Kruse-Becher, Robinson - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015556434

Partial identification of heteroskedastic structural vector autoregressions : theory and Bayesian inference

Lütkepohl, Helmut; Shang, Fei; Uzeda, Luis; Woźniak, … - 2025 - Last updated: May 9, 2025

We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions. Three contributions emerge from our...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015607273

Estimating efficiency effects in a stochastic frontier model with heteroskedastic errors

Shankar, Sriram - 2025

In this paper, we introduce a stochastic frontier model that incorporates efficiency effects and a heteroskedastic error structure. The mean efficiency is specified by a logistic function of the effects variables and the distribution for the one-sided random variable representing inefficiency is...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015626691

Modeling and estimating two-layer network interactions with unknown heteroskedasticity

Chen, Jainkun; Lin, Yanli; Yang, Yang - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015652747

A statistically identified structural vector autoregression with endogenously switching volatility regime

Virolainen, Savi - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015533863

A heteroscedasticity-robust overidentifying restriction test with high-dimensional covariates

Fan, Qingliang; Guo, Zijian; Mei, Ziwei - 2025

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015534249

Random effects panel data models with known heteroskedasticity

Schäper, Julius; Winkelmann, Rainer - 2024 - Revised version, September 2024

The paper considers two estimators for the linear random effects panel data model with known heteroskedasticity. Examples where heteroskedasticity can be treated as given include panel regression with averaged data, meta regression and the linear probability model. While one estimator builds on...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015062188

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Random effects panel data models with known heteroskedasticity

Schäper, Julius; Winkelmann, Rainer - 2024

Book / Working Paper

Estimating linear dynamic panels with recentered moments

Bao, Yong - 2024

This paper proposes estimating linear dynamic panels by explicitly exploiting the endogeneity of lagged dependent variables and expressing the crossmoments between the endogenous lagged dependent variables and disturbances in terms of model parameters. These moments, when recentered, form the...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014636394

Sequential change-point detection in time series models with conditional heteroscedasticity

Lee, Youngmi; Kim, Sungdon; Oh, Haejune - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015071897

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Sequential Change-Point Detection in Time Series Models with Conditional Heteroscedasticity

Lee, Youngmi; Kim, Sungdon; Oh, Haejune - 2023

Book / Working Paper

Identifying demand elasticity via heteroscedasticity : a panel GMM approach to estimation and inference

Brasch, Thomas von; Raknerud, Arvid; Vigtel, Trond C. - 2024

This paper introduces a panel GMM framework for identifying and estimating demand elasticities via heteroscedasticity. While existing panel estimators address the simultaneity problem, the state-ofthe-art Feenstra/Soderbery (F/S) estimator suffers from inconsistency, inefficiency, and lacks a...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015073836

Robust inference on correlation under general heterogeneity

Giraitis, Liudas; Li, Yufei; Phillips, Peter C. B. - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015075086

Show one more version 1

Robust inference on correlation under general heterogeneity

Giraitis, Liudas; Li, Yufei; Phillips, Peter C. B. - 2022

Book / Working Paper

Estimating conditional average treatment effects with heteroscedasticity by model averaging and matching

Shi, Pengfei; Zhang, Xinyu; Zhong, Wei - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015075165

Financial markets and legal challenges to unconventional monetary policy

Griller, Stefan; Huber, Florian; Pfarrhofer, Michael - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015076083

Partial identification of heteroskedastic structural VARs : theory and Bayesian inference

Lütkepohl, Helmut; Shang, Fei; Uzeda, Luis; Woźniak, … - 2024

We consider structural vector autoregressions identified through stochastic volatility. Our focus is on whether a particular structural shock is identified by heteroskedasticity without the need to impose any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014528602

Non-Gaussian score-driven conditionally heteroskedastic models with a macroeconomic application

Blazsek, Szabolcs; Escribano, Álvaro; Licht, Adrián - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014465380

Testing the correct specification of a system of spatial dependence models for stock returns

Kutzker, Tim; Wied, Dominik - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014520115

A powerful subvector Anderson-Rubin test in linear instrumental variables regression with conditional heteroskedasticity

Guggenberger, Patrik; Kleibergen, Frank; Mavroeidis, … - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015154314

Corporate earnings announcements and economic activity

Miescu, Mirela S.; Mumtaz, Haroon - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015168496

OLS limit theory for drifting sequences of parameters on the explosive side of unity

Magdalinos, Tassos; Petrova, Katerina - 2024

A limit theory is developed for the least squares estimator for mildly and purely explosive autoregressions under drifting sequences of parameters with autoregressive roots ρn satisfying ρn → ρ ∈ (-∞, -1] ∪ [1, ∞) and n (|ρn| -1) → ∞. Drifting sequences of innovations and...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015051928

Multi-dimensional monetary policy shocks based on heteroscedasticity

Burri, Marc; Kaufmann, Daniel - 2024

We propose a two-step approach to estimate multi-dimensional monetary policy shocks and their causal effects requiring only daily financial market data and policy events. First, we combine a heteroscedasticity-based identification scheme with recursive zero restrictions along the term structure...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015052047

Heckman sample selection estimators under heteroskedasticity

Carlson, Alyssa; Zhao, Wei - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015130330

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Heckman sample selection estimators under heteroskedasticity

Carlson, Alyssa; Zhao, Wei - 2023

Book / Working Paper

Heteroskedastic structural vector autoregressions identified via long-run restrictions

Bruns, Martin; Lütkepohl, Helmut - 2024 - This version: December 3, 2024

A central assumption for identifying structural shocks in vector autoregressive (VAR) models via heteroskedasticity is the time-invariance of the impact effects of the shocks. It is shown how that assumption can be tested when longrun restrictions are available for identifying structural shocks....

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015207512

Optimal HAR inference

Dou, Liyu - 2024

This paper considers the problem of deriving heteroskedasticity and autocorrelation robust (HAR) inference about a scalar parameter of interest. The main assumption is that there is a known upper bound on the degree of persistence in data. I derive finite‐sample optimal tests in the Gaussian...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015190109

Reprint of: Robust inference on correlation under general heterogeneity

Giraitis, Liudas; Li, Yufei; Phillips, Peter C. B. - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015553738

GLS under monotone heteroskedasticity

Arai, Yoichi; Otsu, Taisuke; Xu, Mengshan - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015553759

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GLS under monotone heteroskedasticity

Arai, Yoichi; Otsu, Taisuke; Xu, Mengshan - 2022

Book / Working Paper

Identification based on higher moments

Lewis, Daniel J. - 2024

Identification based on higher moments has drawn increasing theoretical attention and been widely adopted in empirical practice in macroeconometrics in the last two decades. This article reviews two parallel strands of the literature: identification strategies based on heteroskedasticity and...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014471719

Heteroskedastic proxy vector autoregressions : an identification-robust test for time-varying impulse responses in the presence of multiple proxies

Bruns, Martin; Lütkepohl, Helmut - 2024

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015050043

Partially identified heteroskedastic SVARs

Bacchiocchi, Emanuele; Bastianin, Andrea; Kitagawa, Toru; … - 2024

This paper presents new results on the identification of heteroskedastic structural vector autoregressive (HSVAR) models. Point identification of HSVAR models fails when some shifts in the variances of the structural shocks are suspected to be statistically indistinguishable from each other....

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014556642

Improved inference in financial factor models

Beck, Elliot; De Nard, Gianluca; Wolf, Michael - 2023

Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama-French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014232090

Dependent metaverse risk forecasts with heteroskedastic models and ensemble learning

Syuhada, Kreshna; Tjahjono, Venansius; Hakim, Arief - 2023

Metaverses have been evolving following the popularity of blockchain technology. They build their own cryptocurrencies for transactions inside their platforms. These new cryptocurrencies are, however, still highly speculative, volatile, and risky, motivating us to manage their risk. In this...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014234332

Continuously updated indirect inference in heteroskedastic spatial models

Kyriacou, Maria; Phillips, Peter C. B.; Rossi, Francesca - 2023

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014247296

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Continuously updated indirect inference in heteroskedastic spatial models

Kyriacou, Maria; Phillips, Peter C. B.; Rossi, Francesca - 2019

Book / Working Paper

Continuously Updated Indirect Inference in Heteroskedastic Spatial Models

Kyriacou, Maria; Phillips, Peter C. B.; Rossi, Francesca - 2019

Book / Working Paper

A Bias Test for Heteroscedastic Linear Least Squares Regression

Blankmeyer, Eric - 2023

A correlation between regressors and disturbances presents challenging problems in linear regression. Issues like omitted variables, measurement error and simultaneity render ordinary least squares (OLS) biased and inconsistent. In the context of heteroscedastic linear regression, this note...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014260355

Estimation of Panel Data Models with Cross-Sectionally Heteroskedastic Data

Ahn, Seung C.; Zhang, Xiangyu - 2023

Panel data models with cross-sectionally heteroskedastic data often suffer from the well-known incidental parameters problem. Some recent studies have proposed that the structural parameters (common parameters to all of the cross-sectional entities) can be consistently estimated if they are...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014348689

Unbounded Heteroscedasticity in Autoregressive Models

Kourogenis, Nikolaos; Pittis, Nikitas; Samartzis, Panagiotis - 2023

This paper develops the asymptotic theory for stable autoregressive models in which the noise variance grows in a polynomial-like fashion. It is shown that the asymptotic distribution of the OLS estimator of the coefficient vector is multivariate normal with a covariance matrix that depends on...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014353092

Monotonicity and Heteroskedasticity in Machine Learning Models : An Application to Credit Risk

García-Céspedes, Rubén; Moreno, Manuel; Segarra, Ignacio - 2023

In recent years, machine learning (ML) techniques have become very popular as, in many cases, they outperform other classic methods such as regressions. However, ML techniques usually have two main drawbacks as the marginal effects of the variables of the model may a) be counterintuitive, and b)...

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014355155