• DEoptim

  • Referenced in 53 articles [sw08656]
  • Markov-Switching Generalized AutoRegressive Conditional Heteroskedasticity (MSGARCH) model for the returns of the Swiss Market...
  • fGarch

  • Referenced in 29 articles [sw09994]
  • package fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling: Environment for teaching ”Financial Engineering and Computational Finance...
  • nlmdl

  • Referenced in 119 articles [sw27811]
  • squares estimates for a multivariate nonlinear regression model. Use the SUR option for both. Other ... correct variance estimates for heteroskedasticity and/or serial correlation. For simultaneous nonlinear systems, possibly in implicit...
  • OGLM

  • Referenced in 3 articles [sw14973]
  • choice/ location-scale models that explicitly specify the determinants of heteroskedasticity in an attempt ... other special cases of ordinal generalized linear models can also be estimated by oglm. oglm ... variables that are allowed when modeling heteroskedasticity. Stata 9 or 10 users should use oglm9...
  • boottest

  • Referenced in 5 articles [sw37419]
  • bootstrap was originally developed for regression models with heteroskedasticity of unknown form. Over the past...
  • hetGP

  • Referenced in 2 articles [sw40261]
  • package hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication. Performs Gaussian process regression with ... heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) , with implementation...
  • ARCHModels.jl

  • Referenced in 1 article [sw36552]
  • models in Julia. ARCH (Autoregressive Conditional Heteroskedasticity) models are a class of models designed...
  • xtabond2

  • Referenced in 24 articles [sw20376]
  • closely related dynamic panel data models. The first is the Arellano-Bond (1991) estimator, which ... those fixed effects--idiosyncratic errors that are heteroskedastic and correlated within but not across individuals...
  • ARCH

  • Referenced in 2 articles [sw27756]
  • ARCH models in Python: Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written...
  • dfbr

  • Referenced in 1 article [sw37453]
  • Distribution-free estimation of heteroskedastic binary response models in Stata. In this article, we consider...
  • npsf

  • Referenced in 2 articles [sw35585]
  • normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants...
  • olsrr

  • Referenced in 1 article [sw41427]
  • ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics...
  • IVREG2H

  • Referenced in 1 article [sw31887]
  • estimation using heteroskedasticity-based instruments. ivreg2h estimates an instrumental variables regression model providing the option ... identification of structural parameters in regression models with endogenous or mismeasured regressors in the absence ... product of heteroskedastic errors, which is a feature of many models where error correlations...
  • glmx

  • Referenced in 1 article [sw41218]
  • generalized linear models (GLMs), especially for binary responses, including parametric links and heteroskedastic latent variables...
  • GARCH Toolbox

  • Referenced in 1 article [sw14890]
  • GARCH Toolbox uses a general ARMAX/GARCH composite model to perform simulation, forecasting, and parameter estimation ... time series in the presence of conditional heteroskedasticity. Supporting functions perform tasks such ... estimation diagnostic testing, hypothesis testing of residuals, model order selection, and time series transformations. Graphics...
  • rivtest

  • Referenced in 1 article [sw37485]
  • variables model by allowing for variance–covariance estimation that is robust to arbitrary heteroskedasticity...
  • vbdcast

  • Referenced in 3 articles [sw34857]
  • outbreaks: Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study ... outperforms a more classical generalized linear (autoregressive) model (GLM) that we developed to utilize abundant...
  • xtserialpm

  • Referenced in 0 articles [sw37541]
  • errors of a linear panel model after estimation of the regression coefficients by the within ... that it allows for heteroskedasticity. In simulations documented below, xtserialpm is found to provide...
  • REndo

  • Referenced in 0 articles [sw17790]
  • Regressors using Latent Instrumental Variables. Fits linear models with endogenous regressor using latent instrumental variable ... approach as well as Lewbel’s (2012) heteroskedasticity approach, Park and Gupta’s (2012) joint...
  • Ranktest

  • Referenced in 1 article [sw31899]
  • econometrics concerns cointegration in vector autoregressive (VAR) models; the Johansen trace test is a test ... when disturbances are heteroskedastic or autocorrelated, the test statistics are no longer valid. The Kleibergen...