The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform

Dong Yan - One of the best experts on this subject based on the ideXlab platform.

  • Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables
    Energy Conversion and Management, 2015
    Co-Authors: Huaiwei Sun, Na Zhao, Xiaofan Zeng, Dong Yan
    Abstract:

    Abstract The traditional approaches that employ the correlations between solar radiation and other measured meteorological variables are commonly utilized in studies. It is important to investigate the time-varying relationships between meteorological variables and solar radiation to determine which variables have the strongest correlations with solar radiation. In this study, the nonlinear autoregressive moving average with exogenous variable–generalized autoregressive conditional Heteroscedasticity (ARMAX–GARCH) and multivariate GARCH (MGARCH) time-series approaches were applied to investigate the associations between solar radiation and several meteorological variables. For these investigations, the long-term daily global solar radiation series measured at three stations from January 1, 2004 until December 31, 2007 were used in this study. Stronger relationships were observed to exist between global solar radiation and sunshine duration than between solar radiation and temperature difference. The results show that 82–88% of the temporal variations of the global solar radiation were captured by the sunshine-duration-based ARMAX–GARCH models and 55–68% of daily variations were captured by the temperature-difference-based ARMAX–GARCH models. The advantages of the ARMAX–GARCH models were also confirmed by comparison of Auto-Regressive and Moving Average (ARMA) and neutral network (ANN) models in the estimation of daily global solar radiation. The strong heteroscedastic persistency of the global solar radiation series was revealed by the AutoRegressive Conditional Heteroscedasticity (ARCH) and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) parameters. In order to illustrate the novelty and usefulness of the MGARCH model in energy applications, the conditional covariances and correlation coefficients between the global solar radiation and the meteorological variables among stations were obtained by dynamic conditional correlation (DCC) models. The resulting conditional covariances and correlation coefficients were found to be large. It was also observed that the conditional correlation coefficients between global solar radiation and sunshine duration were higher than that between global solar radiation and temperature difference. The results of this study will provide a better understanding of the associations between global solar radiation and meteorological variables and will provide a basis for the investigation of this relationship in models.

Esther Ruiz - One of the best experts on this subject based on the ideXlab platform.

  • Unobserved component models with asymmetric conditional variances
    Computational Statistics & Data Analysis, 2006
    Co-Authors: Carmen Broto, Esther Ruiz
    Abstract:

    Unobserved component models with GARCH disturbances are extended to allow for asymmetric responses of conditional variances to positive and negative shocks. The asymmetric conditional variance is represented by a member of the QARCH class of models. The proposed model allows to distinguish whether the possibly asymmetric conditional Heteroscedasticity affects the short-run or the long-run disturbances or both. Statistical properties of the new model and the finite sample properties of a QML estimator of the parameters are analyzed. The correlogram of squared auxiliary residuals is shown to be useful to identify the conditional Heteroscedasticity. Finite sample properties of squared auxiliary residuals are also analysed. Finally, the results are illustrated by fitting the model to daily series of financial and gold prices, as well as to monthly series of inflation. The behavior of volatility in both types of series is different. The conditional Heteroscedasticity mainly affects the short-run component in financial prices while in the inflation series, the Heteroscedasticity appears in the long-run component. Asymmetric effects are found in both types of variables.

  • Using auxiliary residuals to detect conditional Heteroscedasticity in inflation
    Research Papers in Economics, 2006
    Co-Authors: Esther Ruiz, Carmen Broto
    Abstract:

    In this paper we consider a model with stochastic trend, seasonal and transitory components with the disturbances of the trend and transitory disturbances specified as QGARCH models. We propose to use the differences between the autocorrelations of squares and the squared autocorrelations of the auxiliary residuals to identify which component is heteroscedastic. The finite sample performance of these differences is analysed by means of Monte Carlo experiments. We show that conditional Heteroscedasticity truly present in the data can be rejected when looking at the correlations of observations or of standardized residuals while the autocorrelations of auxiliary residuals allow us to detect adequately whether there is Heteroscedasticity and which is the heteroscedastic component. We also analyse the finite sample behaviour of a QML estimator of the parameters of the model. Finally, we use auxiliary residuals to detect conditional Heteroscedasticity in monthly series of inflation of eight OECD countries. We conclude that, for most of these series, the conditional Heteroscedasticity affects the transitory component while the long-run and seasonal components are homoscedastic. Furthermore, in the countries where there is a significant relationship between the volatility and the level of inflation, this relation is positive, supporting the Friedman hypothesis.

Reinhold Kosfeld - One of the best experts on this subject based on the ideXlab platform.

  • Spurious spatial regression and Heteroscedasticity
    Journal of Spatial Science, 2011
    Co-Authors: Jørgen Lauridsen, Reinhold Kosfeld
    Abstract:

    A two-step Lagrange Multiplier test strategy has recently been suggested as a device to reveal spatial nonstationarity and spurious spatial regression. The present paper generalises this procedure by incorporating control for unobserved Heteroscedasticity. Using Monte Carlo simulation, the behaviour of several relevant tests for nonstationarity and/or Heteroscedasticity is investigated. The two-step Lagrange Multiplier test for spatial nonstationarity turns out to be robust towards Heteroscedasticity. While several tests for Heteroscedasticity prove inconclusive under certain circumstances, it is shown that a Lagrange Multiplier test for Heteroscedasticity based on spatially differenced variables serves well as an indication of Heteroscedasticity irrespective of stationarity status.

  • Spatial cointegration and Heteroscedasticity
    Journal of Geographical Systems, 2007
    Co-Authors: Jørgen Lauridsen, Reinhold Kosfeld
    Abstract:

    A two-step Lagrange Multiplier test strategy has recently been suggested as a tool to reveal spatial cointegration. The present paper generalises the test procedure by incorporating control for unobserved Heteroscedasticity. Using Monte Carlo simulation, the behaviour of several relevant tests for spatial cointegration and/or Heteroscedasticity is investigated. The two-step test for spatial cointegration appears to be robust towards Heteroscedasticity. While several tests for Heteroscedasticity prove to be inconclusive under certain circumstances, a Lagrange Multiplier test for Heteroscedasticity based on spatially differenced variables is shown to serve well as an indication of Heteroscedasticity irrespective of cointegration status.

  • Spurious Spatial Regression, Spatial Cointegration and Heteroscedasticity
    2006
    Co-Authors: Jørgen Lauridsen, Reinhold Kosfeld
    Abstract:

    A test strategy consisting of a two-step application of a Lagrange Multiplier test was recently suggested as a device to reveal spatial nonstationarity, spurious spatial regression and spatial cointegration. The present paper generalises the test procedure by incorporating control for biased test values emerging from unobserved Heteroscedasticity. Using Monte Carlo simulation, the behaviour of several relevant tests for nonstationarity and/or Heteroscedasticity are investigated. The two-step test for spatial nonstationarity turns out to be robust towards Heteroscedasticity. While several tests for Heteroscedasticity prove to be inconclusive under certain circumstances, a Lagrange Multiplier test for Heteroscedasticity based on spatially differenced variables serves well as an indication of Heteroscedasticity irrespective of stationarity status. JEL Classifications: C21; C40; C51; J60.

Huaiwei Sun - One of the best experts on this subject based on the ideXlab platform.

  • Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables
    Energy Conversion and Management, 2015
    Co-Authors: Huaiwei Sun, Na Zhao, Xiaofan Zeng, Dong Yan
    Abstract:

    Abstract The traditional approaches that employ the correlations between solar radiation and other measured meteorological variables are commonly utilized in studies. It is important to investigate the time-varying relationships between meteorological variables and solar radiation to determine which variables have the strongest correlations with solar radiation. In this study, the nonlinear autoregressive moving average with exogenous variable–generalized autoregressive conditional Heteroscedasticity (ARMAX–GARCH) and multivariate GARCH (MGARCH) time-series approaches were applied to investigate the associations between solar radiation and several meteorological variables. For these investigations, the long-term daily global solar radiation series measured at three stations from January 1, 2004 until December 31, 2007 were used in this study. Stronger relationships were observed to exist between global solar radiation and sunshine duration than between solar radiation and temperature difference. The results show that 82–88% of the temporal variations of the global solar radiation were captured by the sunshine-duration-based ARMAX–GARCH models and 55–68% of daily variations were captured by the temperature-difference-based ARMAX–GARCH models. The advantages of the ARMAX–GARCH models were also confirmed by comparison of Auto-Regressive and Moving Average (ARMA) and neutral network (ANN) models in the estimation of daily global solar radiation. The strong heteroscedastic persistency of the global solar radiation series was revealed by the AutoRegressive Conditional Heteroscedasticity (ARCH) and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) parameters. In order to illustrate the novelty and usefulness of the MGARCH model in energy applications, the conditional covariances and correlation coefficients between the global solar radiation and the meteorological variables among stations were obtained by dynamic conditional correlation (DCC) models. The resulting conditional covariances and correlation coefficients were found to be large. It was also observed that the conditional correlation coefficients between global solar radiation and sunshine duration were higher than that between global solar radiation and temperature difference. The results of this study will provide a better understanding of the associations between global solar radiation and meteorological variables and will provide a basis for the investigation of this relationship in models.

Axel Munk - One of the best experts on this subject based on the ideXlab platform.

  • Testing Heteroscedasticity in nonparametric regression
    Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1998
    Co-Authors: Holger Dette, Axel Munk
    Abstract:

    The importance of being able to detect Heteroscedasticity in regression is widely recognized because efficient inference for the regression function requires that Heteroscedasticity is taken into account. In this paper a simple consistent test for Heteroscedasticity is proposed in a nonparametric regression set-up. The test is based on an estimator for the best L 2 -approximation of the variance function by a constant. Under mild assumptions asymptotic normality of the corresponding test statistic is established even under arbitrary fixed alternatives. Confidence intervals are obtained for a corresponding measure of Heteroscedasticity. The finite sample performance and robustness of these procedures are investigated in a simulation study and Box-type corrections are suggested for small sample sizes.