The Experts below are selected from a list of 253020 Experts worldwide ranked by ideXlab platform
Wai Keung Li - One of the best experts on this subject based on the ideXlab platform.
-
on fractionally integrated autoregressive moving average Time Series Models with conditional heteroscedasticity
Journal of the American Statistical Association, 1997Co-Authors: Shiqing Ling, Wai Keung LiAbstract:Abstract This article considers fractionally integrated autoregressive moving-average Time Series Models with conditional heteroscedasticity, which combines the popular generalized autoregressive conditional heteroscedastic (GARCH) and the fractional (ARMA) Models. The fractional differencing parameter d can be greater than 1/2, thus incorporating the important unit root case. Some sufficient conditions for stationarity, ergodicity, and existence of higher-order moments are derived. An algorithm for approximate maximum likelihood (ML) estimation is presented. The asymptotic properties of ML estimators, which include consistency and asymptotic normality, are discussed. The large-sample distributions of the residual autocorrelations and the square-residual autocorrelations are obtained, and two portmanteau test statistics are established for checking model adequacy. In particular, non-stationary FARIMA(p, d, q)-GARCH(r, s) Models are also considered. Some simulation results are reported. As an illustration,...
Richard A. Davis - One of the best experts on this subject based on the ideXlab platform.
-
goodness of fit testing for Time Series Models via distance covariance
arXiv: Statistics Theory, 2019Co-Authors: Phyllis Wan, Richard A. DavisAbstract:In many statistical modeling frameworks, goodness-of-fit tests are typically administered to the estimated residuals. In the Time Series setting, whiteness of the residuals is assessed using the sample autocorrelation function. For many Time Series Models, especially those used for financial Time Series, the key assumption on the residuals is that they are in fact independent and not just uncorrelated. In this paper, we apply the auto-distance covariance function (ADCV) to evaluate the serial dependence of the estimated residuals. Distance covariance can discriminate between dependence and independence of two random vectors. The limit behavior of the test statistic based on the ADCV is derived for a general class of Time Series Models. One of the key aspects in this theory is adjusting for the dependence that arises due to parameter estimation. This adjustment has essentially the same form regardless of the model specification. We illustrate the results in simulated examples.
-
consistency of minimum description length model selection for piecewise stationary Time Series Models
Electronic Journal of Statistics, 2013Co-Authors: Richard A. Davis, Chun Yip YauAbstract:This paper establishes the consistency of the minimum description length (MDL) model selection procedure by [10, 11] for a class of non-stationary Time Series Models. We consider a Time Series model in which the observations are viewed as coming from stationary segments. In other words, the data are assumed to come from a general Time Series model in which the parameters change at break-points. Each of these segments is modeled by a pre-specified family of parametric stationary Time Series Models. [10, 11] formulated the above problem and used the minimum description length (MDL) principle to estimate the number of break-points, the location of the break-points, the order of the parametric model and the parameter values in each of the segments. The procedure performed well on a variety of examples. In this paper we show consistency of their minimal MDL model selection procedure under general regularity conditions on the likelihood function. Results about the rate of convergence of the break-point-location estimator are also given. Applications are considered for detecting changes in independent random variables, and in ARMA and GARCH processes.
-
least absolute deviation estimation for general autoregressive moving average Time Series Models
Journal of Time Series Analysis, 2010Co-Authors: Rongning Wu, Richard A. DavisAbstract:We study least absolute deviation (LAD) estimation for general autoregressive moving average Time-Series Models that may be noncausal, noninvertible or both. For ARMA Models with Gaussian noise, causality and invertibility are assumed for the parameterization to be identifiable. The assumptions, however, are not required for Models with non-Gaussian noise, and hence are removed in our study. We derive a functional limit theorem for random processes based on an LAD objective function, and establish the consistency and asymptotic normality of the LAD estimator. The performance of the estimator is evaluated via simulation and compared with the asymptotic theory. Application to real data is also provided.
Qi Zheng - One of the best experts on this subject based on the ideXlab platform.
-
conditional maximum likelihood estimation for a class of observation driven Time Series Models for count data
Statistics & Probability Letters, 2017Co-Authors: Yunwei Cui, Qi ZhengAbstract:Abstract This paper investigates the statistical inference for a class of observation-driven Time Series Models of count data based on the conditional maximum likelihood estimator (CMLE), where the conditional distribution of the observed count given a state process is from the one-parameter exponential family. Under certain regularity conditions, the strong consistency and asymptotic normality of the CMLE of the misspecified likelihood function are established.
Farah Yasmeen - One of the best experts on this subject based on the ideXlab platform.
-
coherent mortality forecasting the product ratio method with functional Time Series Models
Demography, 2013Co-Authors: Rob J Hyndman, Heather Booth, Farah YasmeenAbstract:When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components Models of simple and interpretable functions of rates. The product-ratio functional forecasting method Models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary Time Series Models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenized across subpopulations.
-
coherent mortality forecasting the product ratio method with functional Time Series Models
Research Papers in Economics, 2011Co-Authors: Rob J Hyndman, Heather Booth, Farah YasmeenAbstract:When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for non-divergent or coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components Models of simple and interpretable functions of rates. The product-ratio functional forecasting method Models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary Time Series Models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenised across subpopulations.
Shiqing Ling - One of the best experts on this subject based on the ideXlab platform.
-
A General Asymptotic Theory for Time Series Models
CIRJE F-Series, 2009Co-Authors: Shiqing Ling, Michael McaleerAbstract:This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodic Time Series Models. Under simple conditions that are straightforward to check, we establish the strong consistency, the rate of strong convergence and the asymptotic normality of a general class of estimators that includes LSE, MLE, and some M-type estimators. As an application, we verify the assumptions for the long-memory fractional ARIMA model. Other examples include the GARCH(1,1) model, random coefficient AR(1) model and the threshold MA(1) model.
-
on fractionally integrated autoregressive moving average Time Series Models with conditional heteroscedasticity
Journal of the American Statistical Association, 1997Co-Authors: Shiqing Ling, Wai Keung LiAbstract:Abstract This article considers fractionally integrated autoregressive moving-average Time Series Models with conditional heteroscedasticity, which combines the popular generalized autoregressive conditional heteroscedastic (GARCH) and the fractional (ARMA) Models. The fractional differencing parameter d can be greater than 1/2, thus incorporating the important unit root case. Some sufficient conditions for stationarity, ergodicity, and existence of higher-order moments are derived. An algorithm for approximate maximum likelihood (ML) estimation is presented. The asymptotic properties of ML estimators, which include consistency and asymptotic normality, are discussed. The large-sample distributions of the residual autocorrelations and the square-residual autocorrelations are obtained, and two portmanteau test statistics are established for checking model adequacy. In particular, non-stationary FARIMA(p, d, q)-GARCH(r, s) Models are also considered. Some simulation results are reported. As an illustration,...
