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Item type:Publication, The least-squares criteria of the random coefficient dynamic regression model(2012-06-01)Araveeporn, AutchaThe random coefficient dynamic regression (RCDR) model develops from random coefficient autoregressive (RCA) model and autoregressive (AR) model. The RCDR model is considered by adding exogenous variables. In this article, the concept of the least-squares (LS) criterion is used to estimate the parameter on the RCDR model. Simulation results have shown that the proposed coefficient of the AR model provided asymptotically unbiased estimates nearly for most of the six data-generating models. The RCDR model is then applied to a series of daily observations of the exchange rate of Baht/GBP and Baht/EUR to illustrate the methodology. The predictions of LS criteria are used with those obtained on 20 hold-out future values of withheld observations. Copyright © Grace Scientific Publishing, LLC. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Developing nonparametric conditional heteroscedastic autoregressive nonlinear model by using maximum likelihood method(2011-01-01)Araveeporn, AutchaThe goal of this work is to develop a nonparametric conditional heteroscedastic autoregressive nonlinear (NCHARN) model by using maximum likelihood method that not only account for possibly non-linear trend but also account for possibly non-linear conditional variance of response as a function of predictor variables in the presence of auto-correlated errors. The trend and the heteroscedasticity are modeled using a class of penalized spline and the residuals are modeled as a autoregressive process (AR) by selecting an appropriate number of lag residuals. Both classical penalized spline and AR process of penalized spline under NCHARN model are developed to obtain the smooth estimates of the conditional mean and variance functions. The resulting estimated values are then used the maximum likelihood method to fi t a trend, volatility, and a coeffi cient of AR process by suitably choosing the order of AR using the Akaike Information Criteria (AIC). The forecasting performance of the proposed methods is then applied to the series of monthly observations of the Stock Exchange Rate of Thailand (SERT) to illustrate the methodology. The forecasts these methods are compared with those obtained based on future six months of withheld observations.