Estimating Functions for Circular Time Series Models

Sankhya A ◽  
2021 ◽  
Author(s):  
Aerambamoorthy Thavaneswaran ◽  
Nalini Ravishanker
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Estimating Functions ◽  
Circular Time Series

Procedures for outlier detection in circular time series models

2014 ◽  
Vol 21 (4) ◽  
pp. 793-809 ◽  
Author(s):  
A. H. Abuzaid ◽  
I. B. Mohamed ◽  
A. G. Hussin
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Time Series Models ◽  
Circular Time Series

scholarly journals Joint Estimation Using Quadratic Estimating Function

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Y. Liang ◽  
A. Thavaneswaran ◽  
B. Abraham
Keyword(s):  
Time Series ◽  
Information Gain ◽  
Joint Estimation ◽  
Time Series Models ◽  
Estimating Function ◽  
Estimating Functions ◽  
Conditional Mean ◽  
Conditional Moments ◽  
Acd Models ◽  
Variance Parameters

A class of martingale estimating functions is convenient and plays an important role for inference for nonlinear time series models. However, when the information about the first four conditional moments of the observed process becomes available, the quadratic estimating functions are more informative. In this paper, a general framework for joint estimation of conditional mean and variance parameters in time series models using quadratic estimating functions is developed. Superiority of the approach is demonstrated by comparing the information associated with the optimal quadratic estimating function with the information associated with other estimating functions. The method is used to study the optimal quadratic estimating functions of the parameters of autoregressive conditional duration (ACD) models, random coefficient autoregressive (RCA) models, doubly stochastic models and regression models with ARCH errors. Closed-form expressions for the information gain are also discussed in some detail.

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
2010 ◽  
Vol 32 (JRM 1) ◽  
pp. 24-29
Author(s):  
Marnik G. Dekimpe ◽  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Recent Developments

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

Sign in / Sign up

Export Citation Format

Share Document