scholarly journals A new model for time series of counts

2014 ◽  
Author(s):  
Wooi Chen Khoo ◽  
Seng Huat Ong
Keyword(s):  
Time Series ◽  
Time Series Of Counts ◽  
New Model

scholarly journals A New Bivariate Random Coefficient INAR(1) Model with Applications

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 39
Author(s):  
Qi Li ◽  
Huaping Chen ◽  
Xiufang Liu
Keyword(s):  
Time Series ◽  
Random Coefficient ◽  
Finite Sample ◽  
Time Series Of Counts ◽  
New Model ◽  
Excess Zeros ◽  
Proposed Model ◽  
Over Dispersion ◽  
Conditional Maximum ◽  
Stochastic Properties

Excess zeros is a common phenomenon in time series of counts, but it is not well studied in asymmetrically structured bivariate cases. To fill this gap, we first considered a new first-order, bivariate, random coefficient, integer-valued autoregressive model with a bivariate innovation, which follows the asymmetric Hermite distuibution with five parameters. An attractive advantage of the new model is that the dependence between series is achieved by innovative parts and the cross-dependence of the series. In addition, the time series of counts are modeled with excess zeros, low counts and low over-dispersion. Next, we established the stationarity and ergodicity of the new model and found its stochastic properties. We discuss the conditional maximum likelihood (CML) estimate and its asymptotic property. We assessed finite sample performances of estimators through a simulation study. Finally, we demonstrate the superiority of the proposed model by analyzing an artificial dataset and a real dataset.

Extreme Overdispersion and Persistence in Time-Series of Counts

2020 ◽  
Author(s):  
Leopoldo Catania ◽  
Eduardo Rossi ◽  
Paolo Santucci de Magistris
Keyword(s):  
Time Series ◽  
Time Series Of Counts

scholarly journals QUANTILE DOUBLE AUTOREGRESSION

2021 ◽  
pp. 1-47
Author(s):  
Qianqian Zhu ◽  
Guodong Li
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Simulation Studies ◽  
Finite Sample ◽  
Conditional Quantile ◽  
New Model ◽  
Conditional Quantiles ◽  
Financial Time ◽  
Portmanteau Tests ◽  
Strict Stationarity

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

The Modeling of Great Salt Lake Elevation Time Series Based on ARFIMA With Stable Innovations

2009 ◽  
Author(s):  
Hu Sheng ◽  
YangQuan Chen
Keyword(s):  
Time Series ◽  
Fractional Integral ◽  
Salt Lake ◽  
Moving Average ◽  
Great Salt Lake ◽  
Western Hemisphere ◽  
Conditional Heteroskedasticity ◽  
New Model ◽  
The People ◽  
Auto Regressive

Great Salt Lake (GSL) is the largest salt lake in the western hemisphere, the fourth-largest terminal lake in the world. The elevation of Great Salt Lake has critical effect on the people who live nearby and their properties. It is crucial to build an exact model of GSL elevation time series in order to predict the GSL elevation precisely. Although some models, such as FARIMA or ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity), have been built to characterize the variation of Great Salt Lake elevation, these models can not characterize it perfectly. Therefore, it became a key point to build a more appropriate model of GSL elevation time series. In this paper a new model based on fractional autoregressive integrated moving average (ARFIMA) with Stable innovations is applied to analyze the data and predict the future levels. From the analysis we can see that the new model can characterize GSL elevation time series more accurately. The new model will be beneficial to predict GSL elevation more precisely.

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