scholarly journals Bayesian Analysis of Multiplicative Seasonal Threshold Autoregressive Processes

2020 ◽  
Vol 43 (2) ◽  
pp. 251-284
Author(s):  
Joaquín González Borja ◽  
Fabio Humberto Nieto Sánchez
Keyword(s):  
Time Series ◽  
Autoregressive Process ◽  
Autoregressive Processes ◽  
Model Estimate ◽  
Threshold Autoregressive ◽  
Proposed Model ◽  
Threshold Autoregressive Process ◽  
Identification Stage ◽  
The Relationship ◽  
Special Case

Seasonal fluctuations are  often  found  in many  time  series.   In addition, non-linearity  and  the  relationship  with  other   time series   are  prominent behaviors  of  several,  of  such   series. In this   paper,    we  consider   the modeling  of multiplicative seasonal threshold autoregressive processes with exogenous input (TSARX), which explicitly and simultaneously incorporate multiplicative seasonality and threshold nonlinearity. Seasonality is modeled to  be  stochastic and  regime  dependent.  The  proposed model  is  a  special case  of a  threshold autoregressive process with  exogenous input  (TARX). We  develop   a   procedure  based  on  Bayesian  methods   to   identify  the model,   estimate parameters,  validate  the  model  and  calculate  forecasts. In  the identification stage   of  the  model,   we  present a  statistical test   of regime  dependent multiplicative seasonality.  The proposed methodology is illustrated with a simulated example and applied  to economic empirical data. 

Approximations and boundary conditions for continuous-time threshold autoregressive processes

1994 ◽  
Vol 31 (4) ◽  
pp. 1103-1109 ◽  
Author(s):  
Rob J. Hyndman
Keyword(s):  
Time Series ◽  
Boundary Conditions ◽  
Continuous Time ◽  
Linear Time ◽  
Autoregressive Processes ◽  
Threshold Autoregressive ◽  
The Past ◽  
Time Threshold ◽  
Non Linear ◽  
The Way

Continuous-time threshold autoregressive (CTAR) processes have been developed in the past few years for modelling non-linear time series observed at irregular intervals. Several approximating processes are given here which are useful for simulation and inference. Each of the approximating processes implicitly defines conditions on the thresholds, thus providing greater understanding of the way in which boundary conditions arise.

Approximations and boundary conditions for continuous-time threshold autoregressive processes

1994 ◽  
Vol 31 (04) ◽  
pp. 1103-1109
Author(s):  
Rob J. Hyndman
Keyword(s):  
Time Series ◽  
Boundary Conditions ◽  
Continuous Time ◽  
Linear Time ◽  
Autoregressive Processes ◽  
Threshold Autoregressive ◽  
The Past ◽  
Time Threshold ◽  
Non Linear ◽  
The Way

Continuous-time threshold autoregressive (CTAR) processes have been developed in the past few years for modelling non-linear time series observed at irregular intervals. Several approximating processes are given here which are useful for simulation and inference. Each of the approximating processes implicitly defines conditions on the thresholds, thus providing greater understanding of the way in which boundary conditions arise.

A Bayesian Approach to Robust Skewed Autoregressive Processes

2017 ◽  
Vol 69 (2) ◽  
pp. 165-182 ◽  
Author(s):  
Mohsen Maleki ◽  
Reinaldo B. Arellano-Valle ◽  
Dipak K. Dey ◽  
Mohammad R. Mahmoudi ◽  
Seyed Mohammad J. Jalali
Keyword(s):  
Time Series ◽  
Bayesian Approach ◽  
Time Series Data ◽  
Probability Distributions ◽  
Series Data ◽  
Simulation Studies ◽  
Autoregressive Processes ◽  
Proposed Model ◽  
Ar Models ◽  
Informative Prior Distributions

This article studies autoregressive (AR) models assuming innovations with scale mixtures of skew-normal (SMSN) distributions, an attractive and flexible family of probability distributions. A Bayesian analysis considering informative prior distributions is presented. Comprehensive simulation studies are performed to support the performance of the proposed model and methods. The proposed methods are also applied on a real-time series data which has previously been analysed under Gaussian and Student- t AR models.

The Influence of Cognitive and Affective Reactions to Feedback on Subsequent Goals

2010 ◽  
Vol 15 (2) ◽  
pp. 121-131 ◽  
Author(s):  
Remus Ilies ◽  
Timothy A. Judge ◽  
David T. Wagner
Keyword(s):  
Performance Feedback ◽  
Business School ◽  
Structural Equation ◽  
Emotional Reactions ◽  
Equation Model ◽  
Proposed Model ◽  
And Performance ◽  
Dispositional Measure ◽  
The Relationship ◽  
Reactions To Feedback

This paper focuses on explaining how individuals set goals on multiple performance episodes, in the context of performance feedback comparing their performance on each episode with their respective goal. The proposed model was tested through a longitudinal study of 493 university students’ actual goals and performance on business school exams. Results of a structural equation model supported the proposed conceptual model in which self-efficacy and emotional reactions to feedback mediate the relationship between feedback and subsequent goals. In addition, as expected, participants’ standing on a dispositional measure of behavioral inhibition influenced the strength of their emotional reactions to negative feedback.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

scholarly journals Does a Manager Respond to a Going-Concern Audit Opinion with an Asymmetry in Gain and Loss?

2021 ◽  
Vol 13 (8) ◽  
pp. 4425
Author(s):  
Taewoo Kim
Keyword(s):  
Time Series ◽  
Accounting Conservatism ◽  
Timely Manner ◽  
Audit Opinion ◽  
Going Concern ◽  
Asymmetric Timeliness ◽  
Gain And Loss ◽  
Audit Opinions ◽  
The Relationship

In this paper, I investigate the relationship between previous going-concern audit opinions and subsequent asymmetric timeliness in accounting. Using the time-series and price-based models and conservatism proxy, I find that firms with going-concern audit opinions subsequently report losses in a more timely manner than firms that did not receive going-concern audit opinions. Furthermore, I also find that firms exiting going-concern audit opinions are more likely to report losses rather than gains in a timely manner, compared to firms non-exiting from going-concern opinions. This study extends the prior research by exploring the association between going-concern opinions and accounting conservatism from the perspective of client firms—that is, how firms behave strategically and conservatively to bypass going-concern opinions, once the firms had received previous going-concern opinions.

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