In this paper, we introduce an autoregressive model which has an evolution that is driven by an exogenous pilot signal. This model shares some properties with TAR (Threshold Auto Regressive) models and STAR (Smooth Transition Auto Regressive) models. This text de nes the model, it presents an estimator for this model, and an estimator for the variance of the innovation, which is not constant in...

Increased focus on price stability by a discretionary central bank reduces output and price volatility in a model with rationally inattentive …rms. The volatility reduction can be arbitrarily large, e.g., imply a ‘Great Moderation’, and is particularly pronounced when …rms can process information almost perfectly. The model-implied vector auto-regressive (VAR) dynamics are consistent with the e...

This paper employs a Global Vector Auto Regressive (GVAR) model to study the evolution of response Commonwealth Independent States (CIS) foreign output and oil price shocks. During an observation period two decades, cross-country trade financial linkages experience notable changes. We find CIS countries be highly sensitive global regional shocks, with that sensitivity increasing after crisis. s...

ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. climate catastrophes may cause social and economic tension. forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). weather forecasting is the application of science a...

The existing spatial panel structural vector auto-regressive model can effectively capture the time and dynamic dependence of endogenous variables. However, hypothesis that common factors have same effect for all units is unreasonable. Therefore, incorporating effects, time-individual this paper develops a more general autoregressive with interactive effects (ISpSVAR) reflect different on units...

stochastic, processes can be stationary or nonstationary. they depend on the magnitude of shocks. in other words, in an auto regressive model of order one, the estimated coefficient is not constant. another finding of this paper is the relation between estimated coefficients and residuals. we also develop a catastrophe and chaos theory for change of roots from stationary to a nonstationary one ...

Even in sustained vowels, durations of successive glottal cycles are not identical. They fluctuate quasi-randomly around an average. This phenomenon is known as jitter. More recently, correlation analysis has shown that perturbations of neighboring glottal cycles are interdependent, i.e. they are not purely random. We have shown that the non-random component of jitter can be modeled by means of...

Security problems with network are significant, such as network failures and malicious attacks. Monitoring network traffic and detect anomalies of network traffic is one of the effective manner to ensure network security. In this paper, we propose a hybrid method for network traffic prediction and anomaly detection. Specifically, the original network traffic data is decomposed into high-frequen...

Efficient and robust fault detection and diagnosis (FDD) can potentially play an important role in developing building management systems (BMS) for high performance buildings. Our research indicates that, in comparison to traditional model-based or data-driven methods, the combination of time series modeling and machine learning techniques produces higher accuracy and lower false alarm rates in...

Increased focus on price stability by a discretionary central bank reduces output and price volatility in a model with rationally inattentive …rms. The volatility reduction can be arbitrarily large, e.g., imply a 'Great Moderation', and is particularly pronounced when …rms can process information almost perfectly. The model-implied vector auto-regressive (VAR) dynamics are consistent with the e...