Time-varying parameter auto-regressive models for autocovariance nonstationary time series

2009 ◽  
Vol 52 (3) ◽  
pp. 577-584 ◽  
Author(s):  
WanChun Fei ◽  
Lun Bai
Keyword(s):  
Time Series ◽  
Nonstationary Time Series ◽  
Time Varying ◽  
Time Varying Parameter ◽  
Auto Regressive

scholarly journals An Extended Time Series Algorithm for Modal Identification from Nonstationary Ambient Response Data Only

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang ◽  
Tse-Chuan Tseng
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Identification Problem ◽  
Nonstationary Time Series ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Time Varying ◽  
Varying Parameters ◽  
Response Data ◽  
Time Varying Parameters

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

Online Bayesian Time-varying Parameter Estimation of HIV-1 Time-series*

2012 ◽  
Vol 45 (16) ◽  
pp. 1294-1299 ◽  
Author(s):  
András Hartmann ◽  
Susana Vinga ◽  
João M. Lemos
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Time Varying ◽  
Time Varying Parameter ◽  
Hiv 1

scholarly journals Structural Breaks dan Ketidakstabilan Permintaan Uang di Indonesia

2017 ◽  
Vol 17 (2) ◽  
pp. 169-183
Author(s):  
Deviyantini Deviyantini ◽  
Iman Sugema ◽  
Tony Irawan
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Interest Rate ◽  
Money Demand ◽  
Structural Breaks ◽  
Demand Function ◽  
Series Data ◽  
Time Varying ◽  
Money Demand Function ◽  
Time Varying Parameter

Structural Breaks and Instability of Money Demand in IndonesiaThis research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks, are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The result of Gregory and Hansen test indicates there is no long term equilibrium between variables (money demand, income, domestic interest rate, foreign interest rate, exchange rate, and inflation) in the model, neither M1 nor M2 model. On the other word, money demand function is unstable. The source of the instability is exchange rate variable.Keywords: Stability Money Demand; Structural Breaks; Time Varying Parameter ModelAbstrakPenelitian ini bertujuan untuk mengidentifikasi sumber-sumber ketidakstabilan fungsi permintaan uang (M1 dan M2) akibat dari perubahan struktural yang terjadi karena adanya guncangan ekonomi. Guncangan tersebut, yang secara teknis ditunjukkan oleh keberadaan structural breaks di dalam data, dapat menyebabkan parameter menjadi tidak konstan. Ketidakstabilan fungsi permintaan uang dianalisis dengan menggunakan Gregory and Hansen test. Sumber ketidakstabilan dari permintaan uang diidentifikasi dengan menggunakan time varying parameter model. Penelitian ini menggunakan data time series dalam bentuk kuartalan dari 1993Q1 sampai 2013Q4. Hasil Gregory and Hansen test menunjukkan bahwa tidak ada keseimbangan jangka panjang di antara variabel-variabel (permintaan uang, pendapatan, suku bunga domestik, suku bunga luar negeri, nilai tukar, dan inflasi) di dalam model, baik pada model M1 maupun M2. Dengan kata lain, fungsi permintaan uang tidak stabil. Sumber ketidakstabilan tersebut berasal dari variabel nilai tukar.

scholarly journals Estimation of Dynamic Networks for High-Dimensional Nonstationary Time Series

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 55 ◽  
Author(s):  
Mengyu Xu ◽  
Xiaohui Chen ◽  
Wei Biao Wu
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Dynamic Networks ◽  
Scaling Limit ◽  
Nonstationary Time Series ◽  
Change Points ◽  
High Dimensional ◽  
Time Varying ◽  
Matrix Estimation ◽  
Sample Covariance

This paper is concerned with the estimation of time-varying networks for high-dimensional nonstationary time series. Two types of dynamic behaviors are considered: structural breaks (i.e., abrupt change points) and smooth changes. To simultaneously handle these two types of time-varying features, a two-step approach is proposed: multiple change point locations are first identified on the basis of comparing the difference between the localized averages on sample covariance matrices, and then graph supports are recovered on the basis of a kernelized time-varying constrained L 1 -minimization for inverse matrix estimation (CLIME) estimator on each segment. We derive the rates of convergence for estimating the change points and precision matrices under mild moment and dependence conditions. In particular, we show that this two-step approach is consistent in estimating the change points and the piecewise smooth precision matrix function, under a certain high-dimensional scaling limit. The method is applied to the analysis of network structure of the S&P 500 index between 2003 and 2008.

scholarly journals Large mixed-frequency VARs with a parsimonious time-varying parameter structure

2021 ◽  
Author(s):  
Thomas B Götz ◽  
Klemens Hauzenberger
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Computational Complexity ◽  
Common Factor ◽  
Time Varying ◽  
Frequency Vector ◽  
Mixed Frequency ◽  
Forecasting Models ◽  
Time Varying Parameter ◽  
Joint Dynamics

Summary In order to simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural change, we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables, we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.

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