Time-varying Autoregressive Parameters Research Articles

ÇEKİRDEK, GIDA VE ENERJİ ENFLASYONLARINDA KALICILIĞIN ARAŞTIRILMASI: ZAMANLA DEĞİŞEN YAKLAŞIMDAN BİR KANIT

This study examines how inflation persistence evolves over time in Euro Area, the UK, the US and OECD countries over the period of 1999:07-2021:09. We consider the persistence of core, energy, and food inflation, as well total inflation. Using an ARMA(1,1) model with time-varying autoregressive parameters based on the Kalman filter (TV-AR), we find that inflation persistence increases significantly during crisis periods and total persistence peaks during the COVID-19 pandemic. However, the persistence of energy inflation decreases due to the economic slowdown during the COVID-19 pandemic. These results highlight how inflation persistence is related to the objectives and conduct of monetary policy. The findings indicate that the various measures taken by the governments such as containment and closure policies, travel restrictions to prevent the spread of COVID-19 cause to increase in total and food inflation persistences. Energy inflation persistence declines due to a slowdown in production during the COVID-19 period.

Time Varying Autoregressive Model Parameters Estimation using Discrete Energy Separation Algorithm

Time Varying Autoregressive (TVAR) model for the Amplitude and Frequency modulated (AM-FM) signal is presented In this paper. TVAR parameters of AM-FM signal are estimated using Discrete Energy Separation (DESA) Algorithm. The performance of DESA method is shown to be comparable to the existing basis function method for AM, FM, AM-FM signal models. The proposed method is simpler to execute in hardware and consumes considerably less computational resources compared to the method using Adaptive and the Basis function methods. .It is demonstrated that the proposed technique based on DESA has certain distinct advantages over the conventional method employing basis functions. Another advantage is that the present method works well with quickly varying signals

A Forward–Backward Approach for Instantaneous Frequency Estimation of Frequency Modulated Signals in Noisy Environment

In this paper a forward–backward basis function approach for instantaneous frequency estimation of the frequency-modulated signal in noisy environment is presented. At first, a forward–backward prediction approach is applied for least squares estimation of time-varying autoregressive parameters. A time-varying parameters are expressed as a summation of constants multiplied by basis functions. Then, the time-varying frequencies are extracted from the time-varying autoregressive parameters by calculating the angles of the estimation error filter polynomial roots. The experimental results are presented, which shows the superiority of the proposed method against the covariance (forward prediction) approach.

Adaptive Radar Detection in Doubly Nonstationary Autoregressive Doppler Spread Clutter

The problem of adaptive radar detection in clutter which is nonstationary both in slow and fast time is addressed. Nonstationarity within a coherent processing interval (CPI) often precludes target detection because of the masking induced by Doppler spreading of the clutter. Across range bins (i.e., fast time), nonstationarity severely limits the amount of training data available to estimate the noise covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in complex multipath propagation conditions where path lengths can change abruptly in dynamic scenarios. To mitigate nonstationary Doppler spread clutter, an approximation to the generalized likelihood ratio test (GLRT) detector is presented wherein the CPI from the hypothesized target range is used for both clutter estimation and target detection. To overcome the lack of training data, a modified time-varying autoregressive (TVAR) model is assumed for the clutter return. In particular, maximum likelihood (ML) estimates of the TVAR parameters, computed from a single snapshot of data, are used in a GLRT for detecting stationary targets in possibly abruptly nonstationary clutter. The GLRT is compared with three alternative methods including a conceptually simpler ad hoc approach based on extrapolation of quasi-stationary data segments. Detection performance is assessed using simulated targets in both synthetically-generated and real radar clutter. Results suggest the proposed GLRT with TVAR clutter modeling can provide between 5-8 dB improvement in signal-to-clutter plus noise ratio (SCNR) when compared with the conventional methods.

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

In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TVPAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.

COMPARING TIME-VARYING AUTOREGRESSIVE STRUCTURES OF LOCALLY STATIONARY PROCESSES

In this paper, a novel statistical test is introduced to compare two locally stationary time series. The proposed approach is a Wald test considering time-varying autoregressive modeling and function projections in adequate spaces. The covariance structure of the innovations may be also time-varying. In order to obtain function estimators for the time-varying autoregressive parameters, we consider function expansions in splines and wavelet bases. Simulation studies provide evidence that the proposed test has a good performance. We also assess its usefulness when applied to a financial time series.

Pattern Recognition of Non-Stationary Time Series with Finite Length

Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time varying parameter auto-regressive (TVPAR) model. After the system was taught to recognize the size data, the system correctly recognized the size of series of cocoon filaments as much as 96.95% of the time for a single series and 98.72% of the time for the mean of two series. The correct recognition rate was higher after suitable filtering. The theory and method can be used to analyze other types of non-stationary finite length time series.

On recursive estimation for time varying autoregressive processes

This paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and uniform results are provided when the time-varying autoregressive parameters belong to appropriate smoothness classes. An adequate normalization for the correction term used in the recursive estimation procedure allows for very mild assumptions on the innovations distributions. The rate of convergence of the pointwise estimates is shown to be minimax in $\beta$-Lipschitz classes for $0<\beta\leq1$. For $1<\beta\leq 2$, this property no longer holds. This can be seen by using an asymptotic expansion of the estimation error. A bias reduction method is then proposed for recovering the minimax rate.

Conditions for the Equivalence of the Autoregressive Latent Trajectory Model and a Latent Growth Curve Model With Autoregressive Disturbances

Curran and Bollen combined two models for longitudinal panel data: the latent growth curve model and the autoregressive model. In their model, the autoregressive relationships are modeled between the observed variables. This is a different model than a latent growth curve model with autoregressive relationships between the disturbances. However, when the autoregressive parameter is invariant over time and lies between-1 and 1, it can be shown that these models are algebraically equivalent. This result can be shown to generalize to the multivariate case. When the autoregressive parameters in the autoregressive latent trajectory model vary over time, the equivalence between the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances no longer holds. However, a latent growth curve model with time-varying autoregressive parameters for the disturbances could be considered an interesting alternative to the autoregressive latent trajectory model with time-varying autoregressive parameters.

Particle methods for Bayesian modeling and enhancement of speech signals

This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diffusion processes. The main aim of the paper is to perform on-line estimation of the clean speech and model parameters and to determine the adequacy of the chosen statistical models. Efficient particle methods are developed to solve the optimal filtering and fixed-lag smoothing problems. The algorithms combine sequential importance sampling (SIS), a selection step and Markov chain Monte Carlo (MCMC) methods. They employ several variance reduction strategies to make the best use of the statistical structure of the model. It is also shown how model adequacy may be determined by combining the particle filter with frequentist methods. The modeling and enhancement performance of the models and estimation algorithms are evaluated in simulation studies on both synthetic and real speech data sets.

Modeling general distributed nonstationary process and identifying time-varying autoregressive system by wavelets: theory and application

In this paper, some new techniques for time-varying parametric autoregressive (AR) system identification by wavelets are presented. Firstly, we derive a new multiresolution least squares (MLS) algorithm for Gaussian time-varying AR model identification employing wavelet operator matrix representation. This method can optimally balance between the over-fitted solution and the poorly represented identification. The main features of the time-varying model parameters are estimated by a multiresoulution method, which represents the smooth trends as well as the rapidly changing components. Combining the total least squares algorithm with the MLS algorithm, a new method is presented which can make the identification of a noisy time-varying AR model. Finally, we deal with a non-Gaussian time-varying AR model for modeling nonstationary processes in a non-Gaussian distribution. A pseudo-maximum likelihood estimation algorithm is proposed for this model identification. The time-varying AR parameters as well as the non-Gaussian probability density (approximated by Gaussian mixture density) parameters of the driving noise sequence (DNS) are simultaneously estimated. Simulation results verify that our methods can effectively identify time-varying AR systems with general distributed DNS. A realistic application of the proposed technique in blind equalization of time-varying fading channel will be explored.

Analysis of Acoustic Signatures from Moving Vehicles UsingTime-Varying Autoregressive Models

Time-varying autoregressive (TVAR) modeling approach for the analysis of acoustic signatures from moving vehicles is presented in this paper. Acoustic signatures from moving vehicles are nonstationary, and features extracted under the stationary assumption often result unsatisfactory performance. In TVAR modeling approach, the time-varying parameters are expanded as a linear combination of deterministic time functions. In this paper, the TVAR parameters are expanded by a low-order discrete cosine transform (DCT), since DCT is known to be close to the optimal Kahrunen-Loeve transform when the signal is Markov. The maximum likelihood estimation and order selection in TVAR models are also discussed. Many attributes of vehicle activities, such as vehicle type, engine speed, loading, road condition, etc., may be inferred from the estimated model parameters. The performance of the TVAR modeling approach is tested with both synthetic and real acoustic signatures. A synthetic signal containing multiple time-varying sinusoids are used to compare the performances in the estimation of time-frequency distribution with other approaches. In the experiment with acoustic signatures from moving vehicles, it is shown that the TVAR models can be effectively used to determine vehicle activities and types at close range and cruising speed.

Estimation of time‐varying autoregressive model parameters by multilayered neural networks: Identification of local stationary sections from noise variance

This paper proposes a method of estimating time-varying autoregressive (AR) model parameters by means of multilayered neural networks (NNs). First, an estimation method for time-varying AR parameters is discussed under the assumption of local stationarity. Then it is shown that the section length T satisfying local stationarity is very important in maintaining the balance of estimation precision for both noise variance and time-varying AR parameters. Next, on the basis of the nonlinear Yule-Walker equation, the noise variance is estimated by NNs. The section length T satisfying local stationarity is determined by estimated noise variance, and for the specified section length T, time-varying AR parameters are estimated by the Yule-Walker method. Finally, from numerical simulation results it is shown that the proposed method is very effective for the estimation of time-varying AR model parameters. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(7): 20–27, 1997