Dynamic Instrument Rules Based on Time Varying Coefficients Vector Autoregressive Modeling and Forecast-Based Monetary Policy

2005 ◽  
Author(s):  
Koiti Yano ◽  
Seisho Sato
Keyword(s):  
Monetary Policy ◽  
Time Varying ◽  
Autoregressive Modeling ◽  
Vector Autoregressive ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Vector Autoregressive Modeling

scholarly journals The Monetary Transmission Mechanism in Canada: A Time-Varying Vector Autoregression with Stochastic Volatility

2018 ◽  
Vol 5 (6) ◽  
pp. 42
Author(s):  
Ronald Henry Lange
Keyword(s):  
Monetary Policy ◽  
Private Sector ◽  
Stochastic Volatility ◽  
Vector Autoregression ◽  
Transmission Mechanism ◽  
Space Representation ◽  
Output Gap ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Varying Coefficients

This study uses the state-space representation of a time-varying vector autoregression with stochastic volatility (TVP-VAR-SV) to study monetary policy and private sector behaviour in Canada. The main results indicate that both shock variances and autoregressive coefficients of the VAR have evolved systematically over time. The time-varying coefficients of the systematic component of the VAR suggest that monetary policy has become more proactive and less reactive regarding inflation since the early-1990s, which coincides with the adoption of explicit inflation targets. Monetary policy is now able to focus mainly on movements in the output gap to prevent future increases in inflation. The coefficients on the policy rate in both the output gap and inflation equations suggest that the private sector and therefore the transmission mechanism have become more sensitive to monetary policy responses. On the other hand, the coefficients on the output gap in the equations for both inflation and the policy rate have been relatively stable over this period, consistent with view that monetary policy remains more forward-looking regarding inflation than being reactive to inflation surprises as in the past.

MULTIVARIATE TIME-VARYING AUTOREGRESSIVE MODELING OF FETAL SYMPATHO-VAGAL BALANCE THROUGH GESTATION

2013 ◽  
Vol 25 (01) ◽  
pp. 1350014 ◽  
Author(s):  
D. Gutiérrez
Keyword(s):  
High Specificity ◽  
Time Varying ◽  
Autoregressive Modeling ◽  
Short Term ◽  
Data Set ◽  
Varying Coefficients ◽  
Non Invasive ◽  
Time Varying Coefficients ◽  
The Time Domain ◽  
Short Term Variability

A processing framework is proposed to model relative changes in fetal sympatho-vagal balance at equally spaced gestational periods. The proposed method is based on a multivariable time-varying autoregression (TVAR) of the beat-to-beat time differences obtained from non-invasive fetal electrocardiographic (ECG) or magnetocardiographic (MCG) measurements. In order to quantify the sympatho-vagal balance at each measured gestational period, the ratio between the standard deviation of normal-to-normal (SDNN) beat intervals and the sum of absolute differences (SAD) is computed. While the SDNN quantifies the overall variability of the sympathetic and vagal systems, the SAD enhances short-term variability components related to vagal control, then the ratio of these two compares with high specificity the overall variability against the short-term vagal component in the time domain. The SDNN/SAD ratio is used to form a new data set by removing short-term variability events, then leaving only those corresponding to longer-term sympatho-vagal balance. The new data set is then analyzed as a dynamical system by fitting it to a suitable multivariate TVAR, and relative changes in the sympatho-vagal balance through the analyzed gestational periods are assumed to be related to the dynamics of the time-varying coefficients of the TVAR. In order to demonstrate the applicability of the proposed method, simulated and real fetal E/MCG data are analyzed. The results show that the modeling approach is able to infer the expected trend seen through sympatho-vagal development.

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

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