scholarly journals Wavelet based time-varying vector autoregressive modelling

2007 ◽  
Vol 51 (12) ◽  
pp. 5847-5866 ◽  
Author(s):  
João R. Sato ◽  
Pedro A. Morettin ◽  
Paula R. Arantes ◽  
Edson Amaro
Keyword(s):  
Time Varying ◽  
Autoregressive Modelling ◽  
Vector Autoregressive

scholarly journals The Time-Varying Effect of Monetary Policy on Asset Prices

2020 ◽  
Vol 102 (4) ◽  
pp. 690-704 ◽  
Author(s):  
Pascal Paul
Keyword(s):  
Monetary Policy ◽  
Asset Prices ◽  
House Prices ◽  
The United States ◽  
Model Parameters ◽  
Time Varying ◽  
Real Economy ◽  
Monetary Policy Shocks ◽  
Vector Autoregressive ◽  
Policy Shocks

This paper studies how monetary policy jointly affects asset prices and the real economy in the United States. I develop an estimator that uses high-frequency surprises as a proxy for the structural monetary policy shocks. This is achieved by integrating the surprises into a vector autoregressive model as an exogenous variable. I use current short-term rate surprises because these are least affected by an information effect. When allowing for time-varying model parameters, I find that compared to the response of output, the reaction of stock and house prices to monetary policy shocks was particularly low before the 2007–2009 financial crisis.

Instantaneous Spectral Analysis of Non-Stationary Ship Motion Data

2006 ◽  
Author(s):  
Toshio Iseki
Keyword(s):  
Spectral Analysis ◽  
State Space Model ◽  
Ship Motion ◽  
Ship Motions ◽  
Time Varying ◽  
Vector Autoregressive ◽  
Varying Coefficient ◽  
Motion Data ◽  
Stationary Conditions ◽  
Noise Contribution

The time varying coefficient vector autoregressive (TVVAR) modeling is applied to the cross-spectral analysis of non-stationary ship motion data. Introducing the instantaneous response, a vector autoregressive model can be reduced to simple time varying coefficient autoregressive (TVAR) models for each ship motion and the required CPU time is effectively reduced. The TVVAR model and stochastic perturbed difference equations are transformed into a state space model. The vector-valued unknown coefficients can be evaluated and the instantaneous cross spectra of ship motions can be calculated at every moment. The results showed good agreements with one of the TVAR modeling and also with the stationary autoregressive (SAR) modeling analysis under stationary conditions. Furthermore, the instantaneous relative noise contribution was also estimated using the TVVAR coefficients and illustrated how the structure of a spectrum changed according to the ship manoeuvres for the first time. Optimum order of the model and Akaike’s information criterion were also examined for several changes of parameters. Moreover, it is confirmed that the TVVAR modeling can estimate the instantaneous cross spectra and relative noise contribution of ship motions even under non-stationary conditions.

scholarly journals Construction of China’s financial conditions index in the post-crisis era

2019 ◽  
Vol 2 (2) ◽  
pp. 258-276
Author(s):  
Nan Li ◽  
Liu Yuanchun
Keyword(s):  
Financial Stability ◽  
Design Methodology ◽  
Predictive Power ◽  
Time Varying ◽  
Content Type ◽  
Vector Autoregressive ◽  
Financial Variables ◽  
Financial Conditions ◽  
Time Varying Parameter ◽  
Better Than

Purpose The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations. Design/methodology/approach The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation. Findings Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI. Originality/value FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.

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