scholarly journals Testing for Granger (non‐)causality in a time‐varying coefficient VAR model

2008 ◽  
Vol 27 (4) ◽  
pp. 293-303 ◽  
Author(s):  
Dimitris K. Christopoulos ◽  
Miguel A. León‐Ledesma
Keyword(s):  
Var Model ◽  
Time Varying ◽  
Varying Coefficient

scholarly journals The Dynamic Impacts of the COVID-19 Pandemic on Log Prices in China: An Analysis Based on the TVP-VAR Model

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 449
Author(s):  
Chenlu Tao ◽  
Gang Diao ◽  
Baodong Cheng
Keyword(s):  
Exchange Rate ◽  
Structural Changes ◽  
Raw Materials ◽  
Supply And Demand ◽  
Oil Prices ◽  
Control Measures ◽  
Var Model ◽  
Time Varying ◽  
Wood Industry ◽  
The Impact

China’s wood industry is vulnerable to the COVID-19 pandemic since wood raw materials and sales of products are dependent on the international market. This study seeks to explore the speed of log price recovery under different control measures, and to perhaps find a better way to respond to the pandemic. With the daily data, we utilized the time-varying parameter autoregressive (TVP-VAR) model, which can incorporate structural changes in emergencies into the model through time-varying parameters, to estimate the dynamic impact of the pandemic on log prices at different time points. We found that the impact of the pandemic on oil prices and Renminbi exchange rate is synchronized with the severity of the pandemic, and the ascending in the exchange rate would lead to an increase in log prices, while oil prices would not. Moreover, the impulse response in June converged faster than in February 2020. Thus, partial quarantine is effective. However, the pandemic’s impact on log prices is not consistent with changes of the pandemic. After the pandemic eased in June 2020, the impact of the pandemic on log prices remained increasing. This means that the COVID-19 pandemic has long-term influences on the wood industry, and the work resumption was not smooth, thus the imbalance between supply and demand should be resolved as soon as possible. Therefore, it is necessary to promote the development of the domestic wood market and realize a “dual circulation” strategy as the pandemic becomes a “new normal”.

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.

A NOTE ON MUTH'S RATIONAL EXPECTATIONS HYPOTHESIS: A TIME-VARYING COEFFICIENT INTERPRETATION

2006 ◽  
Vol 10 (3) ◽  
pp. 415-425 ◽  
Author(s):  
P.A.V.B. SWAMY ◽  
GEORGE S. TAVLAS
Keyword(s):  
Rational Expectations ◽  
Econometric Model ◽  
Objective Probability ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Exact Representation ◽  
Expectations Hypothesis ◽  
Varying Coefficient ◽  
True Model ◽  
Necessary And Sufficient

Under certain interpretations of its coefficients, a specified econometric model is an exact representation of the “true” model, defining the “objective” probability distribution. This note enumerates these interpretations. In the absence of the conditions implied by these interpretations, the econometric model is misspecified. The note shows that model misspecifications prevent the satisfaction of a necessary and sufficient condition for individual expectations to be rational in Muth's sense. Whereas restrictive forms of econometric models can give very inaccurate predictions, this note describes the conditions under which the predictions generated from time-varying coefficient models coincide with the predictions generated from the relevant economic theory.

scholarly journals The Time-Varying Effects of Monetary Policy on House Prices in China: An Application of TVP-VAR Model with Stochastic Volatility

2018 ◽  
Vol 13 (4) ◽  
pp. 149 ◽  
Author(s):  
Weina Cai ◽  
Sen Wang
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
House Prices ◽  
Var Model ◽  
Time Varying ◽  
Credit Channel ◽  
Monetary Policy Shocks ◽  
Time Varying Parameter ◽  
Policy Shocks ◽  
Interest Rate Channel

The boom of housing market in China in recent years has attracted great concerns from all over the world. How monetary policy affects house prices in China becomes an essential topic. This paper studies the time-varying effects of monetary policy on house prices in China during 2005.7-2017.10, by using a time-varying parameter VAR model. This paper obtains three interesting results. First, there are time-varying features of the responses of house prices to monetary policy shocks half-year and 1-year ahead, no matter through interest rate channel or through credit channel. Second, interest rate channel and credit channel have been enhanced since financial crisis in 2008. Third, the responses of nominal house prices to monetary policy in China are mainly driven by the responses of real house prices, instead of inflation. Finally, this paper gives proper suggestions for each finding respectively to central bank in China.

scholarly journals Changing effects of monetary policy in the US–evidence from a time-varying coefficient VAR

2008 ◽  
Vol 40 (18) ◽  
pp. 2353-2360 ◽  
Author(s):  
Florian Höppner ◽  
Christian Melzer ◽  
Thorsten Neumann
Keyword(s):  
Monetary Policy ◽  
Time Varying ◽  
Varying Coefficient ◽  
The Us

scholarly journals Transmission mechanisms in Japan’s quantitative easing policy (2001–2006)

2017 ◽  
Vol 6 (2) ◽  
pp. 35 ◽  
Author(s):  
Hiroyuki Ijiri
Keyword(s):  
Bank Lending ◽  
Var Model ◽  
Quantitative Easing ◽  
Time Varying ◽  
Transmission Channel ◽  
Bank Lending Channel ◽  
Monthly Data ◽  
Lending Channel ◽  
Transmission Mechanisms ◽  
Time Varying Parameter

This study investigates exchange rates and bank lending as the transmission channels for Japan’s Quantitative Easing Policy (QEP) during 2001–2006. Using a Time Varying Parameter-VAR model and monthly data to analyze the dynamism of the QEP, this study is the first to show that the exchange rate channel was the effective QEP transmission channel after around 2005, while the bank lending channel was inactive.

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