Gasoline demand elasticities in the world’s energy gluttons: a time-varying coefficient approach

2021 ◽  
Author(s):  
Chien-Chiang Lee ◽  
Godwin Olasehinde-Williams
Keyword(s):  
Time Varying ◽  
Gasoline Demand ◽  
Varying Coefficient ◽  
Demand Elasticities

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 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 TIME-VARYING COEFFICIENT MODELS: A PROPOSAL FOR SELECTING THE COEFFICIENT DRIVER SETS

2016 ◽  
Vol 21 (5) ◽  
pp. 1158-1174 ◽  
Author(s):  
Stephen G. Hall ◽  
P. A. V. B. Swamy ◽  
George S. Tavlas
Keyword(s):  
Arbitrary Element ◽  
The Other ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Varying Coefficient ◽  
Sovereign Bond ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Bond Spreads

Coefficient drivers are observable variables that feed into time-varying coefficients (TVCs) and explain at least part of their movement. To implement the TVC approach, the drivers are split into two subsets, one of which is correlated with the bias-free coefficient that we want to estimate and the other with the misspecification in the model. This split, however, can appear to be arbitrary. We provide a way of splitting the drivers that takes account of any nonlinearity that may be present in the data, with the aim of removing the arbitrary element in driver selection. We also provide an example of the practical use of our method by applying it to modeling the effect of ratings on sovereign-bond spreads.

Wavelet estimation in time-varying coefficient models

2019 ◽  
Vol 59 (2) ◽  
pp. 276-293
Author(s):  
Xingcai Zhou ◽  
Beibei Ni ◽  
Chunhua Zhu
Keyword(s):  
Varying Coefficient Models ◽  
Time Varying ◽  
Wavelet Estimation ◽  
Varying Coefficient

scholarly journals A Two-Stage Scheduling RPC Based on Time-Varying Coefficient Information of State-Dependent ARX Model

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Feng Zhou ◽  
Peidong Zhu ◽  
Minghua Xie ◽  
Jun Wu ◽  
Lihua Cao
Keyword(s):  
Tracking Control ◽  
State Space Model ◽  
Time Varying ◽  
Two Stage ◽  
Arx Model ◽  
Varying Coefficient ◽  
Output Tracking ◽  
Output Tracking Control ◽  
State Dependent ◽  
Variation Range

A two-stage scheduling robust predictive control (RPC) algorithm, which is based on the time-varying coefficient information of the state-dependent ARX (SD-ARX) model, is designed for the output tracking control of a class of nonlinear systems. First, by using the parameter variation range information of the SD-ARX, a strategy for constructing the system’s polytopic model is designed. To further reduce the conservativeness of the convex polytopic sets which are designed to wrap the system’s future dynamics, the variation range information of the SD-ARX model’s parameters is also considered and compressed. In this method, the polytopic state-space model of the system is constructed directly based on the special structure of the SD-ARX model itself, and there is no need to make such assumption that the bounds on the parameter’s variation range in the system model are known or measurable. And then, a two-stage scheduling RPC algorithm is designed for the output tracking control. A numerical example is presented to demonstrate the effectiveness of the proposed RPC strategy.

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