scholarly journals Sieve Bootstrap Inference for Time-Varying Coefficient Models

2021 ◽  
Author(s):  
Marina Friedrich
Keyword(s):  
Varying Coefficient Models ◽  
Time Varying ◽  
Sieve Bootstrap ◽  
Varying Coefficient ◽  
Bootstrap Inference

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 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 Forecasting Volatility with Time-Varying Coefficient Regressions

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qifeng Zhu ◽  
Miman You ◽  
Shan Wu
Keyword(s):  
Constant Coefficient ◽  
Stock Exchange ◽  
Composite Index ◽  
Predictive Ability ◽  
Varying Coefficient Models ◽  
Time Varying ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Out Of Sample ◽  
Time Varying Coefficients

We extend the heterogeneous autoregressive- (HAR-) type models by explicitly considering the time variation of coefficients in a Bayesian framework and comprehensively comparing the performances of these time-varying coefficient models and constant coefficient models in forecasting the volatility of the Shanghai Stock Exchange Composite Index (SSEC). The empirical results suggest that time-varying coefficient models do generate more accurate out-of-sample forecasts than the corresponding constant coefficient models. By capturing and studying the time series of time-varying coefficients of the predictors, we find that the coefficients (predictive ability) of heterogeneous volatilities are negatively correlated and the leverage effect is not significant or inverse during certain periods. Portfolio exercises also demonstrate the superiority of time-varying coefficient models.

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