interest as investment opportunity cost or in other words cost of obtaining the credit requirement in the production process, have a key role in the good's cost price and hence it's necessary to control this kind of variables that have harmful effects on the interest rate's trends. among this, the unexpected changes in some variables that can have destructive effects on interest ...

Conditional heteroskedasticity can be exploited to identify the structural vector autoregressions (SVAR) but the implications for inference on structural impulse responses have not been investigated in detail yet. We consider the conditionally heteroskedastic SVAR-GARCH model and propose a bootstrap-based inference procedure on structural impulse responses. We compare the finite-sample properti...

Abstract An important and widely used class of semiparametric models is formed by the varying-coefficient models. Although varying coefficients are traditionally assumed to be smooth functions, model considered here with coefficient functions containing a finite set discontinuities. Contrary existing nonparametric estimation piecewise under dependence applicable in time series heteroskedastic s...

A computationally simple bias correction for linear dynamic panel data models is proposed and its asymptotic properties are studied when the number of time periods fixed or tends to infinity with units. The approach can accommodate both fixed-effects random-effects assumptions, heteroskedastic errors, as well higher-order autoregressive models. Panel-corrected standard errors that allow robust ...

This paper assesses the effect of expected inflation and inflation risk on interest rates within the Fisher hypothesis framework. Autoregressive Conditional Heteroscedastic models are used to estimate the conditional variability of inflation as a proxy for risk. With the UK quarterly data from 1958:4 to 1994:4, we found that both the expected inflation and the conditional variability of inflati...

This paper shows that the Hinich (1982) bispectrum test for gaussianity and the Hinich and Rothman (1998) test for time reversibility can be used to falsify the null hypothesis that an autoregressive conditionally heteroskedastic model (ARCH) of its generalization (GARCH) generates nonlinear behavior in the variance of an observed time series. The term “falsify” means that the null hypothesis c...

Purpose This paper aims to study the co-movement dynamics of Islamic equity returns explain international portfolio diversification opportunities for investors having a heterogeneous stock holding period in light Brexit. Design/methodology/approach The authors use following three recent methodologies: multivariate generalised autoregressive conditional heteroskedastic-dynamic correlations, cont...

We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to any sequence generation task. We extensively evaluate the proposed model on machine translation (En↔De and En↔Ro) and image caption generation, and observe that...

Since the seminal works of Engle [7] and Bollerslev [3] about heteroskedastic return series models, many extensions of their (G)ARCH models have been proposed in the literature. In particular, the functional dependence of conditional variances and the shape of the conditional distribution of returns have been varied in several ways (see [1] and [5] for an extensive overview). These two issues h...