scholarly journals A Consistent Nonparametric Test for Granger Non-Causality Based on the Transfer Entropy

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1123
Author(s):  
Cees Diks ◽  
Hao Fang
Keyword(s):  
Asymptotic Normality ◽  
Exchange Rates ◽  
Null Hypothesis ◽  
Taylor Expansion ◽  
Kernel Density ◽  
Nonparametric Test ◽  
Transfer Entropy ◽  
First Order ◽  
Stock Indices ◽  
Nonparametric Estimates

To date, testing for Granger non-causality using kernel density-based nonparametric estimates of the transfer entropy has been hindered by the intractability of the asymptotic distribution of the estimators. We overcome this by shifting from the transfer entropy to its first-order Taylor expansion near the null hypothesis, which is also non-negative and zero if and only if Granger causality is absent. The estimated Taylor expansion can be expressed in terms of a U-statistic, demonstrating asymptotic normality. After studying its size and power properties numerically, the resulting test is illustrated empirically with applications to stock indices and exchange rates.

scholarly journals Performance Degradation in Cross-Eye Jamming Due to Amplitude/Phase Instability between Jammer Antennas

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5027
Author(s):  
Je-An Kim ◽  
Joon-Ho Lee
Keyword(s):  
Performance Analysis ◽  
Phase Difference ◽  
Taylor Expansion ◽  
Amplitude Ratio ◽  
Random Variables ◽  
Performance Degradation ◽  
Analytical Performance ◽  
Phase Instability ◽  
Gaussian Random Variables ◽  
First Order

Cross-eye gain in cross-eye jamming systems is highly dependent on amplitude ratio and the phase difference between jammer antennas. It is well known that cross-eye jamming is most effective for the amplitude ratio of unity and phase difference of 180 degrees. It is assumed that the instabilities in the amplitude ratio and phase difference can be modeled as zero-mean Gaussian random variables. In this paper, we not only quantitatively analyze the effect of amplitude ratio instability and phase difference instability on performance degradation in terms of reduction in cross-eye gain but also proceed with analytical performance analysis based on the first order and second-order Taylor expansion.

scholarly journals The Effect of Exchange Value on the Stock Index Listed in Indonesia Stock Exchange during Covid-19 Pandemic

2021 ◽  
Vol 2 (1) ◽  
pp. 6-14
Author(s):  
Zahra Zhafira ◽  
Einde Evana ◽  
Ratna Septiyanti
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Exchange Rates ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Stock Index ◽  
Simple Linear Regression ◽  
Simple Linear Regression Analysis ◽  
Stock Indices

This study aims to examine the effect of exchange rates on the stock index during the Covid-19 pandemic. This research was conducted using secondary data. The population in this study were all stock indices listed on the Indonesia Stock Exchange with a sample size of 89 and a total stock index of 34. The study period was 4 months, 17 January 2020 to 20 May 2020. The sample data collection of this study used the purposive method. Sampling with world economic conditions and Indonesia which are weakening due to the Covid-19 pandemic and based on the phenomenon that the exchange rate is experiencing a continuous movement even every year the exchange rate depreciates IDR against the US Dollar. One of the causes of the high fluctuation of the rupiah exchange rate against the dollar came from economic factors such as inflation, the interest rate on Bank Indonesia certificates during the Covid-19 pandemic. This study uses a simple linear regression analysis method using SPSS V.26. The results of simple linear regression analysis show that exchange rates have a negative and significant effect on all stock indices listed on the Indonesia Stock Exchange, these results have similarities or differences with the results of research in other emerging market countries.

scholarly journals PARAMETER ESTIMATION IN NONLINEAR AR–GARCH MODELS

2011 ◽  
Vol 27 (6) ◽  
pp. 1236-1278 ◽  
Author(s):  
Mika Meitz ◽  
Pentti Saikkonen
Keyword(s):  
Asymptotic Normality ◽  
Autoregressive Models ◽  
Garch Models ◽  
Martingale Difference ◽  
First Order ◽  
First Results ◽  
Consistency And Asymptotic Normality ◽  
Quasi Maximum Likelihood ◽  
Nonlinear Autoregressive ◽  
Nonlinear Autoregressive Models

This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally heteroskedastic errors. We consider a general nonlinear autoregression of order p (AR(p)) with the conditional variance specified as a general nonlinear first-order generalized autoregressive conditional heteroskedasticity (GARCH(1,1)) model. We do not require the rescaled errors to be independent, but instead only to form a stationary and ergodic martingale difference sequence. Strong consistency and asymptotic normality of the global Gaussian quasi-maximum likelihood (QML) estimator are established under conditions comparable to those recently used in the corresponding linear case. To the best of our knowledge, this paper provides the first results on consistency and asymptotic normality of the QML estimator in nonlinear autoregressive models with GARCH errors.

scholarly journals ASYMPTOTIC THEORY FOR NONLINEAR QUANTILE REGRESSION UNDER WEAK DEPENDENCE

2015 ◽  
Vol 32 (3) ◽  
pp. 686-713 ◽  
Author(s):  
Walter Oberhofer ◽  
Harry Haupt
Keyword(s):  
Quantile Regression ◽  
Asymptotic Normality ◽  
Regression Model ◽  
Asymptotic Theory ◽  
Asymptotic Properties ◽  
Covariance Estimation ◽  
First Order ◽  
Regression Quantiles ◽  
Quantile Regression Model ◽  
Consistency And Asymptotic Normality

This paper studies the asymptotic properties of the nonlinear quantile regression model under general assumptions on the error process, which is allowed to be heterogeneous and mixing. We derive the consistency and asymptotic normality of regression quantiles under mild assumptions. First-order asymptotic theory is completed by a discussion of consistent covariance estimation.

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