|
bootstrap method |
4 |
|
likelihood ratio test |
4 |
|
ar(p) model |
3 |
|
buffered ar model |
3 |
|
buffered ar(p) model |
3 |
|
buffered ar-garch model |
3 |
|
e-learning |
3 |
|
exchange rate |
3 |
|
face-to-face learning |
3 |
|
garch model |
3 |
|
marked empirical process |
3 |
|
nonlinear time series |
3 |
|
online teaching |
3 |
|
randomized controlled experiment |
3 |
|
teaching effectiveness |
3 |
|
threshold ar model |
3 |
|
threshold ar(p) model |
3 |
|
asymmetric innovation |
2 |
|
block-wise random weighting method |
2 |
|
conditionally heteroscedastic model |
2 |
|
diagnostic checking |
2 |
|
exchange rates |
2 |
|
least squares estimation |
2 |
|
leptokurtic innovation |
2 |
|
non-gaussian qmle |
2 |
|
pearsonian qmle |
2 |
|
pearson’s type iv distribution |
2 |
|
portmanteau test |
2 |
|
spectral test |
2 |
|
stock indexes |
2 |
|
weak arma models |
2 |
|
wild bootstrap |
2 |
|
adaptive estimator |
1 |
|
adaptive inference |
1 |
|
additive model |
1 |
|
ar(p) |
1 |
|
arch-type model |
1 |
|
arma(p, q) models |
1 |
|
arma-garch model |
1 |
|
arma-garch/igarch model |
1 |
|
asymmetric ewma model |
1 |
|
asymptotic normality |
1 |
|
augmented dar model |
1 |
|
autoregressive model |
1 |
|
big data |
1 |
|
causality-in-mean |
1 |
|
causality-in-variance |
1 |
|
cointegration |
1 |
|
conditional asset pricing model |
1 |
|
conditional heteroscedasticity |
1 |
|
conditional quantile |
1 |
|
consistency |
1 |
|
covariance matrix time series model |
1 |
|
dar model |
1 |
|
darwin model |
1 |
|
double ar(p) model |
1 |
|
dynamic loadings |
1 |
|
egarch and gjr models |
1 |
|
endogeneity |
1 |
|
factor dar model |
1 |
|
g/arch noises |
1 |
|
generalized exponentially weighted moving average quantile model |
1 |
|
generalized quasi-maximum likelihood estimator |
1 |
|
geometric brownian motion |
1 |
|
global self-weighted/local quasi-maximum exponential likelihood estimator |
1 |
|
heavy-tailed innovation |
1 |
|
heavy-tailed noises |
1 |
|
heavy-tailedness |
1 |
|
heteroscedasticity |
1 |
|
hilbert-schmidt independence criterion |
1 |
|
instantaneous causality |
1 |
|
lad estimator |
1 |
|
lade |
1 |
|
lagrange multiplier test |
1 |
|
long memory regressor |
1 |
|
lyapunov exponent |
1 |
|
machine learning |
1 |
|
maximum likelihood estimation |
1 |
|
mixed portmanteau test |
1 |
|
model check |
1 |
|
model diagnostics |
1 |
|
multivariate time series models |
1 |
|
neural networks |
1 |
|
ngarch |
1 |
|
non-linear dependence |
1 |
|
non-normal innovation |
1 |
|
non-standard asymptotics |
1 |
|
nonlinear quantile factor model |
1 |
|
nonlinear regression |
1 |
|
nonstationary arma |
1 |
|
non‐stationarity |
1 |
|
option valuation |
1 |
|
parameter on the boundary |
1 |
|
power generalized auto-regressive conditional heteroscedasticity models |
1 |
|
profiled quasi maximum likelihood estimation |
1 |
|
qmele and strong consistency |
1 |
|
qmle |
1 |
|
quantile time series model |
1 |
|
quasi-maximum exponential likelihood estimator |
1 |
|
random weighting approach |
1 |
|
random-weighting approach |
1 |
|
realized covariance matrix |
1 |
|
residual acfs |
1 |
|
residual bootstrap |
1 |
|
risk neutralized measure |
1 |
|
score test |
1 |
|
self-weighted lade |
1 |
|
semiparametric bekk model |
1 |
|
semiparametric garch model |
1 |
|
semiparametric time series model |
1 |
|
sign-based portmanteau test |
1 |
|
specification testing |
1 |
|
squared residual acfs |
1 |
|
stability test |
1 |
|
strong consistency |
1 |
|
structural change testing |
1 |
|
structure change |
1 |
|
testing for independence |
1 |
|
threshold ar(p) |
1 |
|
top lyapunov exponent |
1 |
|
two-parameter gaussian process |
1 |
|
value at risk |
1 |
|
variational autoencoder |
1 |
|
volatility model |
1 |
|
volatility skew |
1 |
|
weak auto-regressive moving average models |
1 |
|
weak convergence |
1 |
|
weighted least absolute deviations estimator |
1 |
|
weighted portmanteau test |
1 |
|
zero-drift garch model |
1 |
