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Found 12 papers, 0 papers with code
Asymptotics for the Generalized Autoregressive Conditional Duration Model
Engle and Russell (1998, Econometrica, 66:1127--1162) apply results from the GARCH literature to prove consistency and asymptotic normality of the (exponential) QMLE for the generalized autoregressive conditional duration (ACD) model, the so-called ACD(1, 1), under the assumption of strict stationarity and ergodicity.
An identification and testing strategy for proxy-SVARs with weak proxies
We show that frequentist asymptotic inference in these situations can be conducted through Minimum Distance estimation and standard asymptotic methods if the proxy-SVAR can be identified by using `strong' instruments for the non-target shocks; i. e. the shocks which are not of primary interest in the analysis.
Factor Network Autoregressions
We propose a factor network autoregressive (FNAR) model for time series with complex network structures.
The Econometrics of Financial Duration Modeling
We establish new results for estimation and inference in financial durations models, where events are observed over a given time span, such as a trading day, or a week.
Bootstrap inference in the presence of bias
We consider bootstrap inference for estimators which are (asymptotically) biased.
Time-Varying Poisson Autoregression
In this paper we propose a new time-varying econometric model, called Time-Varying Poisson AutoRegressive with eXogenous covariates (TV-PARX), suited to model and forecast time series of counts.
Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models
We show that adaptive information criteria-based approaches can be used to estimate the autoregressive lag order to use in connection with bootstrap adaptive PLR tests, or to jointly determine the co-integration rank and the VAR lag length and that in both cases they are weakly consistent for these parameters in the presence of non-stationary volatility provided standard conditions hold on the penalty term.
Inference in heavy-tailed non-stationary multivariate time series
We propose a novel methodology which does not require any knowledge or estimation of the tail index, or even knowledge as to whether certain moments (such as the variance) exist or not, and develop an estimator of the number of stochastic trends $m$ based on the eigenvalues of the sample second moment matrix of $y_{t}$.
MinP Score Tests with an Inequality Constrained Parameter Space
We show that our tests perform better than/or perform as good as existing score tests in terms of joint testing, and has furthermore the added benefit of allowing for simultaneously testing individual elements of parameter of interest.
Specification tests for GARCH processes
The test statistics considered are of Kolmogorov-Smirnov and Cram\'{e}r-von Mises type, and are based on a certain empirical process marked by centered squared residuals.
Bootstrap Inference for Hawkes and General Point Processes
Inference and testing in general point process models such as the Hawkes model is predominantly based on asymptotic approximations for likelihood-based estimators and tests.
Bootstrapping Non-Stationary Stochastic Volatility
Instead, we use the concept of `weak convergence in distribution' to develop and establish novel conditions for validity of the wild bootstrap, conditional on the volatility process.
