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Neglecting parameter changes in GARCH models
Selection of the break in the Perron-type tests
Testing for structural change in regression with long memory processes
长记忆回归过程中的结构变化检验
The paper considers tests for structural change in time series regression models where both regressors and residuals may exhibit long range dependence. The limiting distribution of the test statistic depends on unknown parameters and is approximated by a bootstrap procedure. The asymptotic validity of bootstrap is shown and performance of the testing procedure is examined in a simple Monte Carlo experiment.
Renewal regime switching and stable limit laws
Cointegration in fractional systems with deterministic trends
Structural breaks with deterministic and stochastic trends
Modelling structural breaks, long memory and stock market volatility: an overview
Small sample properties of forecasts from autoregressive models under structural breaks
Robust GMM tests for structural breaks
A parametric bootstrap test for cycles
周期性的参数自助检验
The paper proposes a simple test for the hypothesis of strong cycles and as a by-product a test for weak dependence for linear processes. We show that the limit distribution of the test is the maximum of a (semi)Gaussian process G (τ), τ ∈ [0; 1]. Because the covariance structure of G (τ) is a complicated function of τ and model dependent, to obtain the critical values (if possible) of maxτ∈[0;1] G (τ) may be difficult. For this reason we propose a bootstrap scheme in the…
Generating schemes for long memory processes: regimes, aggregation and linearity
Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects
具有嵌套随机效应的受限与离散因变量模型的最大似然估计
Gauss-Hermite quadrature is often used to evaluate and maximize the likelihood for random component probit models. Unfortunately, the estimates are biased for large cluster sizes and/or intraclass correlations. We show that adaptive quadrature largely overcomes these problems. We then extend the adaptive quadrature approach to general random coefficient models with limited and discrete dependent variables. The models can include several nested random effects (intercepts and…
The distance between rival nonstationary fractional processes
Size and power of tests of stationarity in highly autocorrelated time series
Sign tests for long-memory time series
Bootstrap inference in systems of single equation error correction models
Combining estimators to improve structural model estimation and inference under quadratic loss
Quasi-maximum likelihood estimation for conditional quantiles
VAR forecasting under misspecification
Robust efficient method of moments
A nonparametric test for changing trends
Subsampling inference in threshold autoregressive models
门限自回归模型中的子抽样推断
This paper discusses inference in self exciting threshold autoregressive (SETAR) models. Of main interest is inference for the threshold parameter. It is well-known that the asymptotics of the corresponding estimator depend upon whether the SETAR model is continuous or not. In the continuous case, the limiting distribution is normal and standard inference is possible. In the discontinuous case, the limiting distribution is non-normal and cannot be estimated consistently. We…