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A comparison of minimum MSE and maximum power for the nearly integrated non-Gaussian model
τ-estimators of regression models with structural change of unknown location
A consistent estimator for the binomial distribution in the presence of “incidental parameters”: an application to patent data
Testing for unit roots with flow data and varying sampling frequency
Deterministic least squares filtering
System theory for system identification
The relation of the CCA subspace method to a balanced reduction of an autoregressive model
The asymptotic variance of subspace estimates
Nonlinear instrumental variable estimation of an autoregression
Modeling of time series arrays by multistep prediction or likelihood methods
Generalized Levinson–Durbin and Burg algorithms
An omnibus test for the time series model AR(1)
A complete class of tests when the likelihood is locally asymptotically quadratic
Robust estimation of generalized linear models with measurement errors
Variance expressions for spectra estimated using auto-regressions
Bootstrapping nonparametric estimators of the volatility function
Least squares in general vector spaces revisited
Estimation of simultaneous systems of spatially interrelated cross sectional equations
Contributions to econometrics, time-series analysis, and systems identification: a Festschrift in honor of Manfred Deistler
Aggregation of space-time processes
Strong rules for detecting the number of breaks in a time series
检测时间序列结构突变数量的强一致规则
This paper proposes a new approach for detecting the number of structural breaks in a time series when estimation of the breaks is performed one at the time. We consider the case of shifts in the mean of a possibly nonlinear process, allowing for dependent and heterogeneous observations. This is accomplished through a simple, sequential, almost sure rule ensuring that, in large samples, both the probabilities of overestimating and underestimating the number of breaks are…