Robust methods for detecting multiple level breaks in autocorrelated time series
نویسندگان
چکیده
منابع مشابه
Detecting shocks: Outliers and breaks in time series
A single outlier in a regression model can be detected by the effect of its deletion on the residual s irn of squares. An equivalent procedure is the simple intervention in which an extra parameter is added for the mean of the observation in question. Similarly, for unobserved components or structural time-series models, the effect of elaborations of the model on inferences can be investigated ...
متن کاملHAC Corrections for Strongly Autocorrelated Time Series∗
Applied work routinely relies on heteroskedasticity and autorcorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations. In this paper, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference tha...
متن کاملWhich OIC countries are catching up? Time Series Evidences with Multiple Structural Breaks
Abstract In this paper, income per capita convergence hypothesis is tested in selected OIC countries. For this purpose, we use the time series model and univariate KPSS stationary test with multiple structural breaks (Carrion-i-Silvestre et al. (2005)) over the period 1950-2008. The results show that most OIC countries could not catch up toward USA. Although because of some positive term of tra...
متن کاملDetecting multiple mean breaks at unknown points in official time series
In this paper, we propose a computationally effective approach to detect multiple structural breaks in the mean occurring at unknown dates. We present a non-parametric approach that exploits, in the framework of least squares regression trees, the contiguity property of data generating processes in time series data. The proposed approach is applied first to simulated data and then to the Quarte...
متن کاملConfidence intervals in stationary autocorrelated time series
In this study we examine in covariance stationary time series the consequences of constructing confidence intervals for the population mean using the classical methodology based on the hypothesis of independence. As criteria we use the actual probability the confidence interval of the classical methodology to include the population mean (actual confidence level), and the ratio of the sampling e...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Econometrics
سال: 2010
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2010.02.003