Detrending Time-Aggregated Data
نویسنده
چکیده
This paper examines the combined influences of detrending and time aggregation on the measurement of business cycles. The approximate band-pass filter of Baxter and King (1999) performs relatively well in the sense that it retains the basic shape of disaggregate spectra and cospectra when applied to time aggregated data and is straightforward to apply across sampling intervals. Analysis of known time series processes and actual U.S. macro data, as well as simulation of a standard high-frequency RBC model, confirm the theoretical results. JEL Codes: C1 and E3. ∗To be presented at the 2003 Winter Meetings of the Econometric Society, Washington D.C. The author is an assistant professor in the Department of Economics, Utah State University, 3530 Old Main Hill, Logan, UT, 84322-3530, 435-797-2322 (phone), 435-797-2701 (fax), [email protected].
منابع مشابه
On the trend, detrending, and variability of nonlinear and nonstationary time series.
Determining trend and implementing detrending operations are important steps in data analysis. Yet there is no precise definition of "trend" nor any logical algorithm for extracting it. As a result, various ad hoc extrinsic methods have been used to determine trend and to facilitate a detrending operation. In this article, a simple and logical definition of trend is given for any nonlinear and ...
متن کاملComparison of detrending methods for optimal fMRI preprocessing.
Because of the inherently low signal to noise ratio (SNR) of fMRI data, removal of low frequency signal intensity drift is an important preprocessing step, particularly in those brain regions that weakly activate. Two known sources of drift are noise from the MR scanner and aliasing of physiological pulsations. However, the amount and direction of drift is difficult to predict, even between nei...
متن کاملData-driven detrending of nonstationary fractal time series with echo state networks
In this paper, we propose a novel data-driven approach for removing trends (detrending) from nonstationary, fractal and multifractal time series. We consider real-valued time series relative to measurements of an underlying dynamical system that evolves through time. We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo ...
متن کاملNonlinear trend removal should be carefully performed in heart rate variability analysis
Background : In Heart rate variability analysis, the rate-rate time series suffer often from aperiodic non-stationarity, presence of ectopic beats etc. It would be hard to extract helpful information from the original signals. • Problem : Trend removal methods are commonly practiced to reduce the influence of the low frequency and aperiodic non-stationary in RR data. This can unfortunately affe...
متن کاملForecasting Dynamic Time Series in the Presence of Deterministic Components
This paper studies the error in forecasting a dynamic time series with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model which detrends the data before estimating the dynamic parameters are much less precise than those based on an autoregression which includes the deterministic components. The local asymptotic distribution of the ...
متن کامل