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Fitting autoregressive models for prediction
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This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models in a stationary time series.Abstract:
This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models. The use of autoregressive representation of a stationary time series (or the innovations approach) in the analysis of time series has recently been attracting attentions of many research workers and it is expected that this time domain approach will give answers to many problems, such as the identification of noisy feedback systems, which could not be solved by the direct application of frequency domain approach [1], [2], [3], [9].read more
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Asymptotic Mean-square Error of Predicting more than One-step ahead Using the Regression Method
TL;DR: In this article, the authors considered the problem of linear least squares prediction of the future values of a discrete, stationary autoregressive process from a realization of past values and gave an expression for the asymptotic mean-square error of predicting more than one-step ahead when the autoregression coefficients are estimated from the sample by solving the Yule-Walker equations.
Journal ArticleDOI
Introducing model uncertainty by moving blocks bootstrap
TL;DR: A Monte Carlo study is presented comparing the finite sample properties of the proposel method with those of alternative methods in the case of prediction intervas.
Journal ArticleDOI
Cointegration and causality between fertility and female labor participation in Taiwan: A multivariate approach
TL;DR: In this article, the authors applied Hsiao's version of Granger causality to female labor participation and failed to find the expected relationship that female participation negatively predicts fertility in Taiwan.
Journal ArticleDOI
A Time-Varying Fiscal Reaction Function for Brazil
TL;DR: In this paper, the authors evaluate the sustainability of public debt in Brazil using monthly data from January 2003 to June 2016, based on the estimation of fiscal reaction functions with time-varying coefficients.
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