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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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Effects of doxazosin on ambulatory blood pressure and sympathetic nervous activity in hypertensive Type 2 diabetic patients with overt nephropathy.
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Journal ArticleDOI
Fractional Bayesian Lag Length Inference in Multivariate Autoregressive Processes
TL;DR: In this article, the posterior distribution of the number of lags in a multivariate autoregression is derived under an improper prior for the model parameters, and the fractional Bayes approach is used to handle the in...
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New technologies and diffusion of innovative financial products: Evidence on exchange-traded funds in selected emerging and developed economies
Adam Marszk,Ewa Lechman +1 more
TL;DR: In this paper, the authors investigated the relationship between adoption of ICTs and the diffusion of exchange-traded funds (ETFs) in 32 emerging and developed economies, using data from the turnover of ETFs on local stock exchanges.
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