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Nonlinear autoregressive exogenous model

About: Nonlinear autoregressive exogenous model is a research topic. Over the lifetime, 2384 publications have been published within this topic receiving 47179 citations.


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Book ChapterDOI
TL;DR: 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].

2,436 citations

Report SeriesDOI
Stephen Bond1
TL;DR: This paper reviewed econometric methods for dynamic panel data models, and presented examples that illustrate the use of these procedures for the analysis of large number of individuals or firms observed for a small number of time periods.
Abstract: This paper reviews econometric methods for dynamic panel data models, and presents examples that illustrate the use of these procedures The focus is on panels where a large number of individuals or firms are observed for a small number of time periods, typical of applications with microeconomic data The emphasis is on single equation models with autoregressive dynamics and explanatory variables that are not strictly exogenous, and hence on the Generalised Method of Moments estimators that are widely used in this context Two examples using firm-level panels are discussed in detail: a simple autoregressive model for investment rates; and a basic production function

2,200 citations

Journal ArticleDOI
TL;DR: In this article, the authors consider the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) models, and consider the specification of the model based on simple statistical tests: linearity testing against smooth transition autoregression, determining the delay parameter and choosing between LSTAR and ESTAR models.
Abstract: This article considers the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) autoregressive models. This includes the specification of the model based on simple statistical tests: linearity testing against smooth transition autoregression, determining the delay parameter and choosing between LSTAR and ESTAR models are discussed. Estimation by nonlinear least squares is considered as well as evaluating the properties of the estimated model. The proposed techniques are illustrated by examples using both simulated and real time series.

1,883 citations

Journal ArticleDOI
TL;DR: In this paper, a simple yet widely applicable model-building procedure for threshold autoregressive models is proposed based on some predictive residuals, and a simple statistic is proposed to test for threshold nonlinearity and specify the threshold variable.
Abstract: The threshold autoregressive model is one of the nonlinear time series models available in the literature. It was first proposed by Tong (1978) and discussed in detail by Tong and Lim (1980) and Tong (1983). The major features of this class of models are limit cycles, amplitude dependent frequencies, and jump phenomena. Much of the original motivation of the model is concerned with limit cycles of a cyclical time series, and indeed the model is capable of producing asymmetric limit cycles. The threshold autoregressive model, however, has not received much attention in application. This is due to (a) the lack of a suitable modeling procedure and (b) the inability to identify the threshold variable and estimate the threshold values. The primary goal of this article, therefore, is to suggest a simple yet widely applicable model-building procedure for threshold autoregressive models. Based on some predictive residuals, a simple statistic is proposed to test for threshold nonlinearity and specify the ...

977 citations

Proceedings ArticleDOI
19 Aug 2017
TL;DR: Zhang et al. as discussed by the authors proposed a dual-stage attention-based recurrent neural network (DA-RNN) to capture long-term temporal dependencies appropriately and select the relevant driving series to make predictions.
Abstract: The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a.k.a., input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction.

785 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023146
2022261
2021140
2020135
2019142
2018123