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Estimating Nonlinearities in Spatial Autoregressive Models [PDF]

, 2010
In spatial autoregressive models, the functional form of autocorrelation is assumed to be linear. In this paper, we propose a simple semiparametric procedure, based on Yatchew's (1998) partial linear least squares, that relaxes this restriction. Simple simulations show that this model outperforms traditional SAR estimation when nonlinearities are ...
Nicolas Debarsy, Vincenzo Verardi
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On geometric ergodicity of nonlinear autoregressive models

Statistics & Probability Letters, 1995
The authors prove elegantly the geometric ergodicity of a general class of nonlinear \(k\)-th order autoregressive processes \(X_n\), by considering \(k\)-tuples \((X_{n + 1 - k}, \ldots, X_n)\) as Markov processes on the state-space \({\mathcal R}^k\).
Bhattacharya, Rabi, Lee, Chanho
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Nonlinear impact estimation in spatial autoregressive models

Economics Letters, 2018
This paper extends the literature on the calculation and interpretation of impacts for spatial autoregressive models. Using a Bayesian framework, we show how the individual direct and indirect impacts associated with an exogenous variable introduced in a nonlinear way in such models can be computed, theoretically and empirically.
Ay, Jean-Sauveur, Ayouba, Kassoum, Le Gallo, Julie   +2 more
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Ergodicity of nonlinear first order autoregressive models

Journal of Theoretical Probability, 1995
Let the Markov chain \(\{X_ n, n \geq 0\}\) be defined by \[ X_{n + 1} = f(X_ n) + \sigma(X_ n) \varepsilon_{n + 1},\quad n \geq 0, \] where \(f\), \(\sigma\) are measurable, \(f\) is bounded on compacts, \(0 < \sigma_ 1 \leq \sigma(x) \leq \sigma_ 2 < \infty\) for all \(x\), \(\{\varepsilon_ n, n\geq 1\}\) is an i.i.d.
Bhattacharya, Rabi N., Lee, Chanho
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Nonlinear models for autoregressive conditional heteroskedasticity [PDF]

, 2011
This paper contains a brief survey of nonlinear models of autore- gressive conditional heteroskedasticity. The models in question are parametric nonlinear extensions of the original model by Engle (1982). After presenting the individual models, linearity testing and parameter estimation are discussed.
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A nonlinear autoregressive model for speaker verification

International Journal of Speech Technology, 2013
Gaussian Mixture Models (GMM) have been the most popular approach in speaker recognition and verification for over two decades. The inefficiencies of this model for signals such as speech are well documented and include an inability to model temporal dependencies that result from nonlinearities in the speech signal.
Sundararajan Srinivasan, Tao Ma, Georgios Lazarou, Joseph Picone   +3 more
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Nonlinear autoregressive model based on fuzzy relation

Information Sciences, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ikoma, Norikazu, Hirota, Kaoru
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Testing Common Nonlinear Features in Nonlinear Vector Autoregressive Models [PDF]

, 2012
This paper studies a special class of vector smooth-transition autoregressive (VS- TAR) models containing common nonlinear features (CNFs). To test the existence of CNFs in a VSTAR model, a triangular representation for such a system containing CNFs is proposed.
Li, Dao, He, Changli
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Pairs Trading via Nonlinear Autoregressive GARCH Models

2018
Pairs trading is a well-established speculative investment strategy in financial markets. However, the presence of extreme structural change in economy and financial markets might cause simple pairs trading signals to be wrong. To overcome this problem in detecting the buy/sell signals, we propose the use of three non-linear models consisting of Kink ...
Benchawanaree Chodchuangnirun, Kongliang Zhu, Woraphon Yamaka   +2 more
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Modeling Nonlinear Autoregressive Distributed Lag Models: A New Approach

Journal of Quantitative Economics, 2005
It is a common practice in econometrics that estimation is carried out in terms of the reduced form parameters and the structural form parameters are retrieved using the functional relationship between structural form parameters and the reduced form parameters. The reduced form of many useful economic models is a nonlinear distributed lag model (NLADL)
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