Results 131 to 140 of about 7,202 (166)
Vector Autoregressions and Causality [PDF]
Econometrica, 1993 The asymptotic properties of Granger-type causality tests in unrestricted and nonstationary vector autoregression models are discussed. A sufficiency condition involving a rank condition on the submatrix of the cointegrating matrix is developed under which the above causality tests are shown to be valid asymptotically as chi-square criteria.
Toda, Hiro Y, Phillips, Peter C B
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On Periodic and Multiple Autoregressions
Annals of Statistics, 1978 A methodology is presented for analyzing periodic autoregressions which is also applicable when inferring the second order properties of periodically correlated processes. In addition, capitalizing on the connection between periodic and multiple autoregressions, a method is set forth for analyzing the latter, which overcomes the usual requirements of a
Marcello Pagano
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Averaged Autoregression Quantiles in Autoregressive Model
2020This paper considers the averaged autoregression quantile in autoregressive models. Our primary interest is its structure, qualities, and its applications. Moreover, under the local heteroscedasticity we investigate the properties of averaged autoregression quantile. For an illustration, a simulation study is provided.
Yeşim Güney +2 more
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Geographical Analysis, 2009
The paper by Cliff and Ord (1969) caused us to think more about the W matrix and the concept of spatial autoregression. This article reviews some applications of W, reinforcing the point that autoregression in time series is similar to, but not the same as, autoregression in spatial series.
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The paper by Cliff and Ord (1969) caused us to think more about the W matrix and the concept of spatial autoregression. This article reviews some applications of W, reinforcing the point that autoregression in time series is similar to, but not the same as, autoregression in spatial series.
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Regression, Autoregression Models
Journal of Time Series Analysis, 1986Abstract.The accuracy of least squares fitted regression autoregression models as approximations to more general stochastic structures is considered, attention being paid to the accuracy of the estimates of coefficients, of the innovations sequence and to the behaviour of the order (i.e., maximum lag) as determined by methods such as IAC, BIC.
L Kavalieris, E J Hannan
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Structural Vector Autoregressions
2008Structural vector autoregressions (SVARs) are a multivariate, linear representation of a vector of observables on its own lags and (possibly) other variables as a trend or a constant. SVARs make explicit identifying assumptions to isolate estimates of policy and/or private agents’ behaviour and its effects on the economy while keeping the model free of
Jesús Fernández-Villaverde +1 more
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Autoregressive gamma processes
Journal of Forecasting, 2005We introduce a class of autoregressive gamma processes with conditional distributions from the family of noncentered gamma (up to a scale factor). The paper provides the stationarity and ergodicity conditions for ARG processes of any autoregressive order p, including long memory, and closed-form expressions of conditional moments.
Christian Gourieroux, Joann Jasiak
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On the theory for autoregressive processes
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992A theoretical framework for autoregressive estimation is presented. Three levels of approximation are distinguished: probability limits, asymptotic theory and finite sample theory. At each level, formulae are given for the variance of estimated parameters, for the residual variance which is minimized and for the prediction error which is a measure for ...
Piet M. T. Broersen, H. Einar Wensink
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