Publication date
April 2017
DOIAbstract
This postestimation technique produces dynamic simulations of autoregressive ordinary least-squares models
This postestimation technique produces dynamic simulations of autoregressive ordinary least-squares models
SIGLECNRS 17660 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
AbstractA general procedure for modeling stochastic, nonlinear, dynamic process from time series dat...
We develop numerically stable stochastic simulation approaches for solving dynamic economic models. ...
This postestimation technique produces dynamic simulations of autoregressive ordinary least-squares ...
<p>This paper is a short introduction in how to use the dynsim R package for calculating dynamic sim...
<p>dynsim implements Williams and Whitten's (2012) method for dynamic simulations of autoregressive ...
The application of dynamic Autoregressive Distributed Lag (dynardl) simulations and Kernel-based Reg...
Abstract We develop a model for representing stationary time series with arbitrary marginal distribu...
AbstractThis paper presents a two-stage least squares based iterative algorithm, a residual based in...
This paper is concerned with the detection of natural periods and damping factors from uniformlysamp...
Time-series input processes occur naturally in the stochastic simulation of many service, communicat...
The autoregressive model is a tool used in time series analysis to describe and model time series da...
We present a model for representing stationary multivariate time-series input processes with margina...
We introduce a new class of nonlinear autoregressive models from their representation as linear auto...
Multiregression dynamic models are defined to preserve certain conditional independence structures o...
SIGLECNRS 17660 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
AbstractA general procedure for modeling stochastic, nonlinear, dynamic process from time series dat...
We develop numerically stable stochastic simulation approaches for solving dynamic economic models. ...
This postestimation technique produces dynamic simulations of autoregressive ordinary least-squares ...
<p>This paper is a short introduction in how to use the dynsim R package for calculating dynamic sim...
<p>dynsim implements Williams and Whitten's (2012) method for dynamic simulations of autoregressive ...
The application of dynamic Autoregressive Distributed Lag (dynardl) simulations and Kernel-based Reg...
Abstract We develop a model for representing stationary time series with arbitrary marginal distribu...
AbstractThis paper presents a two-stage least squares based iterative algorithm, a residual based in...
This paper is concerned with the detection of natural periods and damping factors from uniformlysamp...
Time-series input processes occur naturally in the stochastic simulation of many service, communicat...
The autoregressive model is a tool used in time series analysis to describe and model time series da...
We present a model for representing stationary multivariate time-series input processes with margina...
We introduce a new class of nonlinear autoregressive models from their representation as linear auto...
Multiregression dynamic models are defined to preserve certain conditional independence structures o...
SIGLECNRS 17660 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
AbstractA general procedure for modeling stochastic, nonlinear, dynamic process from time series dat...
We develop numerically stable stochastic simulation approaches for solving dynamic economic models. ...
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