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Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models
Model Selection Model Averaging Spatial Econometrics Spatial Autoregressive
2016/1/26
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models
Model Selection Model Averaging Spatial Econometrics Spatial Autoregressive
2016/1/20
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables
spivreg spatial-autoregressive models
2015/9/24
We describe the spivreg command, which estimates the parameters
of linear cross-sectional spatial-autoregressive models with spatial-autoregressive
disturbances, where the model may also contain add...
On non-stationary threshold autoregressive models
explosive TAR(1) model least-squares estimator unit root TAR(1) model
2011/7/19
In this paper we study the limiting distributions of the least-squares estimators for the non-stationary first-order threshold autoregressive (TAR(1)) model. It is proved that the limiting behaviors o...
Hidden Markov Mixture Autoregressive Models: Parameter Estimation
Hidden Markov Model Mixture Autoregressive Model Parameter Estimation
2011/6/17
This report introduces a parsimonious structure for mixture of au-
toregressive models, where the weighting coefficients are determined
through latent random variables as functions of all past obser...
Hidden Markov Mixture Autoregressive Models: Stability and Moments
Hidden Markov Model Mixture Autoregressive Model Stability Dynamic Programming Forecasting
2011/6/16
This paper introduces a new parsimonious structure for mixture
of autoregressive models. The weighting coefficients are determined
through latent random variables, following a hidden Markov model.
...
Estimation in nonstationary random coefficient autoregressive models
random coefficient model quasi–maximum likelihood asymptoticnormality consistency law of large numbers
2010/3/18
We investigate the estimation of parameters in the random coefficient autoregressive
model Xk = ('+bk)Xk−1 +ek, where (', !2, 2) is the parameter of the process, Eb20=
!2, Ee20= 2. We consid...
Efficient prediction for linear and nonlinear autoregressive models
Empirical likelihood Owen estimator weighted density estimator kernel smoothed empirical process functional central limit theorem
2010/4/27
Conditional expectations given past observations in stationary
time series are usually estimated directly by kernel estimators, or by
plugging in kernel estimators for transition densities. We show ...