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Th e quantile autoregressive distributed lag model of Galvao et al. (2013) was employed to assess the impact of price realization on India’s tea export. Th e results of the QADL varied signifi...
Cross-sectional spatial models frequently contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive. In this article we describe a computati...
The purpose of this paper is two-fold. First, on a theoretical level we introduce a series-type instrumental variable (IV) estimator of the parameters of a spatial first order autoregressive model wit...
This study develops a methodology of inference for a widely used Cliff–Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while a...
The article investigates the finite sample properties of estimators for spatial autoregressive models where the disturbance terms may follow a spatial autoregressive process. In particular we investig...
The paper considers a Cliff–Ord type spatial model with a spatially lagged dependentvariable and a row normalized weighting matrix with equal weights. We show that the 2SLSand OLS estimators are incon...
On An Efficient Two-Step Estimator for Dynamic Simultaneous Equation Models with Autoregressive Errors.
Abstract: The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin-Watson statistic. We focus our attention on the first-order autoregressive process where the...
This paper investigates international inbound tourism demand for South Korea and its determinants using quantile autoregressive model. In contrast to previous studies which dealt with only conditional...
We develop a general theory to test correct specification of multiplicative error models of non-negative time-series processes, which include the popular autoregressive conditional duration (ACD) mode...
A family of cointegrated vector autoregressive models with adjusted short-run dynamics is introduced. These models can describe evolving short-run dynamics in a more flexible way than standard vector ...
In many applications, it has been found that the autoregressive conditional heteroskedasticity (ARCH) model under the conditional normal or Student’s t distributions are not general enough to account ...
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible ...

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