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In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step...
Persistent link: https://www.econbiz.de/10010264508
Categorical and limited dependent variable models are routinely estimated via maximum likelihood. It is well-known that the ML estimates of the parameters are inconsistent if the distribution or the skedastic component is misspecified. When conditional moment tests were first developed by Newey...
Persistent link: https://www.econbiz.de/10009442280
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This session serves as an introduction to Stata 12’s new sem command for estimating the parameters of simultaneous-equations models. Some of the considered models include unobserved factors. Estimation methods include maximum likelihood and generalized method of moments.
Persistent link: https://www.econbiz.de/10010897904
<title>Abstract</title> One of the most widely used tests for spatial dependence is Moran's (1950) I test. The power of the test will depend on the extent to which the spatial-weights matrix employed in computing the Moran I test statistic properly specifies existing interaction links between spatial units....
Persistent link: https://www.econbiz.de/10010974006
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This paper discusses the advantages of Halton sequences over pseudo- random uniform numbers when using simulation to approximate integrals numeri- cally. We describe two types of sequences and give Mata examples. Finally, we doc- ument the Mata function halton(), currently in release 9.1 of...
Persistent link: https://www.econbiz.de/10004964299
This article gives a brief overview of the popular methods for esti- mating variance components in linear models and describes several ways to obtain such estimates in Stata for various experimental designs. The article’s emphasis is on using xtmixed to estimate variance components. Prior to...
Persistent link: https://www.econbiz.de/10004964300
Stata 11 has new command gmm for estimating parameters by the generalized method of moments (GMM). gmm can estimate the parameters of linear and nonlinear models for cross-sectional, panel, and time-series data. In this presentation, I provide an introduction to GMM and to the gmm command.
Persistent link: https://www.econbiz.de/10005009799
Stata 11 has new commands sspace and dvech for estimating the parameters of space-space models and diagonal-vech multivariate GARCH models, respectively. In this presentation, I provide an introduction to space-space models, diagonal-vech multivariate GARCH models, the implemented estimators,...
Persistent link: https://www.econbiz.de/10005009815