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GMM estimation of the autoregressiveparameter in a spatial autoregressive errormodel using regression residuals

Arnold, Matthias - 2007

This paper suggests an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are di.erent from observable regression residuals. Although this di.erence decreases in large samples, it is...

Persistent link: https://www.econbiz.de/10010298206

GM estimation of higher order spatial autoregressive processes in panel data error component models

Badinger, Harald; Egger, Peter - 2008

This paper presents a generalized moments (GM) approach to estimating an R-th order spatial regressive process in a panel data error component model. We derive moment conditions to estimate the parameters of the higher order spatial regressive process and the optimal weighting matrix required to...

Persistent link: https://www.econbiz.de/10010264361

GM estimation of higher-order spatial autoregressive processes in cross-section models with heteroskedastic disturbances

Badinger, Harald; Egger, Peter - 2008

This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial...

Persistent link: https://www.econbiz.de/10010264403

Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances

Kelejian, Harry H.; Prucha, Ingmar R. - 2008

One important goal of this study is to develop 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 allowing for unknown heteroskedasticity in the innovations. We first...

Persistent link: https://www.econbiz.de/10010264476

A spatial Cliff-ord-type model with heteroskedastic innovations: small and large sample results

Arraiz, Irani; Drukker, David M.; Kelejian, Harry H.; … - 2008

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

Estimation of higher-order spatial autoregressive panel data error component models

Badinger, Harald; Egger, Peter - 2009

This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM)...

Persistent link: https://www.econbiz.de/10010264566

Joining panel data with cross-sections for efficiency gains: an application to a consumption equation for Nicaragua

Bruno, Randolph Luca; Stampini, Marco - 2007

This paper explores how cross-sectional data can be exploited jointly with longitudinal data, in order to increase estimation efficiency while properly tackling the potential bias due to unobserved individual characteristics. We propose an innovative procedure and we show its implementation by...

Persistent link: https://www.econbiz.de/10010271885

Why Are Asset Returns Predictable?

Lüders, Erik - 2002

Starting from an information process governed by a geometric Brownian motion we show that asset returns are predictable if the elasticity of the pricing kernel is not constant. Declining [Increasing] elasticity of the pricing kernel leads to mean reversion and negatively autocorrelated asset...

Persistent link: https://www.econbiz.de/10010297953

Finite sample of the Durbin-Watson test against fractionally integrated disturbances

Kleiber, Christian; Krämer, Walter - 2004

We consider the finite sample power of various tests against serial correlation in the disturbances of a linear regression when these disturbances follow a stationary long memory process. It emerges that the power depends on the form of the regressor matrix and that, for the Durbin-Watson test...

Persistent link: https://www.econbiz.de/10010306236

Can we make money with fifth-order autocorrelation in Japanese stock prices?

Tsutsui, Yoshiro; Hirayama, Kenjiro; Tanaka, Takahiro; … - 2005

We first report that one-minute returns on TOPIX have exhibited significant autocorrelation at five-minute intervals … jump in excess of a predetermined band seem to be the source of this autocorrelation, since these have been updated at five …-minute intervals since August 1998. Individual stock returns also exhibit fifth-order autocorrelation, but this disappears when the …

Persistent link: https://www.econbiz.de/10010332467

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