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A spatial vector autoregressive model (SpVAR) is defined as a VAR which includes spatial as well as temporal lags among a vector of stationary state variables. SpVARs may contain disturbances that are spatially as well as temporally correlated. Although the structural parameters are not fully identified in SpVARs, contemporaneous spatial lag coefficients may be identified by weakly exogenous state variables. Dynamic spatial panel data econometrics is used to estimate SpVARs. The incidental parameter problem is handled by bias correction rather than more popular alternatives such as generalised methods of moments (GMM). The interaction between temporal and spatial stationarity is discussed. The impulse responses for SpVARs are derived, which naturally depend upon the temporal and spatial dynamics of the model. We provide an empirical illustration using annual spatial panel data for Israel. The estimated SpVAR is used to calculate impulse responses between variables, over time, and across space. Finally, weakly exogenous instrumental variables are used to identify contemporaneous spatial lag coefficients.Spatial econometrics, spatial autocorrelation, vector autoregressions, spatial panel data, C21, C22, C23, C53,

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We use statistical methods for nonstationary time series to test the anthropogenic interpretation of global warming (AGW), according to which an increase in atmospheric greenhouse gas concentrations raised global temperature in the 20th century. Specifically, the methodology of polynomial cointegration is used to test AGW since during the observation period (1880–2007) global temperature and solar irradiance are stationary in 1st differences whereas greenhouse gases and aerosol forcings are stationary in 2nd differences. We show that although these anthropogenic forcings share a common stochastic trend, this trend is empirically independent of the stochastic trend in temperature and solar irradiance. Therefore, greenhouse gas forcing, aerosols, solar irradiance and global temperature are not polynomially cointegrated. This implies that recent global warming is not statistically significantly related to anthropogenic forcing. On the other hand, we find that greenhouse gas forcing might have had a temporary effect on global temperature

This article is concerned with identifying, for the first time, the separate effects of linguistic distance (language of origin) and country of origin on the destination language proficiency of immigrants. The determinants of Hebrew language proficiency (fluency and literacy) among immigrants in Israel are studied using the 1972 Census of Israel and the Immigration Absorption (panel) Surveys conducted in the 1970s.Country of origin and language of origin matter for proficiency in Hebrew, especially in the longer term. By country of origin, those from North Africa are the least proficient. By language of origin, Arabic speakers are the most proficient, suggesting a small linguistic distance from Hebrew. Immigrants from English-speaking origins are the least proficient in Hebrew.This may reflect a large linguistic distance or, more likely, the unique role of English as the international language, which reduces incentives for investments in Hebrew. Immigrants from dual-language countries of origin are more proficient in Hebrew than those from single language origins.