<p>From left to right, the number of control variables increases in a stepwise fashion. The IVs for income are the variables that are in the income equation (listed in the corresponding columns in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001456#pbio-1001456-t004" target="_blank">Table 4</a>) but not in the disease equation here. Robust standard errors are presented in parentheses next to their corresponding coefficient estimates. First-stage <i>F</i>-test indicates strength of IVs if there is only one endogenous variable (income). If there are multiple endogenous variables (income and spatially lagged disease), Shea's Partial <i>R</i><sup>2</sup> indicates strength of IVs <a href="http://www.plosbiology.org...
Classical regression model literature has generally assumed that measured and unmeasured covariates...
Health diagnosis indicators used as explanatory variables in econometric models often suffer from su...
Results of generalized linear regression model analyses for factors significantly associated with th...
<p>From left to right, the number of control variables, which are listed on the left, increases in a...
<p>Columns 2 and 4 represent parameter estimates for the income and disease equations, which corresp...
<p>The dependent variable is the Ln(GDP) and all variables are in Logarithm form.</p><p>Figures in p...
<p>Parameter estimates for second-stage regressions that include biodiversity (columns a and b) and ...
<p>Coefficient estimates from linear regression models are shown with standard errors in parentheses...
<p>Significant F statistic (p<0,001) for both regression models.</p><p><b>Model 1:</b> independent v...
<p>Note. Par = parameters, Estim = estimate (log for variance), SD = standard deviation estima...
In longitudinal studies, the generalized estimating equation (GEE) estimator of the parameters of a ...
Abstract: Sammel and Ryan (1996) developed a latent variable model that allows for covariate effects...
We construct a novel statistic to test hypothezes on subsets of the structural parameters in anInstr...
The “difference ” and “system ” generalized method of moments (GMM) estimators for dynamic panel mod...
Regression analysis results showing comparison of annual per-capita total costs between patients wit...
Classical regression model literature has generally assumed that measured and unmeasured covariates...
Health diagnosis indicators used as explanatory variables in econometric models often suffer from su...
Results of generalized linear regression model analyses for factors significantly associated with th...
<p>From left to right, the number of control variables, which are listed on the left, increases in a...
<p>Columns 2 and 4 represent parameter estimates for the income and disease equations, which corresp...
<p>The dependent variable is the Ln(GDP) and all variables are in Logarithm form.</p><p>Figures in p...
<p>Parameter estimates for second-stage regressions that include biodiversity (columns a and b) and ...
<p>Coefficient estimates from linear regression models are shown with standard errors in parentheses...
<p>Significant F statistic (p<0,001) for both regression models.</p><p><b>Model 1:</b> independent v...
<p>Note. Par = parameters, Estim = estimate (log for variance), SD = standard deviation estima...
In longitudinal studies, the generalized estimating equation (GEE) estimator of the parameters of a ...
Abstract: Sammel and Ryan (1996) developed a latent variable model that allows for covariate effects...
We construct a novel statistic to test hypothezes on subsets of the structural parameters in anInstr...
The “difference ” and “system ” generalized method of moments (GMM) estimators for dynamic panel mod...
Regression analysis results showing comparison of annual per-capita total costs between patients wit...
Classical regression model literature has generally assumed that measured and unmeasured covariates...
Health diagnosis indicators used as explanatory variables in econometric models often suffer from su...
Results of generalized linear regression model analyses for factors significantly associated with th...