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least squares (OLS), fixed effects panel and first-differences general method of moment (GMM) model tests were executed in EViews on a dataset for 25 EU countries covering 1996–2017. The research question was as follows: Do the various appropriations (i
negative impact in OLS and fixed effects econometric models. With American data, this significant negative impact has been confirmed by other authors as well, including Vafeas (1999) with Tobin's Q in OLS models, Cheng et al. (2008) with Tobin's Q and
Can socioeconomic fluctuations explain variations in European Union suicide mortality? To answer this question, we investigate the effect of socioeconomic and demographic factors on (agespecific) male and female suicide rates using a fixed-effects technique and panel data for 15 EU countries, over a time period that leads up to, coincides with, and follows the recession of 2008. The findings show that suicide rates for young and working-age populations are more sensitive to general economic conditions than suicide rates for other age groups, and that male suicide behavior is more responsive than female behavior. In this setting, suicide rates are likely to be higher in countries with lower income, higher unemployment, higher divorce rates, and, most importantly, weaker systems of social protection. Our results, however, raise serious doubts about government involvement in crisis-related mental illness prevention and mental health promotion.
single studies. Weights can be assigned based on the assumption of fixed and random effects. In short, the difference of these approaches is whether there is meaningful methodological heterogeneity in the studies. A fixed-effects model assigns great
. First, for using panel data, the model to be used needs to be determined. The panel fixed effects or random effects model is used to estimate the model. The difference between the two models is whether the error term representing the unobservable
variables in order to account for country-fixed effects ( Windmeijer 2005 ). We first estimate the basic growth equation using the fixed effect estimator, see Column (1) in Table 3 . The standard assumption regarding the link between the GDP per capita
defining sample sizes were avoided, due to the existence of complex and interactive relationships between factors that affect power ( Mathieu, Aguinis, Culpepper, & Chen, 2012 ). Considering fixed effects and cross-level interactions, studies using random
, Mächler, Bolker, & Walker, 2015 ) in order to account for the repeated measures. The LMEs included a random intercept for each participant in addition to the fixed effects, but no random slopes for participants. Distribution
. Next, predictors at the individual level (substance consumption) and at the macro-level (GDP) were included as fixed effects with random intercepts in order to estimate the direct associations between the predictors and the outcome measure without
1995–2016 for 27 EU countries. 20 The literature is ambivalent with respect to using country fixed effects in the panel estimation, as the choice between adding or omitting fixed effects can be characterized by a trade-off. On the one hand, by applying
