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The cointegrated VAR approach combines differences of variables with cointegration among them and by doing so allows the user to study both long-run and short-run effects in the same model. The CVAR describes an economic system where variables have been pushed away from long-run equilibria by exogenous shocks (the pushing forces) and where short-run adjustments forces pull them back toward long-run equilibria (the pulling forces). In this model framework, basic assumptions underlying a theory model can be translated into testable hypotheses on the order of integration and cointegration of key variables and their relationships. The set of hypotheses describes the empirical regularities we would expect to see in the data if the long-run properties of a theory model are empirically relevant.

I analyze the effect of inequality on growth over different time-frames. In a large cross-country dataset for the period 1950-2007 I find evidence of a short-run (5-year periods) and medium-run (10-year periods) inverse-U relationship between inequality and growth. In the long-run (20-year periods), however, inequality has a negative effect on growth in poor countries, and a positive effect in rich ones. Finally, in the 37-year period (1970-2007) higher inequality is associated with a lower rate of growth. Thus, while some (but not much) inequality is good for growth, the negative effect of inequality on growth becomes dominant over longer time-frames.

I analyze the effect of inequality on growth over different time-frames. In a large cross-country dataset for the period 1950-2007 I find evidence of a short-run (5-year periods) and medium-run (10-year periods) inverse-U relationship between inequality and growth. In the long-run (20-year periods), however, inequality has a negative effect on growth in poor countries, and a positive effect in rich ones. Finally, in the 37-year period (1970-2007) higher inequality is associated with a lower rate of growth. Thus, while some (but not much) inequality is good for growth, the negative effect of inequality on growth becomes dominant over longer time-frames.

The authors investigate the impact of direct-response commercials on incoming calls at a national call center. To this end, the authors analyze the data of a fast service for repairs of (parts of) a durable consumption good in Flanders, Belgium. The authors have access to data at the 15 minute interval covering 30 months in which 5172 radio commercials were broadcasted on six radio stations at various times of the day and at with differing commercial lengths. Their model is a two-level model, where the first-level estimates of the short-run and long-run effects are correlated with various aspects of the commercial is the second level. Their main conclusion is that GRPs are the key drivers of the effectiveness of commercials.