Date of this Version
Economists often have to use temporally aggregated data in causality tests. A number of theoretical studies have pointed out that temporal aggregation has distorting effects on causal inference. This paper examines the issue in detail by plugging in theoretical cross covariances into the limiting values of least squares estimates. An extensive Monte Carlo study is conducted to examine small sample results. An empirical example is also provided. It is observed that in general the most distorting causal inferences are likely at low levels of aggregation where the order of aggregation just exceeds the actual causal lag. At high levels of aggregation, causal information concentrates in contemporaneous correlations. At present, a data-based approach is not available to establish the direction of causality between contemporaneously correlated variables.