Analyzing Three-Dimensional Panel Data of Forecasts

摘要: With the proliferation of quality multi-dimensional surveys, it becomes increasingly important for researchers to employ an econometric framework in which these data can be properly analyzed and put their maximum use. In this chapter we have summarized such a developed Davies Lahiri (1995, 1999), illustrated some uses panel data. particular, characterized adaptive expectations mechanism context broader rational implicit hypotheses, suggested ways testing one hypothesis over others. We find that, under model, forecaster who fully adapts new information is equivalent whose forecast bias increases linearly with horizon. A also provides means distinguish between anticipated unanticipated changes target as well volatilities associated changes. show that proper identification perceived are critical correct understanding estimation uncertainty. absence rich data, typically used variance errors proxies shocks. It volatility change not (subsequently-observed) variable or should condition This because uncertainty formed when made, hence anything was unknown made factor determining finding has implications on how estimate real time construct measure average historical uncertainty, cf. Sheng (2010a). Finally, Rational Expectations tested by constructing appropriate variance-covariance matrix specific type multidimensional available.