Chapter One, A Forecast Rationality Test that Allows for Loss Function Asymmetries, proposes a new forecast rationality test that allows for asymmetric preferences without assuming any particular functional form for the forecaster's loss function. The construction of the test is based on the simple idea that under rationality, asymmetric preferences imply that the conditional bias of the forecast error is zero. The null hypothesis of forecast rationality under asymmetric loss (i.e. no conditional bias) is tested by constructing a Bierens [1982, 1990] conditional moment type test. Chapter Two, Global Identification Failure in DSGE Models and its Impact on Forecasting, considers the identification problem in DSGE models and its transfer to other objects of interest such as point forecasts. The results document that when observationally equivalent parameter points belong to the same model structure, the implied point forecasts are the same under both correct specification and misspecification. However, when analyzing the identification problem permitting models with different structures (e.g. different policy rules that produce sets of data dynamics that are quantitatively similar), the paper shows that indistinguishable parameter estimates can lead to distinct predictions. Chapter Three, Identification Robust Predictive Ability Testing, considers the predictive accuracy evaluation of models that are strongly identified in some part of the parameter space but non-identified or weakly identified in another part of the parameter space. The paper shows that when comparing the predictive ability of models that might be affected by identification deficiencies, the null distribution of out-of-sample predictive ability tests is not well approximated by the standard normal distribution. As a result, employing a standard (strong) identification critical value might lead to misleading inference. We propose methods to make the out-of-sample predictive ability tests robust to identification loss.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:725274 |
| Date | January 2016 |
| Creators | Naghi, Andrea |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/93377/ |
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