Naghi, Andrea (2016) Three essays in econometrics. PhD thesis, University of Warwick.

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Abstract

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.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	H Social Sciences > HB Economic Theory
Library of Congress Subject Headings (LCSH):	Econometrics, Economic forecasting -- Econometric models, Macroeconomics -- Econometric models
Official Date:	September 2016
Dates:	Date Event September 2016 Submitted
Institution:	University of Warwick
Theses Department:	Department of Economics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Corradi, Valentina ; Galvão, Ana Beatriz C. (Ana Beatriz Camatari) ; Komunjer, Ivana ; Smith, Jeremy (Jeremy P.)
Sponsors:	Economic and Social Research Council (Great Britain) ; Royal Economic Society (Great Britain)
Format of File:	pdf
Extent:	xi, 168 leaves : illustrations, charts
Language:	eng

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