Browsing by Subject "Predictability"
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Item Essays on monetary economics and financial economics(2009-06-02) Kim, Sok WonIn this dissertation three different economic issues have been analyzed. The first issue is whether monetary policy rules can improve forecasting accuracy of inflation. The second is whether the preference of a central bank is symmetry or not. The last issue is whether the behavior of aggregate dividends is asymmetry. Each issue is considered in Chapter II, III and IV, respectively. The linkage between monetary policy rules and the prediction of inflation is explored in Chapter II. Our analysis finds that the prediction performance of the term structure model hinges on monetary policy rules, which involve the manipulation of the federal funds rate in response to the change in the price level. As the Fed's reaction to inflation becomes stronger, the predictive information contained in the term structure becomes weaker. Using the long-run Taylor rule, a new assessment of the prediction performance regarding future change in inflation is provided. The empirical results indicate that the long-run Taylor rule improves forecasting accuracy. In chapter III, the asymmetric preferences of the central bank of Korea are examined under New Keynesian sticky prices forward-looking economy framework. To this end, this chapter adopts the central bank's objective functional form as a linear-exponential function instead of the standard quadratic function. The monetary policy reaction function is derived and then asymmetric preference parameters are estimated during the inflation targeting period: 1998:9-2005:12. The empirical evidence supports that while the objective of output stability is symmetry, but the objective of price stability is not symmetry. Specifically, it appears that the central bank of Korea aggressively responds to positive inflation gaps compared to negative inflation gaps. Chapter IV examines the nonlinear dividend behavior of the aggregate stock market. We propose a nonlinear dividend model that assumes managers minimize the regime dependent adjustment costs associated with being away from their target dividend payout. By using the threshold vector error correction model, we find significant evidence of a threshold effect in aggregate dividends of S&P 500 Index in quarterly data when real stock prices are used for the target. We also find that when dividends are relatively higher than target, the adjustment cost of dividends is much smaller than that when they are lower.Item Linear Diagnostics to Assess the Performance of an Ensemble Forecast System(2011-10-21) Satterfield, Elizabeth A.The performance of an ensemble prediction system is inherently flow dependent. This dissertation investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model grid point location ?. A local error covariance matrix P? is defined for each local region and the diagnostics are applied to the linear space S? defined by the range of the ensemble based estimate of P?. The particular diagnostics are chosen to help investigate the ability of S? to efficiently capture the space of true forecast or analysis uncertainties, accurately predict the magnitude of forecast or analysis uncertainties, and to distinguish between the importance of different state space directions. Additionally, we aim to better understand the roots of the underestimation of the magnitude of uncertainty by the ensemble at longer forecast lead times. Numerical experiments are carried out with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on a reduced (T62L28) resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere are used in these experiments. It is found that (i) paradoxically, the linear space S? provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time, (ii) S? provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth, (iii) the E-dimension is a reliable predictor of the performance of S? in predicting the space of forecast uncertainties, (iv) the ensemble grossly underestimates the forecast error variance in S?, (v) when realistic observation coverage is used, the ensemble typically overestimates the uncertainty in the leading eigen-directions of ?P ? and underestimates the uncertainty in the trailing directions at analysis time and underestimates the uncertainty in all directions by the 120-hr forecast lead time, and (vi) at analysis time, with a constant covariance inflation factor, the ensemble typically underestimates uncertainty in densely observed regions and overestimates the uncertainty in sparsely observed regions.Item Toward understanding predictability of climate: a linear stochastic modeling approach(Texas A&M University, 2004-11-15) Wang, FamingThis dissertation discusses the predictability of the atmosphere-ocean climate system on interannual and decadal timescales. We investigate the extent to which the atmospheric internal variability (weather noise) can cause climate prediction to lose skill; and we also look for the oceanic processes that contribute to the climate predictability via interaction with the atmosphere. First, we develop a framework for assessing the predictability of a linear stochastic system. Based on the information of deterministic dynamics and noise forcing, various predictability measures are defined and new predictability-analysis tools are introduced. For the sake of computational efficiency, we also discuss the formulation of a low-order model within the context of four reduction methods: modal, EOF, most predictable pattern, and balanced truncation. Subsequently, predictabilities of two specific physical systems are investigated within such framework. The first is a mixed layer model of SST with focus on the effect of oceanic advection.Analytical solution of a one-dimensional model shows that even though advection can give rise to a pair of low-frequency normal modes, no enhancement in the predictability is found in terms of domain averaged error variance. However, a Predictable Component Analysis (PrCA) shows that advection can play a role in redistributing the predictable variance. This analytical result is further tested in a more realistic two-dimensional North Atlantic model with observed mean currents. The second is a linear coupled model of tropical Atlantic atmosphere-ocean system. Eigen-analysis reveals that the system has two types of coupled modes: a decadal meridional mode and an interannual equatorial mode. The meridional mode, which manifests itself as a dipole pattern in SST, is controlled by thermodynamic feedback between wind, latent heat flux, and SST, and modified by ocean heat transport. The equatorial mode, which manifests itself as an SST anomaly in the eastern equatorial basin, is dominated by dynamic feedback between wind, thermocline, upwelling, and SST. The relative strength of thermodynamic vs dynamic feedbacks determines the behavior of the coupled system, and enables the tropical Atlantic variability to be more predictable than the passive-ocean scenario.