This dissertation collects three independent essays in the area of Macroeconomics and Macroeconomic forecasting. The first chapter introduces and motivates the three essays. Chapter 2 highlights a serious problem of the Bayesian vector autoregressive (BVAR) models with Litterman???s prior cannot be used to get accurate forecasts of the driftless variables in a mixed drift models. BVAR models with Litterman???s prior, because of the diffuse prior on the constant, do not perform well in the long-run forecasting of I(1) variables either, if they have no drift. This is interesting as in practice most of the macro models include both drift and driftless variables. One solution to this problem is using the Bewley (1979) transformation to impose zero drift to driftless variables in a mixed drift VAR models. A novel feature of this chapter is the use of g-prior in BVAR models to alleviate poor estimation of drift parameters of the Traditional BVAR model. Chapter 3 deals with another possible explanation for the poor performance of the Traditional BVAR models in inflation forecasting. BVAR with Litterman???s prior have the disadvantage of a lack of robustness to deterministic shifts, exacerbated by the ill-determination of the intercept. Several structural break tests show that Australian inflation has breaks in the mean. Chapter 3 uses the Kalman filter to allow parameters to vary over time. The novelty of this chapter is modifying the standard BVAR model, where deterministic components evolve over time. Moreover, this chapter set aside the assumption of diagonality in the prior variance-covariance. Hence, another novelty of this chapter is using a BVAR model with modified non-diagonal variance-covariance matrix similar to the g-prior, where the deterministic components are the only source of variation, to forecast Australian inflation. Chapter 4 moves onto DSGE models and estimates a partially microfunded small-open economy (SOE) New-Keynesian model of the Australian economy. In this chapter, structural parameters of the rest of world (ROW), SOE, and closed economy, are estimated using Australian data as the small economy, and the US as the ROW, with the full information maximum likelihood.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed on Mar. 13, 2007). PDF text: vi, 124 p. : col. ill. UMI publication number: AAT 3225346. Includes bibliographical references. Also available in microfilm and microfiche format.

Thesis (M.S.)--University of Delaware, 2007. / Principal faculty advisor: David R. Legates, Dept. of Geography. Includes bibliographical references.

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 87-88). Also available on the World Wide Web.

Thesis (M.S.)--Oregon State University, 1982. / Map folded in pocket. Typescript (photocopy). Includes bibliographical references (leaves 61-65). Also available on the World Wide Web.

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 ℓ. A local error covariance matrix Pℓ is defined for each local region and the diagnostics are applied to the linear space Sℓ defined by the range of the ensemble based estimate of Pℓ. The particular diagnostics are chosen to help investigate the ability of Sℓ 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 Sℓ 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) Sℓ 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 Sℓ in predicting the space of forecast uncertainties, (iv) the ensemble grossly underestimates the forecast error variance in Sℓ, (v) when realistic observation coverage is used, the ensemble typically overestimates the uncertainty in the leading eigen-directions of ˆP ℓ 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.

Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models are applied to historical data for two years: 2001 and 2002. The approaches are (1) to analyze nonlinear regression models for long term forecasting of annual patterns compared with outdoor temperature, and (2) to analyze multiple regression models for the building type regardless of outdoor temperature. In the first approach, twenty four buildings are categorized as base load group and no base group. Average temperature, cooling efficiencies, and cooling knot temperature are estimated by nonlinear regression models: segment and parabola models. The adjusted r-square results in good performance up to ninety percent accuracy. In the second approach, the other selected six buildings are categorized as no trend group. This group does not respond to outdoor temperature. As the result, multiple a regression model is formed by combination of variables from the nonlinear models and physical building variables of cooling efficiency, cooling temperature, light bulbs, area, outdoor temperature, and orientation of fronts. This model explains up to sixty percent of all convenience stores' data. In conclusion, the accuracy of prediction models is measured by the adjusted r-square results. Among these three models, the multiple regression model shows the highest adjusted r-square (0.597) over the parabola (0.5419) and segment models (0.4806). When the three models come to the application, the multiple regression model is best fit for no trend data type. However, when it is used to predict the energy consumption with the buildings that relate to outdoor temperature, segment and parabola model provide a better prediction result.

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Economics." Includes bibliographical references (p. 115-122).

Thesis (PhD(Economics))--University of Pretoria, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--Florida State University, 2004. / Advisor: Dr. Xiaolei Zou, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (June 18, 2004). Includes bibliographical references.