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.

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.

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Econometric models are often made up of assumptions that never truly match reality. One of the most challenged requirements is that the coefficients of econometric models remain constant over time, in the sense that it is assumed that the future will be similar to the past. If the assumption of constant coefficients is not satisfied, any conclusions reached from normal (constant coefficient) models will be biased. Another, very closely related, contested assumption is that the functional form (usually linear) of a model remains unchanged over time. The theory of linearity has long been the centre of all econometric model-building. According to Teräsvirta (1994), if linear estimates were not successful in practice, they would have been forsaken long ago, and this has certainly not been the case. Quite the opposite has been experienced: some very influential ideas based on the linear relationships between variables, like cointegration analysis, have been established. Nonetheless, there are definite situations in which linear models are unable to grasp the underlying economic theory of the data accurately. In developing economies like the South African economy, the notion of constant coefficients and the assumption of linearity are far-fetched because these economies are frequently characterized by changes in both the economic policy and the economic structure. It is thus important to see these changes in developing economies as providing valuable information for econometric modelling. Incorporating these changes into models will provide not only better forecasts, but also better information for policy analyses. This study addresses the problem of non-linearity by applying smooth transition autoregressive (STAR) specifications to an existing simultaneous macroeconomic model of the South African economy. The results support the view that non-linear models provide better forecasts than linear specifications of equations. / Dr. I. Botha

This paper aims at analyzing a macro economy with a continuum of infinitely-lived households that make rational decisions about consumption and wealth savings in the face of employment and aggregate productivity shocks. The heterogeneous population structure arises when households differ in wealth and employment status against which they cannot insure. In this framework, the household wealth evolution is modeled as a mixture Markov process. The stationary wealth distributions are obtained using eigen structures of transition matrices under the Perron-Frobenius theorem. This step is utilized repeatedly to find the equilibrium state of the system, and it leads to an efficient framework for studying the dynamic general equilibrium. A systematic evaluation of the equilibrium state under different initial conditions is further presented and analyzed. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Degree Awarded: Summer Semester, 2010. / Date of Defense: April 28, 2010. / Markov Process, The Perron-Frobenius Theorem, Wealth Distributions, Dynamic General Equilibrium Modeling / Includes bibliographical references. / Anuj Srivastava, Professor Co-Directing Dissertation; Paul Beaumont, Professor Co-Directing Dissertation; Wei Wu, Committee Member; Alec Kercheval, Outside Committee Member.

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Typescript (photocopy). / Digitized by Kansas Correctional Industries

Thesis (doctoral)--Universität, Klagenfurt, 2001. / Includes bibliographical references (p. 197-202).