This thesis consists of three self-contained essays that contribute to the literature on macroeconomicforecasting and empirical macroeconomics. The first essay establishes the importance ofgood starting conditions (i.e., nowcasts) and terminal conditions (i.e., steady-states or "stars")in obtaining accurate forecasts from vector autoregressive (VAR) models estimated with quarterlydata. It does so by proposing the technique of relative entropy to tilt the VAR forecastboth in the near term with the survey nowcast and in the long run with the survey long-runprojection. Doing so leads to meaningful gains in multi-horizon forecast accuracy. The gainsin accuracy are made possible because our proposal is an indirect approach to accommodatingstructural change and moving end points.The second essay develops a framework based on the model and density combinations thatgenerate highly accurate point and density nowcasts of inflation at a daily frequency. We adopta novel flexible treatment in the use of the aggregation function to combine density estimatesfrom a range of mixed-frequency models. The framework permits dynamic model averaging viaweights that are updated based on learning from past performance. Together these featuresallow non-Gaussian densities. The accuracy of the density and implied point nowcasts are significantly more accurate than the nowcasts from the survey of professional forecasters.The third essay develops a large-scale unobserved components model to estimate a rangeof macroeconomic stars (i.e., terminal points). The model is motivated by economic theoryand empirical features such as time-varying parameters and stochastic volatility. The modelallows for a direct link between the model-based star and long-run survey expectations, whichsignificantly improves the precision of the model-based estimates of stars. The by-products arethe time-varying estimates of the wage and price Phillips curves, passthrough between pricesand wages, which provide new insights into these empirical relationships' instability in the USdata.
Date of Award | 1 Oct 2021 |
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Original language | English |
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Awarding Institution | - University Of Strathclyde
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Supervisor | Gary Koop (Supervisor) & Julia Darby (Supervisor) |
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