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Open Access Publications from the University of California

Information and Inference in Econometrics: Estimation, Testing and Forecasting

  • Author(s): Tu, Yundong
  • Advisor(s): Lee, Tae-Hwy
  • Ullah, Aman
  • et al.
Abstract

Economic and Financial phenomena convey enormous information about the underlying structure of economic and policy interest. The first objective of the thesis is mainly concerned with how to make use of information efficiently, specifically, (1) how to separate noises from useful information in the presence of large dimensional data, (2) how to incorporate prior information (economic constraint), and (3) how to employ model structure, to conduct more informed inference, and thus to understand the economic structure wisely and draw sound policy conclusions.

The second dimension of information refers to the recent developments in the information theory that measure how much information content the observed data contains. The formalism of Maximum Entropy provides an information-theoretic approach to tackle economic problems, especially those with data observed in aggregate terms. Thus, the second objective of the thesis is to make use of this line of research and develop a new estimation method to measure quantities of economic interest when researchers are faced with model uncertainty.

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