English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Using a state-space system, I forecasted the US Treasury yields by employingfrequentist and Bayesian methods after first decomposing the yields of varyingmaturities into its unobserved term structure factors. Then, I exploited thestructure of the state-space model to forecast the Treasury yields and comparedthe forecast performance of each model using mean squared forecast error. Amongthe frequentist methods, I applied the two-step Diebold-Li, two-step principalcomponents, and one-step Kalman filter approaches. Likewise, I imposed the fivedifferent priors in Bayesian VARs: Diffuse, Minnesota, natural conjugate, theindependent normal inverse: Wishart, and the stochastic search variableselection priors. After forecasting the Treasury yields for 9 differentforecast horizons, I found that the BVAR with Minnesota prior generallyminimizes the loss function. I augmented the above BVARs by includingmacroeconomic variables and constructed impulse response functions with arecursive ordering identification scheme. Finally, I fitted a sign-restrictedBVAR with dummy observations.

Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs