Skip to main content

Analysis and Prediction of the UK economy

Warren, James (2016) Analysis and Prediction of the UK economy. Doctor of Philosophy (PhD) thesis, University of Kent, University of Kent. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:58879)

PDF
Language: English

Restricted to Repository staff only until November 2021.
Contact us about this Publication
[img]

Abstract

Using the business cycle accounting (BCA) framework pioneered by Chari, Kehoe and McGratten (2007, Econometrica) we examine the causes of the 2008-09 recession in the UK. There has been much commentary on the finnancial causes of this recession, which we might expect to bring about variation in the intertemporal rate of substitution in consumption. However, the recession appears to have been mostly driven by shocks to the efficiency wedge in total production, rather than the intertemporal (asset price) consumption, labour or spending wedge. From an expenditure perspective this result is consistent with the observed large falls in both consumption and investment during the

only the deeper of the recessions were captured. Further work on dealing with the label switching problem may be required for better performance for the Bayesian treatment of MFVARs with regime switches.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Chadha, Jagjit
Uncontrolled keywords: Business Cycle Accounting, Major Recessions, TFP, Financial Frictions Forecasting, mixed frequency data, bridge equations, MIDAS, MFVAR, factor models, Markov switching
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HC Economic History and Conditions
Divisions: Faculties > Social Sciences > School of Economics
Depositing User: Users 1 not found.
Date Deposited: 22 Nov 2016 18:00 UTC
Last Modified: 29 May 2019 18:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58879 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year