Capturing the Shape of Business Cycles with Nonlinear Autoregressive Leading Indicator Models
AbstractThis paper studies linear and linear autoregressive leading indicator models of business cycles in OECD countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles gauged by the non-parametric procedures developed by Harding and Pagan (2001). Our preliminary findings indicate that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle. In particular, they can capture the features of recession and the deviation of the actual path of the cycles from a triangular approximation to this path, both characteristics that other models of GDP fail to reproduce.
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Bibliographic InfoPaper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 7/01.
Length: 36 pages
Date of creation: Jun 2001
Date of revision:
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Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Web page: http://www.buseco.monash.edu.au/depts/ebs/
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Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-04-25 (All new papers)
- NEP-ECM-2002-04-25 (Econometrics)
- NEP-ETS-2002-04-25 (Econometric Time Series)
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- Harding, Don & Pagan, Adrian, 2001. "Extracting, Using and Analysing Cyclical Information," MPRA Paper 15, University Library of Munich, Germany.
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