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Nonlinear Autoregresssive Leading Indicator Models of Output in G-7 Countries Author info | Abstract | Publisher info | Download info | Related research | Statistics Heather M. Anderson ()
George Athanasopoulos
Farshid Vahid ()
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This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 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 is gauged by non-parametric shape tests, and their ability to predict the probability of recession. We find that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle in cases where linear models fail. Also, our nonlinear leading indicator models for USA, Canada and the UK outperform other models of GDP with respect to predicting the probability of recession.
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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number
20/02.
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Length: 38 pages
Date of creation: Dec 2002Date of revision:
Handle: RePEc:msh:ebswps:2002-20Contact details of provider: Postal: PO Box 11E, Monash University, Victoria 3800, Australia Phone: +61-3-9905-2489 Fax: +61-3-9905-5474 Email: Web page: http://www.buseco.monash.edu.au/depts/ebs/ More information through EDIRC
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Keywords: Business Cycles ; Leading Indicators ; Model Evaluation ; Nonlinear Models ; Yield Spreads. ; Other versions of this item:
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation
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