Interest Rate Forecasting using Regression Analysis
Introduction
? Forecast of interest rates can be done in many different ways, qualitative (surveys, opinion polls) as well as quantitative (reduced form and structural approaches)*
? Example of methods in quantitative approaches
- Regression method
- Univariate method (e.g. ARIMA)
- Vector autogressive models (VAR)
- Single equation approaches
- Structural systems of simultaneous equations
This paper will focus on the structural approach relying mainly on the Regression Model technique
Advantages of the structural approach:
? Rests on economic theory (unlike reduced form methods such as VAR)
? Can trace the effects of changes in macroeconomic variables to interest rates (more likely long rates)
Disadvantages of the structural approach
? Data not always readily available at the required frequency
To forecast interest rates using macroeconomic variables imply the use of a structural approach ? of which 2 processes are involved: 1) model building, and 2) forecasting
? Model building: model the relationship between interest rate and relevant macro variables as prescribed by economic theories and quantify (estimate) the relationships using an econometric technique
? Forecasting ? use the estimated model and assumptions on explanatory variables to project future values of the interest rate
Literature Review
A Structural approach to interest rate forecasting
Model building
? Economic ...