This paper examines interest rate forecasts made for the period 1982-90 and examines three issues: (1) Is there a general agreement among analysts about the level of interest rates six months in the future? (2) Are all the forecasters equally good? (3) Are the forecasts valuable to prospective users
Forecasting changes in UK interest rates
โ Scribed by Tae-Hwan Kim; Paul Mizen; Thanaset Chevapatrakul
- Publisher
- John Wiley and Sons
- Year
- 2008
- Tongue
- English
- Weight
- 199 KB
- Volume
- 27
- Category
- Article
- ISSN
- 0277-6693
- DOI
- 10.1002/for.1043
No coin nor oath required. For personal study only.
โฆ Synopsis
Abstract
Making accurate forecasts of the future direction of interest rates is a vital element when making economic decisions. The focus on central banks as they make decisions about the future direction of interest rates requires the forecaster to assess the likely outcome of committee decisions based on new information since the previous meeting. We characterize this process as a dynamic ordered probit process that uses information to decide between three possible outcomes for interest rates: an increase, decrease or no change. When we analyse the predictive ability of two information sets, we find that the approach has predictive ability both inโsample and outโofโsample that helps forecast the direction of future rates.โCopyright ยฉ 2008 John wiley & Sons, Ltd.
๐ SIMILAR VOLUMES
## Abstract This study compares the forecasting performance of a structural exchange rate model that combines the purchasing power parity condition with the interest rate differential in the long run, with some alternative exchange rate models. The analysis is applied to the Norwegian exchange rate
## Abstract Recent empirical finance research has suggested the potential for interest rate series to exhibit nonโlinear adjustment to equilibrium. This paper examines a variety of models designed to capture these effects and compares both their inโsample and outโofโsample performance with a linear
This paper proposes a new forecasting method in which the cointegration rank switches at unknown times. In this method, time series observations are divided into several segments, and a cointegrated vector autoregressive model is fi tted to each segment. The goodness of fi t of the global model, con
In this paper we develop a latent structure extension of a commonly used structural time series model and use the model as a basis for forecasting. Each unobserved regime has its own unique slope and variances to describe the process generating the data, and at any given time period the model predic