and number Research report Introduction and the ment of problem The operation of monetarypolicies is the central concern of all central banks. This is particularly sure by their forecasts on inflation and output growth. For instance, forecasts of inflation and growth can be observed in the “fan charts” published by the Bank of England in the inflation journals, produced quarterly. These forecasts lie on future prospects, partly affected by other exogenous variables like exchange rates, import and exports, and interest rates that are determined outside the model. It should be pointed out at this point in time that, some projections given by the Bank of England as demonstrated by their models, reflects judgement by the Bank committee (Clark and McCracken 78).
The demonstration here is simple; the Bank of England has constructed a “suite”, in which it is basis lies in the “statistical forecasts model” that will be used to monitor the process of policy and forecasts. The Bank of England adopts this as basis to communicate the rate of inflation and growth. This paper aims to examine how the forecasts for growth and inflation compare with, models like auto-regression and univariate models.
The study on these forecasts “holds interest rates constant” at their going market value (Burnham and Anderson 123). Research Questions 1. What is the failure on the projection of inflation and growth forecasts? 2. What “suite of statistical forecasting models’ to be used by the Bank of England? 3. What best models explain the macro-economic stability in the Bank of England? Methodology Describing large number of linear and auto-regression specifications in growth and inflation forecasts sometimes serve as real bench-marks for growth and inflation projections, but the employment of univariate models can be a great significance.
Inflation is particularly affected by changes of mean value due to changes of exchange rates regimes. For this reasons rolling regression is a better measure of inflation forecasts (Zimmermann 605-657). Review of literature As mention by Burnham and Anderson (1998), the use of auto-regression and nested random walk models give the best estimate on growth and inflation forecasts. “The employment of the Non-linear Univariate exponential Smooth Transition Auto-regressive (NESTA) do well in the statistically forecasted data” (Pg 67).
In the design also, according to Clement and Hendry (1998), the use of non-least squares is practically helpful in growth forecasts. In that case therefore multivariate models based on ordinary least square methods (OLS) helps to leverage the monetary growth in their forecasts. They argue that, random work is also employed in the forecast of the changes on the interest rates. Finally, the monetary vector auto-regression analysis is employed because it incorporates the annual changes (Burnham and Anderson 123). Data Data are gotten from the annual inflation report and statistics; “annual oil price inflation, growth of GDP, annual effective rate of exchange rates and change of broad and narrow money”.
Statistical data can also be gotten on the real rate of consumption and investment, factor series, and imports and exports (Burnham and Anderson 123). Forecasts on real growth and inflation The total sample data size is taken between 1963Q1 – 2007Q2. Data on univariate projection are to be taken in 1996Q2. This in real explanation means that, a fore period of 1998Q3 – 2007Q2 is used (Burnham and Anderson 123). Forecast analysis for RPIX inflation projection Data is combined between 1964Q1 – 2006Q2.
This period reveals large changes in real “mean value of inflation”. For effective forecasts, “rolling of parameter is necessary and sampling on a fixed size”. Models of window 7-year data will be run for quarterly data. For all these inflationary regression projections, the best benchmark of analysis is on OLD, despite of it is simplicity (Clement and Hendry 120). Conclusion Regression models for instance the ODL can do well in the projection of forecasts on growth and inflation than inflation reports from Bank of England prepared by Bank Committee Members (BCM).
This result is an indication of the prevailing macro-economic stability. Work cited Burnham, K.P. , Anderson, D.R. Model Selection and Inference. Springer Versa, Berlin. 1998. Clark, T.E. , McCracken, M.W. ‘Improving forecast accuracy by combining recursive and rolling forecasts’, Federal Reserve Bank of Kansas. 2004. Clark, T.E. , McCracken, M.W. , 2006. The predictive content of the output gap for inflation: resolving in-sample and out-of-sample evidence. Journal of Money, Credit and Banking 38, 1127–1148. Clements, M.P. , Hendry, D.F. Forecasting Economic Time Series. 1998. CUP, Cambridge. Zimmermann, A. (Eds. ), Handbook of Economic Forecasting.
Elsevier, pp. 2006. 605–657. Elder, R, Assessing the MPCs forecasts. Bank of England Quarterly Bulletin. 2005. 326–347.