The UK Inflation Forecast - Case Study Example

UK INFLATION FORECAST REPORT Name of the Institution Name of the student 1.0 Introduction Business forecasting is one of the key tools which economist and financial analyst use to predict the happening in the business and economic world. The policy makers and the government use the forecasting in ensuring that there is economic balance in the country. Bank of England for instance is task in ensuring monetary stability in the country and monetary stability in this case means stable prices, low inflation and confidence in the country currency. The UK government has task the Bank with this responsibility as they seek to meet through the decisions taken by the monetary policy committee. It should be noted that central bank as one of the key policy making body has to safeguard the value of the currency in terms of what it is capable of purchasing. This paper intends to discuss the inflation states in the UK using the data from the bank from the year 1992 to 2012. The data to use include unemployment data, inflation data, Real Gross Domestic Product, exchange rate and Interest rates. 2.0 Result and Discussion The banks monetary policy objective is to deliver price stability, low inflation and in line with that, it supports the government economic objectives including those for development and employment. The government defined price stability by the inflation target of 20 percent. Price stability plays a very important role in economic growth and development giving favorable condition for sustainable growth and development. The bank Act of 1998 gives the banks freedom to set interest rates (bankofengland.co.uk). The graph shows the evolution of exchange rates, The exchange rate varies from point to point moving high and low at various points in time. It is not evenly distributed. Inflation Inflation starts up high with constant reduction in the movement up to 8th point when it shoots up again. Unemployment rate, Employment is high when the economy is at boost and drop drastically at the recession point until the recovery point. Interest rate, Interest rates is not evenly distributed and it varies from point to point Share index and exchange rates within UK. The graph below shows the evolution of the inflation rate. The evolution and growth of the series can be traced back in the early 1970s and 1997 when the MPC was established. The economy of the United Kingdom has grown keeping in mind that it has also faces several challenges in its growth. The recently notable set back was in the year 2008 and 2009 when there as great depression across European countries and the inflation increases with high rate of unemployment and collapse of major financial institutions. The current data indicate the economic stability and high employment rate with low inflation at the set target. The graphs show the evolution of exchange rates, inflation, and unemployment rate, Interest rate, share index and exchange rates within. The UK Bank of England Monetary policy committee is responsible for setting out and controlling the inflation rate within the economy (Enders, 2002). The level of interest rates is decided by special committee called the monetary policy committee. The committee sets the interest rates; it judges will enable the inflation target to be met by the players. This is the key role of the committee since it was established in the year 1997 The ADF explains the inflation data indicates the performance of the economy. Dickey-Fuller = -1.164, Lag order = 4, p-value = 0.000066 alternative hypothesis: stationary > acf (res.ts). From the findings, the results show that the underlying series are stationary. Table1.1: Augmented Dickey-Fuller test statistic Exogenous: Constant Lag Length: 4 (Automatic - based on SIC, maxlag=11) t-Statistic   Prob.* Augmented Dickey-Fuller test statistic -1.164567  0.6860 Test critical values: 1% level -3.516676 5% level -2.899115 10% level -2.586866 *MacKinnon (1996) one-sided p-values. Variable Coefficient Std. Error t-Statistic Prob.   INFL(-1) -0.064564 0.055440 -1.164567 0.2480 D(INFL(-1)) 0.241828 0.109560 2.207272 0.0305 D(INFL(-2)) -0.241158 0.113491 -2.124904 0.0370 D(INFL(-3)) -0.012302 0.110770 -0.111054 0.9119 D(INFL(-4)) -0.401998 0.111211 -3.614738 0.0006 C 0.144673 0.127793 1.132090 0.2614 R-squared 0.304632     Mean dependent var 0.002827 Adjusted R-squared 0.256343     S.D. dependent var 0.462970 S.E. of regression 0.399245     Akaike info criterion 1.075318 Sum squared resid 11.47653     Schwarz criterion 1.256603 Log likelihood -35.93741     Hannan-Quinn criter. 1.147890 F-statistic 6.308471     Durbin-Watson stat 1.928380 Prob(F-statistic) 0.000066 Autocorrelation functions can be described as a set of correlation coefficient between the series and lags of itself over tine while partial autocorrelation function is the partial correlation coefficients between the series and lags of itself over time. The table below shows the autocorrelation of the Inflation Table 1.0: Autocorrelations Lag Autocorrelation Std. Errora Box-Ljung Statistic Value df Sig.b 1 .267 .108 6.044 1 .014 2 -.141 .108 7.745 2 .021 3 -.222 .107 12.033 3 .007 4 -.390 .106 25.431 4 .000 5 -.065 .106 25.811 5 .000 6 .281 .105 32.990 6 .000 7 .163 .104 35.428 7 .000 8 -.131 .104 37.027 8 .000 9 -.173 .103 39.862 9 .000 10 -.118 .102 41.199 10 .000 11 .087 .102 41.937 11 .000 12 .258 .101 48.477 12 .000 13 .170 .100 51.357 13 .000 14 -.036 .099 51.490 14 .000 15 -.105 .099 52.622 15 .000 16 -.176 .098 55.851 16 .000 If the Q statistics used in determining the sample of ACFs are jointly equal to zero, then it is one can precisely conclude that the series is stationary. Therefore, from the table 1.0 above, the significance is equal to zero therefore the series is stationary. The Ljung-Box statistics from the above table gives better characteristics in larger samples; therefore it further indicates that the series is stationary. The figure 1.0 below further explains the stationery of the sample. Figure 2: Auto-correlation In the case of Partial Auto-correlation it is the partial correlation coefficients between the series and lags of itself over time. The table 2.0 below gives the Partial Autocorrelation of the series (Berry & Linoff, 2009). Table 2.0: Partial Autocorrelations Lag Partial Autocorrelation Std. Error 1 .267 .110 2 -.228 .110 3 -.131 .110 4 -.364 .110 5 .093 .110 6 .166 .110 7 -.053 .110 8 -.294 .110 9 -.034 .110 10 .089 .110 11 .148 .110 12 -.023 .110 13 -.018 .110 14 .037 .110 15 .158 .110 16 -.112 .110 There is narrow similarity between the Autocorrelation and Partial Autocorrelation; however, it normally measures correlation between the observations that are K time period apart and that is after controlling for correlations at intermediate lags. It can also be used to produce a partial correlogram, which is used in Box Jenkins methodology (Enders, 2002). Since the difference is not equal to zero, this proves further that the model is stationary and can be presented in the figure 3 below Figure 3: PACF Autoregressive (AR) process involves the inclusion of lagged dependent. Moving Average (MA) process is a simple model, the dependent variable is regressed against the lagged values of the error term. It involves the interpreting the coefficients and t-statistics in the usual way. The ARMA is the combined process of the AR and MA process. From the process, ARMA is the most appropriate method. In computing AIC and BIC for AR (1), showing first lag, AR (2), second lag AR (3), third lag Therefore Y = Û + Ɵy1+Ɵ2y2+ Ɵ3y = 0.267 -0.141 – 0.222 = - 0. 096 For MA (1), MA (2), MA (3), Û + Ɵy1+t+Ɵ2y2-t+ Ɵ3y-1 0.267 + 0.141+ 0.222 = 0.63 And for ARMA (1, 1), ARMA (1, 2), ARMA (2, 1) and ARMA (2, 2) Table 4.0: ARMA calculation Variable Coefficient Std. Error t-Statistic Prob.   FTSE100 0.001108 0.000219 5.056348 0.0000 GDP -0.001550 0.000141 -11.02586 0.0000 INFL 0.006504 0.005585 1.164688 0.2478 IR -0.040612 0.003214 -12.63452 0.0000 M4 4.55E-07 2.26E-07 2.011682 0.0478 U -0.006411 0.003723 -1.721937 0.0891 C 1.136496 0.057009 19.93535 0.0000 R-squared 0.771643     Mean dependent var 0.613061 Adjusted R-squared 0.753615     S.D. dependent var 0.054942 S.E. of regression 0.027272     Akaike info criterion -4.285353 Sum squared resid 0.056525     Schwarz criterion -4.081355 Log likelihood 184.8422     Hannan-Quinn criter. -4.203398 F-statistic 42.80215     Durbin-Watson stat 0.535264 Prob (F-statistic) 0.000000 The new inflation forecast of 0.006504 indicates that reduction of the inflation as it moves back to the original set target. Table 5.0: heteroskedasticity-consistent standard errors & covariance Variable Coefficient Std. Error t-Statistic Prob.   FTSE100 0.001108 0.000289 3.835086 0.0003 GDP -0.001550 0.000161 -9.610088 0.0000 INFL 0.006504 0.004772 1.363141 0.1769 IR -0.040612 0.003288 -12.35218 0.0000 M4 4.55E-07 1.89E-07 2.405761 0.0186 U -0.006411 0.003445 -1.860973 0.0666 C 1.136496 0.055189 20.59287 0.0000 R-squared 0.771643     Mean dependent var 0.613061 Adjusted R-squared 0.753615     S.D. dependent var 0.054942 S.E. of regression 0.027272     Akaike info criterion -4.285353 Sum squared resid 0.056525     Schwarz criterion -4.081355 Log likelihood 184.8422     Hannan-Quinn criter. -4.203398 F-statistic 42.80215     Durbin-Watson stat 0.535264 Prob(F-statistic) 0.000000 From the table, the inflation coefficient is0.0065 which is less than critical value of 0.05 hence it is significant. The T-statistic is 1.363 showing increase in inflation rate of the UK economy. Autoregressive Model (AR) X = ß1X t-1 + ß2X t-2 + ß3X t-3 Like a multiple regression model, but Xt is regressed on past values of X In AR (1) Model Xt = ß0 + ß1X t-1 + et It should be noted that the AR (1) model, we can check the residuals from the regression for any “left-over” dependence. In the ARIMA model, the stationary process and series have some specific characteristics which include constant mean, constant variance and constant auto covariance structure. It should be noted that the latter refers to the covariance between y (t-1) and y (t-2) being the same as y (t-5) and y (t-6). From the UK data Analysis, it is observed that one stable curve indicates the trade-off between inflation and unemployment and the two are inversely related. This is to say employment decreases as inflation increases. As a result of economic growth, the aggregate demand (AD) will automatically increase and therefore leading to an increase in unemployment. At first, there was a little pressure for increase in wages. Nevertheless, as economy grows, more people will get employed and there will be increase in wage rate and employment in general. 2.6 Estimate the following version of the Phillips curve As the wage rate increases, the cost of production of the firm will also increase and the additional cost will be passed on to the customers in the form of higher prices hence increase in unemployment has resulted to increase in inflation as shown the findings above. Bowersox, & Closs, (2002) indicates that unemployment might not only suffer from illusion as they thought to increase in wages, it represent real wage. The figure below explains the relationship between the two variables The relationship between the two only existing in short run, however in the long run, since unemployment always returns to its natural rate. Using the UK data given, Philips curve relationship tends to prove to be true for many economies like in UK. In the year 2008-2009, the curve dropped indicating that break down of economy that has suffered from high inflation and monetary policy control collapse hence the great depression (Bowersox, & Closs, 2002). 2.7 Test for serial correlation of the least squares residual One of the most concerns of monetary policy is the inflation target that is given by the MPC through UK government. It states that the primary objective is to ensure that CPI inflation is equal to 2%. However, it further clarifies that in so doing, the MPC should be mindful of the implications that our policy actions have for the growth and employment in general. The correlation of the last square residual of unemployment and inflation is shown below INFL U INFL 1 0.4919274501770742 U 0.4919274501770742 1 The monetary policy should be concerned with the economic growth and inflation. The former chief economist of bank of England Mervyn 1997 referred to the value of the constrained discretion built into the inflation target regime. Eq. (1) INFL(-1)  0.008604 -3.554494 -0.589759  0.997637 -0.216252  8383.650  (0.00582)  (1.49834)  (0.58512)  (0.10755)  (0.10824)  (3318.14) Equation; Y = 1 + 0.0086X1 +-0.000582X2 Misspecification Parameters: Alpha 1.0000 Beta 0.0100 Sum of Squared Residuals 18.23008 Root Mean Squared Error 0.468657 End of Period Levels: Mean 2.665570 Trend -0.039824 The alpha is 1 indicate low level of misspecification with 0.01 beta 2.10Use Eq. (1) to perform inflation forecasts This particular discretion relates to the speed with which inflation is returned to its original set target. Variable Coefficient Std. Error t-Statistic Prob.   U 0.409465 0.043632 9.384525 0.0000 GDP 0.008189 0.001033 7.926982 0.0000 C -2.832044 0.492683 -5.748211 0.0000 R-squared 0.575456     Mean dependent var 2.214301 Adjusted R-squared 0.564843     S.D. dependent var 0.969394 S.E. of regression 0.639475     Akaike info criterion 1.979136 Sum squared resid 32.71424     Schwarz criterion 2.066564 Log likelihood -79.13415     Hannan-Quinn criter. 2.014260 F-statistic 54.21877     Durbin-Watson stat 0.602974 Prob(F-statistic) 0.000000 The coefficient indicates correlation with GDP and Inflation conclusion This paper has discussed the relationship between the monetary policy and economic growth. The Bank of England has the sole role of ensuring economic growth and stability through price stabilization. The UK as a country has experience turbulence economic environment with deep recession and economic slow down. The most notable was the financial crisis of the year 2009/2008. This has made the banks to improve their monetary policies to ensure proper economic growth and employment. The findings indicate inverse relationship between inflation and unemployment. Stable prices and currency improves investor confidence hence allowing more investment in the country which later translates to job creation resulting to increase in employment rate and wage increase. Low growth in employment and nominal wages can be easily offset by low growth on the high growth path. This is the main reason why the inflation targeting central bank should do all it can to get economy onto the higher growth path. In my view, the bank of England needs to raise the chances of staying on that favorable path of growth that has been exhibited. This analysis has revealed that inflation plays important role in stabilizing economy and the banks should maintain stable interest rates and currency stability for long term growth and development. References Bowersox, D. & Closs, D. (2002). Economic analysis and business forecasting New York: Mc-Graw-Hill Enders, W 2002 Applied Econometric Time Series, 2nd Ed. (New York, NY: John Wiley and Sons Hanke, J. & Wichern D (2009) Business Forecasting, 9th Ed. Upper Saddle River, NJ: Prentice Hall http://www.bankofengland.co.uk/monetarypolicy/Pages/default.aspx Read More