This helps the financial institutions to organize themselves in such a way that they can forecast more on the future of the economy in order for them to know the best way to curb it when it actually happens. This paper discusses the forecasts on inflation and output growth, the way it can be improved and its usefulness. Discussion What is the failure on the projection of inflation and growth forecasts? Inflation projection is the forecast by economists of the current economy based on real time data and the effects it will have in the future of a country. This is to mean that when a country’s central bank does not practice inflation projection then it will not be able to control the rates when the actual inflation occurs. The failure in the other hand will lead to unclear and untimely communication of policy objectives, plans and tactics (Mandal, Gavin, & Mandal 2000). It can also lead to increase in economic and financial uncertainty (Bergheim 2008). Growth forecasts in the other hand are the prediction of a future economic growth which can be seen through the current economic times. Growth predictions are important as it prepares a country for an incoming increase in a country’s GDP....
Forecasts from univariate non-linear models (Bergheim 2008). Benchmark forecast The first benchmark model is the suite is that the variable of interest is equal to the unconditional mean over the recent past. Another example is the random walk (RW) or no change. These models are often found to forecast
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The forecasts on inflation and output growth
very well. Auto regression is the third benchmark suite. In this model unvariate representations can often be captured by low order systems. While not robust to structural change they are robust to misspecification following incorrect choice of explanatory variables (De Jong 2007). Forecasts from linear vector-auto regressions Linear vector autogressions (VARs) are linear relationships between small set of variables. Economic interests in them relate to identification (structural VARs, SVARs) but unless the restrictions over identify the model which is not the case usually. In it includes basic VARs which are the standard linear reduced form of VAR (Bergheim 2008). There are also the double differenced VARs, forecast from Bayesian VARs; this form of VAR does not suffer from parameterisation. It uses priors with small number of parameters to extract the signal parsimoniously. The last one in the linear vector autogressions is the recursively-estimated VARs (Hudson 2006). Forecasts from univariate non-linear models In this there are a large number of non-linear models that are used to model unvariate processes. It includes models from the smooth transition autogressive (STARs), the threshold autogressive (TAR) and Markov Switching (MS) (De Jong 2007). Smooth transition autogressive (STARs) Estimation of the model is by non-linear least squares. The parameters are sometimes difficult to obtain, in which case a grid search may be
Summary
When general prices of goods and services in a country rise, the value of money drops. This in return leads to inflation. Inflation then in the long run leads to the decrease of purchasing power of a currency. …
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