The difficulties of a common monetary policy in an economic and monetary union are well known where there are differences between the stage of the business cycles and inflation pressures. In this paper, I will show that in a simple open macro model and by using ‘weighted mean mechanism’ monetary authorities can employ a common monetary policy to synchronise diverging business cycles in the member states.
This paper investigates the model estimation and data forecasting of exchange rate using artificial neural network. Recent studies have shown the classification and prediction power of the neural networks. It has been demonstrated that a neural network can approximate any continuous function. Here, in a technical approach, it has been used ARIMA and neural network for a short-term forecast of daily USD to Rial exchange rate. ANN is employed in training and learning processes and thereafter the forecast performance measured making use of two common loss functions. The comparison demonstrates that neural network is far better than ARIMA, the error is about the half.Thereafter, in a fundamental approach via another neural network the effects of some of the most important economic variables on exchange rate prediction in a long-term sense are studied. By sensitivity analysis, the importance and the weight of each economic variable on exchange rate has determined. The results show that it is possible to estimate a model to forecast the value of exchange rate even by having access to a limited subset of data.
The inflation rate, which measured using consumer price index, can be separated into a combination of two persistent and temporary components. This separating is particularly important in analyzing inflation rate and policies to control it. In fact, without knowing the persistent component of inflation, called core inflation, quantitative targeting of inflation may not be accurate. Core inflation as a more persistent component can be measured stripping out the transitory movements in prices. The understanding of the behavior of the national core inflation rate series needs to understand provincial core inflation since the construction of the former is based on the provincial series. So, the purpose of this paper is the estimation of provincial and national core inflation in Iran. Core inflation is unobservable variable, so it estimated using Space State Model and Kalman Filter. Results show that average core inflation in all of the provinces, as well as Iran, is less than average underlying inflation. The standard deviation of core inflation in some provinces is more than underlying inflation. While core inflation in other provinces, as well as Iran, has more standard deviation as compared to underlying inflation.
The euro has been introduced to a region that contains many discrepancies and differences. While there are many countries with different business cycles, exerting a single monetary policy which favors all the countries is impossible. I will show that in a simple open macro model by using "weighted mean mechanism", monetary authorities can exert a common monetary policy to synchronize business cycles and to diminish loss functions in the member states. As we can see by using optimal monetary policy, the business cycles become much more stable and even in 2009 we do not see any recession for Germany and France. Although In this model between 2006 till 2012 the MU (Monetary Union) interest rate should be higher than the United States one, the agent's countries would be in boom rather recession. If MU interest rates in 2012 and 2013 were less than the actual ones, recession in two countries would change to boom for them.
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