nonparametric regression

The relation between inflation and inflation uncertainty is one of the most important empirical relationships in macroeconomics. Some views suggest that the rise in inflation creates real costs through the impact on inflation uncertainty. Another point of view considers the increase in inflationary uncertainty as a priority to the increase in inflation. Becoming the causal direction of this relationship will determine the optimal strategy of central banks in the implementation of monetary policies. In this paper, first, using the MS model with three regimes, future inflation forecast errors are calculated, and then, using the BIP-GARCH method, the conditional variance of inflation is extracted as a proxy of inflation uncertainty. Finally, by using Granger causality test, various hypotheses on the relationship between inflation and inflation uncertainty are investigated, and with the nonparametric regression method, this relationship is inflated in different intervals. The findings indicate that inflation uncertainty increases during periods of political turmoil and is strongly influenced by currency fluctuations. The refinement of outlier forecast errors caused by large and temporary shocks will be the choice of the IGARCH(1,1) model, which means that the effect of shocks is unlimited for inflation uncertainty over time. Also, the non-parametric estimation between inflation and inflation uncertainty shows the dependence of this relationship in which it is placed. In addition, the results show that inflation uncertainty affects future inflation in a non-linear way. As a result, the central bank can control the inflation rate by reducing the uncertainty of monetary policy by correcting inflationary expectations.

Transportation is one of the most important constituent parts of urban life that determines the shape and way of social and economic development of the city. In this regard, the quality of transportation in Hamadan city, which can be very useful in its development, was considered. The nonparametric regression model and artificial neural network were used to assess the citizens' satisfaction from the transportation system. For this purpose, first, a questionnaire was developed based on three main indicators (equipment and facilities status, physical status, management status, and service delivery). Citizens' perspective was gathered. Then, using a nonparametric regression model, independent indicators and satisfaction as an independent variable were used. The correlation and root mean square of the output errors of this model were 0.914 and 0.334 respectively. In another approach, using an artificial neural network, a model with three intrinsic neuron structures, one hidden layer and one output neuron was constructed. The output correlation of this model was 0.998 and the mean square error of the error was about 6 times lower than regression model. The results showed that the neural network model with both linear and nonlinear correlation estimates is more versatile and more suitable than nonparametric regression. On the other hand, price indices with coefficient (0.885), equality and welfare with (0.795) and decrease in demand for travel (0.790) are the most effective indicators for citizens' satisfaction with urban transport network.