Since decrease in inflation has been one of the aims of economic policies and there has been a close relationship between money stock and inflation, in this paper the causality between money supply and PRICES was examined. The goals of this study were to examine causality between money supply and PRICES of AGRICULTURAL products using the causality test of Granger and prediction of four macroeconomics variables. The proxy for money supply were (M1) and (M2). The proxy for retail sales price index of AGRICULTURAL products consumption and wholesale price index of AGRICULTURAL products were respectively (CPI) and (WPI). The data used in this study were collected from internet, PDS database and central bank publication. The results indicated that (WPI) and money supply have dual causality with each other, but money supply is the cause of (CPI).

Inflation rate in Iran has been a growing concern despite the intense struggles, gradually becoming the country's economic problem. This study investigated the influence of inflation uncertainty on price indexes of AGRICULTURAL products using time series data of 1974-2007. The inflation uncertainty is estimated applying GARCH models. The desired model was evaluated by vector auto regression (VAR). Impulse response functions (IRF) and variance decomposition has also been checked. Impulse response functions in the AGRICULTURAL sector against shocks from factors of value-added agriculture, degree of trade freedom, real exchange rate, the volume of liquidity, inflation uncertainty and the consumer price index to price index of AGRICULTURAL products during ten periods in the future were examined. The variance decomposition results indicated that the AGRICULTURAL sector in the short-term, mid-term and long-term, more volatility PRICES of AGRICULTURAL products are explained by shocks. Results also revealed that from the VAR test in the AGRICULTURAL sector indicated that inflation uncertainty together with other variables positively and significantly affects the price index of AGRICULTURAL products. Thus, this variable should be taken to account in order to analyze price behavior of AGRICULTURAL products and their determinants.

Autoregressive integrated moving average (ARIMA) has been one of the widely used linear models in time series forecasting during the past three decades. Recent studies revealed the superiority of Artificial Neural Network (ANN) over traditional linear models in forecasting. But neither ARIMA nor ANNs can be adequate in modeling and forecasting time series since the first model cannot deal with nonlinear relationships and the latter one is not able to handle both linear and nonlinear patterns simultaneously. Hence by combining ARIMA with ANN and designing the hybrid model, data relationship can be modeled more accurately. In this research, a hybrid of ARIMA and ANN models is designed and its prediction performance is compared with those of competing models. Forecasting performance is examined using common criteria such as MSE, RMSE and MAD. Also the significance of any difference between these measures is tested through application of Granger and Newbold statistic. Forecasting results for world wheat price data indicates that combined model significantly improves accuracy achieved by separate models.

Many economists believe that the main channel that inflation makes its harmful impacts on economy is the inflation uncertainty. This paper investigates the determinants of AGRICULTURAL product PRICES emphasizing on the detection of possible impacts caused by inflation uncertainty with using time series data over 1972-2005 period. The empirical methodology employs the GARCH technique to model the uncertainty variable, as well as econometrics methods to investigate possible causal effects and relations among the involved variables.Results indicated that there have been long-run relationship and co-integration between involved variables and AGRICULTURAL product PRICES. Furthermore, accompanied by other variables, the effects of inflation uncertainty as a new variable was significant.

There are implicitly some questions about the role of economic policies in growth and stabilization. Two important macroeconomic instruments are fiscal and monetary policies. Output growth and structural changes are not independent from price stability and foreign equilibrium, so that if inflation push-that appears in development process-is forgiven, the resources will be allocated inefficiently and this will prevent development.In this research, data of years 1972 to 2001 and VAR method for dynamic analysis of effective factors on growth and inflation in AGRICULTURAL sector are used. Results show that in AGRICULTURAL sector, causality direction is from growth to inflation so that growth of AGRICULTURAL sector has a negative effect on inflation in this sector.Variable of capital stock, labor, government and individual investment have positive effects on the output of AGRICULTURAL sector in long run and variables of liquidity growth and exchange rate growth have positive effects on inflation in AGRICULTURAL sector in long run.

Oil price shock can be the main source of disturbance in the oil-producing economy, such as Iran; therefore, the study of the interaction between oil PRICES and macroeconomic variables is important. This study examines the Granger causality relationship between oil price, inflation, industrial products and economic growth, based on data from Iran for the period 1980-2017 using VAR approach. The results indicate that 1) the economic growth is affected by the oil price and INFLATION; While, industrial products have not been the cause of Iranian economic growth in the studied period, 2) economic growth positively react to positive oil price shock, but as time goes on its effect fades, 3) in the short-run, the positive inflation shock causes a reduction in economic growth and industrial products, and 4) in the short-run, the positive inflation shock due to the uncertainty in business atmosphere, may reduce the growth of industrial products, but in the long run, due to adjustments in the industrial sector and an increase in the price of the products, the growth of industrial products becomes positive.

The main goal of the present paper is to investigate the relationship between inflation and uncertainty, within the related theoretical foundations’ framework in Iran Economy during the years1959-2013 (1338-1392 corresponding to Iranian calendar). For this purpose, the causality relationship between inflation and its uncertainty has been studied using the nonlinear Group Method of Data Handling (GMDH) algorithm. Findings show that there is a one-way nonlinear cause from inflation to inflation uncertainty in Iran Economy. In other words, inflation has a significant and a strong effect on inflation uncertainty.

In recent years, Iran's economy has experienced stagflation, low inflation rates or low unemployment rates at the cost of a considerable increase in the other. Government seeks to find ways to repress and manipulate markets to lower its debt financing costs. In this field, financial repression will provide very important policy implications for Iran's economy, which will make it possible for the country's macroeconomic policymakers to target inflation and unemployment rates. Based on this, this paper investigates the effect of financial repression on the causal relationship between inflation and unemployment using the bivariate and multivariate Vector Error Correction Model (VECM) between inflation and unemployment, taking into account the financial repression index (exchange rate and interest rate) in the period from 1987 to 2020. The findings show that despite the existence of a strong long-term two-way causal relationship between inflation and unemployment in the bivariate causality test, there is no significant causal relationship between inflation and unemployment in Iran in the presence of the indicators of financial repression. These findings indicate the absence of inflation-unemployment trade-off in Iran and support that this trade-off is not behavioral and is completely influenced by the government's interventions in the economy.Keywords: financial repression, unemployment, inflation, causality test.