جستجوی مقالات مرتبط با کلیدواژه "شاخص شرایط مالی" در نشریات گروه "اقتصاد"

In the last few years, the financial condition index has been an important indicator to determine the situation of each country. The Financial Conditions Index can be defined as the current state of the variables that affect the future state of the economy. The Financial Conditions Index is a summary index of financial variables that is effective in predicting the economic situation of a country.
Financial conditions in any country are one of the most important issues in the economy. The purpose of this article is to determine and calculate the financial condition index for Iran using the basic component analysis method and time series data from 1990-2020. The results of calculating the financial condition index show that the credit channel has more weight than other channels and this shows the importance of the volume of credit in estimating the country's financial condition index during this period in the research. After that, the asset channel, ie the stock price index and the housing index, have the most weight. Among these, the exchange rate and interest rate channels have the lowest weight. Therefore, the central bank can adopt a forward-looking approach in monetary policy, so that by changing its retrospective approach in monetary policy decisions, it can show the most correct reaction.

Due to the forward-looking nature of economic decisions, forecasting the trend of macroeconomic variables has a fundamental role in economic planning and policies. For this purpose, the present study attempts to extract the financial conditions index for the period 1991-2019, then the movement trend of macroeconomic variables has been forecasted using the Time-Varying Parameter Factor-Augmented Vector Auto-Regressive (TVP-FAVAR). The results show that the financial conditions index has undergone significant changes in recent years, especially in the 2010s, which has had an effective role in the fluctuations of other markets due to the influence and change in the behavior of other macroeconomic variables. Furthermore, forecasting the trend of macroeconomic variables also indicates that this trend is unstable; in other words, instability in the trend of macroeconomic variables is evident and we cannot expect a positive outlook for the above variables.

In recent years, the financial condition index (FCI) has been used in many countries as an important index to determine the state of macroeconomic policies. For this purpose, in the present study, the effects of financial condition index on macroeconomic variables were investigated by applying the time-varying parameter factor-augmented vector autoregressive model (TVP-FAVAR) and using quarterly data during the period (1991-2019). The results indicate that the response type and response rate of macroeconomic variables were different due to the financial condition index shock over time, and this indicates the necessity of employing the parameter - variable approach. According to the obtained results , The unemployment rate and economic growth rate variables in the short and long term showed a Negative and positive response to behavioral changes in the financial condition index variable, respectively; The effects of the financial Conditions Index shock on the inflation rate variable appear after one period; However, the response of this variable to the financial condition index shock in the short and long term has been different according to the conditions prevailing on the economy of the country . Also, the financial conditions index shock in the short run has improved the Gini coefficient variable, but in the long run, especially in the late 2010s has rised the income gap. The response of the budget deficit variable to the financial condition index shock in the whole period under review was positive and the financial condition index shock has increased the government budget deficit.

Structural changes and economic and financial condition change the nature of economic models, and this shows the importance of using models that consider the dynamics of model parameters over time.In this paper, we use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can track expectations about financial conditions and economic trends. Time variation in the model’s parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection that allow the financial variables entering into the FCI to change over time. The results show that the financial conditions in our country have been associated with many instabilities, which has periodically weakened the efficiency of the country's economy by creating imbalances in the financial system of the economy.

Today, the financial condition index is introduced as a tool for forecasting macroeconomic variables in many countries of the world. The aims of the present study are, first, to construct the financial condition index in different patterns of time-varying parameters and then, to analyze the performance of the financial conditions index in order to forecast the macroeconomic variables using the mean squared forecasting error approach (MSFE) and the cumulative sum of log-predictive likelihoods.The results show that the constructed financial conditions index can adapt to different states of Iran's economy and its fluctuations increase over time. Based on the obtained results, the time-varying parameter factor-augmented vector autoregressive model (TVP-FAVAR) shows less forecast error in some macroeconomic variables compared to the factor-augmented vector autoregressive model (FAVAR) and also the vector autoregressive time-varying parameter factor-augmented model (FA-TVP-VAR). The use of combined time-varying parameter factor-augmented vector autoregressive models when the coefficients and selected variables of the financial conditions index change depending on the economic condition, improve the performance of the financial conditions index in forecasting macroeconomic variables. The cumulative sum of log-predictive likelihoods shows that using variable parameter patterns does not have a great effect in reducing the forecast error of some macro variables.

Monetary Policy is one of the important instruments of the economic demand management and is much important in policy making decisions. In this direction, determination the rule of monetary policy making and discretion of expansionary and contractionary monetary policies are also important for policy makers and economists. In this study, the monetary conditions index (MCI) over the period 1352-91 and financial conditions index (FCI) over the period 1377-91 calculated using the Principal Component Analysis for Iran's economy. According to the results, the monetary conditions index impact any 3 channel exchange rate, interest rate and credit channel. Also in the calculation of index financial conditions, in addition to the the above channel 3, also has its effect channel Finance. Credit channel has the greatest weight in both financial and monetary conditions index, while the interest rate channel of the lowest weight than the 3 other channels in both the index, implying that credit channel is more important than the other channels in determining the level of output in Iran.

In recent years “Financial Conditions Index" (herein FCI) has been used as a key indicator of the monetary policy condition. Considering the importance of this index, the purpose of the present research is to reach a comprehensive index that includes all the monetary transmission mechanisms based on the Iranian economy. To this aim, a weighted average of the banking interest, real exchange rates , credits and price of other assets (namely the stock price index, and housing price index ), need to be calculated. The weighted average of the variables are obtained by estimating the, backward-looking aggregate supply and demand equations for Iran. In the next stage, in order to test the validity of the obtained index, and because of the importance of price stability for the central bank, the predictive power of the index from inflation in Iran was examined using Non-Nested Tests and Root Mean Square Error (RMSE). Seasonal data series are collected for the period of 1991-2012.The findings of this study show that the weight of housing price variable is higher than the other variables, whilst, the stock price index coefficient was not significant in any lag. Moreover, the results of second stage tests, indicate a good predicative power of the FCI index from the inflation in Iran.