چکیده:
redicting the amount of country imports toward assessing trade balance and its effect on the balance of payments (BOP) and finally money supply, general level of prices and the rate of economic growth is of paramount importance. Therefore, economic policymakers seriously need a model which cannot only predict the volume of imports well but also be capable of revising the initial prediction over time as soon as new data for the explanatory variables are available. To this purpose, mixed frequency data sampling model was used which allows time series variables with different annual, seasonal and even daily frequencies to be used in a single regression model. In estimating the model using the software R, annual real imports, real exports and quarterly of real GDP, real exchange rate and the volatilities of the real exchange rate in the range of 1988 to 2014 are used. Information related to 2014 is not used in preliminary estimation of relationship, so that the predictive power of the model outside of the estimated range can be tested. The proposed model predicts that real imports of goods as49948 million dollars for 2014 which is associated with an error of only41 million dollars, or about 8 percent, compared to its real amount achieved of49907 million dollars. The result suggests that the predictive power of the MIDAS model is very satisfactory.
خلاصه ماشینی:
Predicting the Country Commodity Imports Using Mixed Frequency Data Sampling (MIDAS) Model1 Vida Varahrami*2, Samaneh Javaherdehi3 Received: 2016, November 7 Accepted: 2017, May 8 Abstract redicting the amount of country imports toward assessing trade balance and its effect on the balance of payments (BOP) andfinally money supply, general level of prices and the rate of economic growth is of paramount importance.
2. 2 Theoretical Foundations of Mixed Frequency Data Sampling (MIDAS) In the traditional method, for time series modeling to predict economic variables, all the variables involved in the model have not necessarily the same frequency, for example, if the dependent variable is quarterly, explanatory variables should also be quarterly.
The approach of "Mixed Frequency Data Sampling (MIDAS)", where the dependent variable usually has lower frequency, is based on two characteristics as follows: having a regression structure such as Autoregressive Distributed Lag (ARDL), as well as a weighting function (for synchronization between the low-frequency and high- frequency variables).
Su, Zhou and Wang (2013) used data for period 1, 1988 to 4 2010 related to returns of securities of stock market weekly as explanatory variable for the economic growth forecasting in Singapore, and the results indicate that MIDAS model has more power compared with regression models for high frequency data.
Bayat and Noferesti (2015) in her thesis, entitled Implementation of Mixed Frequency Data Sampling, in relation with prediction of economic growth rate concluded that, the predictive power of the model studied using MIDAS has been good.
Using combined data of time series with different frequencies modeling that is known as MIDAS, a model was provided to predict and estimate the imports of goods.