Investigating the Gross Domestic Product Forecast Accuracy Test, Based on Comparative Information of Inflated and Non-Inflated Accounting Erning Aggregate

The emergence of a new theory of "Macroaccounting" with a new wave of accounting research over the last decade tries to explain and apply accounting information in economic forecasts. The Macroeconomic theory suggests that economists and macroeconomic forecasters use accounting aggregate information at the macroeconomic level. For example, from accounting earnings to forecast GDP, from cost stickiness to forecast unemployment, from the ratio of book value to market value to predict inflation, and from the pricing model of capital assets Used for economic growth. The present study investigates the accuracy of GDP forecasting based on the comparative information of inflated and non-inflated accounting earnings aggregate. The statistical population of this research includes all companies listed in Tehran Stock Exchange. Macro accounting emphasizes economists' view of seasonal accounting for financial accounting. So the data are collected seasonally and observations are 40 times (2007:Q1-2017:Q4). The method is multivariable time series model with rolling regression. Also, for estimating the prediction accuracy, the methods of mean error magnitude, mean squared error and mean error percentage criterion are used. Evidence show that accounting earnings aggregate as well as depreciated accounting earnings increase the accuracy of GDP forecasts over future periods. Another finding of the study is the efficiency of predicting the accuracy of accounting earnings aggregate relative to accounting earnings depreciation. As a result, the spread of aggregate accounting erning growth reduces the forecasting error of GDP and reduces the high-speed GDP. A one-unit increase in the standard deviation of the quarterly growth of quarterly accounting earnings forecasts 0.94 GDP for future periods. The results also showed that GDP forecasts using depreciated Ernings with 7% error and inflated ernings with 10% error are the most appropriate forecasts for GDP.