Background: The goal of this study was to forecast pulse production in six countries: Afghanistan, Bangladesh, China, India, Nepal and Pakistan (2020-2027). In this study, time series forecasting was used. Methods: The data series were divided into training set from 1961 to 2015 for model building, testing set from 2016 to 2019 for validation and finally, after selecting the best model, forecast was used from 2020 to 2027, the models were compared. The best-fit model was chosen based on the minimum ME, RMSE, MAE, MPE, MAPE, MASE, ACF1 values on the training data set and the minimum MAPE values on the testing data set. Result: The best fitted model for India was NNAR (1,1). Similar to Afghanistan, the best fit model for forecasting was NNAR (3,2). The best fit model for forecasting in China was ARIMA (0,1,1). The best fit model for forecasting in Nepal was ARIMA (1,1,0). The best fit model for forecasting in Pakistan was ETS (A, N, N) (M, N, N). With a 15.73 per cent growth rate from 2020 to 2027, the best models predict that the production of pulses in (Afghanistan, China, India) will increase until 2027. India will continue to be the largest producer of pulses among the six countries, with production expected to reach 1088.778 thousand tons in 2027. Afghanistan and China have extreme growth rates of 25.19% and 11.95%, respectively, while the rest of the countries have relatively stable production volumes. These results may be crucial for developing an effective agriculture production policy, whether by providing forecasted production values or evaluating such policies.

Economics suffers from a blurred view of the economy due to the delay in the official publication of macroeconomic variables and, essentially, of the most important variable of real GDP. Therefore, this paper aimed at nowcasting GDP in India based on high-frequency data released early. Instead of using a large set of data thus increasing statistical complexity, two main indicators of the Indian economy (economic policy uncertainty and consumer price index) were relied on. The paper followed the MIDAS–Almon (PDL) weighting approach, which allowed us to successfully capture structural breaks and predict Indian GDP for the second quarter of 2021, after evaluating the accuracy of the nowcasting and out-of-sample prediction. Our results indicated low values of the RMSE in the sample and when predicting the out-of-sample1- and 4-quarter horizon, but RMSE increased when predicting the 10-quarter horizon. Due to the effect of the short-term structural break, we found that RMSE values decreased for the last prediction point.