introduction: agriculture as one of old sectors of economy has been important role in the supply food for peoples and raw materials. globalization causes rapid growth of world trade and reduces information and communications costs. globalization and rapid growth of trade increases the potential benefits of trade for agriculture from various aspects. the potential benefits of trade for agricultu...

Objective: The present study aims atinvestigating the behavior of realized volatility for high-frequency data of Tehran Stock Index from April28th, 2012 to August 8th, 2018. Methods: Three different types of HAR models including of HAR-RV-CJ, HAR-RV and HAR-RVJ were used to analyze the Realized Volatility. Results: The obtained results of three diverse models revealed that the estimated Reali...

We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor’s 500 (S&P 500) and several other indices, we obtained good performance using these models in an out-of-sam...

High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhanc...

In this paper we examine the forecast accuracy of four univariate time series models for 47 macroeconomic variables of the G7 economies. The models considered are the linear autoregressive model, the smooth transition autoregressive model, and two neural network models. The two neural network models are different because they are specified using two different techniques. Forecast accuracy is as...

Forecasting with Spatial Panel Data This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or...