Nighttime light remote sensing data is regarded as an important data that can be used to measure human socioeconomic activities. Because of the positive correlation between long time-series nighttime light data and economic development, long time-series nighttime light data can be used to construct GDP forecasting models to predict GDP. However, there are different temporal resolutions, measurement standards, and errors due to different sources of nighttime light data. How to eliminate the data errors and the differences between different data sources to construct a reliable long time-series nighttime light data is currently a major problem. In addition, different GDP forecasting models have different applicable conditions and forecasting accuracy. How to choose the optimal GDP forecasting model is another major problem. In this paper, data errors and differences are dealt with at first to obtain long time-series nighttime light data that can be quantitatively analyzed. Then, the advantages and disadvantages of different GDP forecasting models are analyzed and compared. The main contents of this paper are as follows.