Forecasting with Big Data: A Review <s> Big Data <s> This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components. Feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large. The estimated factors are shown to be consistent, even in the presence of time variation in the factor model. <s> BIB001 </s> Forecasting with Big Data: A Review <s> Big Data <s> Many believe that "big data" will transform business, government and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative datasets and proprietary private sector data can greatly improve the way we measure, track and describe economic activity. They also can enable novel research designs that allow researchers to trace the consequences of different events or policies. We outline some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics. <s> BIB002
Big Data itself is a challenge for forecasting as a result of its inherent characteristics. Firstly, Big Data evolves and changes in real time, and as such it is important that the techniques used to forecast Big Data are able to transform unstructured data into structured data  , accurately capture these dynamic changes and detect change points in advance. Secondly, there are challenges stemming from Big Data's highly complex structure and as BIB002 point out, it is a challenge to build forecasting models that do not result in poor outof-sample forecasts owing to the 'over use' of potential predictors. Factor modelling which is discussed in the following section is a potential cure for this challenge, but more devoted research is needed to overcome the issue completely. In this section we identify existing applications of statistical and Data Mining techniques for forecasting with Big Data. We have summarized these based on the related field and topic (where relevant) in order to provide the reader with a more rewarding experience. At the outset, it is noteworthy to mention that  , and BIB001 are closely associated with the developments of econometric techniques for analysis and forecasting with Big Data.