Forecasting with factor-augmented error correction models

TL;DR: In this article, the authors present a survey of dynamic factor models (DFMs), a class of models that has received considerable attention in the past decade because of their ability to model simultaneously and consistently data sets in which the number of series exceeds the total number of time series observations.

TL;DR: In this article, the extent to which wind energy can replace fossil capacities based on wind injection and demand data for 2006 through June 2008 was analyzed and the potential savings due to wind energy was also assessed.

TL;DR: The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data.

Abstract: This paper deals with the dimension reduction of high-dimensional time series based on common factors. In particular we allow the dimension of time series p to be as large as, or even larger than, the sample size n. The estimation of the factor loading matrix and the factor process itself is carried out via an eigenanalysis of a p £ p non-negative de¯nite matrix. We show that when all the factors are strong in the sense that the norm of each column in the factor loading matrix is of the order p1=2, the estimator of the factor loading matrix is weakly consistent in L2-norm with the convergence rate independent of p. This result exhibits clearly that the `curse' is canceled out by the `blessing' of dimensionality. We also establish the asymptotic properties of the estimation when factors are not strong. The proposed method together with their asymptotic properties are further illustrated in a simulation study. An application to an implied volatility data set, together with a trading strategy derived from the ¯tted factor model, is also reported.

TL;DR: In this paper, the authors consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors, and derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimensions.

TL;DR: The authors compared the performance of various structural and time series exchange rate models, and found that a random walk model performs as well as any estimated model at one to twelve month horizons for the dollar/pound, dollar/mark, dollar /yen and trade-weighted dollar exchange rates.

TL;DR: In this article, the convergence rate for the factor estimates that will allow for consistent estimation of the number of factors is established, and some panel criteria are proposed to obtain the convergence rates.