DYNAMIC FACTOR MODELS

By James H. Stock, Mark W. Watson, Michael P. Clements, David F. Henry. 2018

539 citations recorded by the source corpus.

Macroeconometricians face a peculiar data structure. On the one hand, the number of years for which there is reliable and relevant data is limited and cannot readily be increased other than by the passage of time. On the other hand, for much of the postwar period statistical agencies have collected monthly or quarterly data on a great many related macroeconomic, financial, and sectoral variables. Thus macroeconometricians face data sets that have hundreds or even thousands of series, but the number of observations on each series is relatively short, for example 20 to 40 years of quarterly data. This chapter surveys work on a class of models, dynamic factor models (DFMs), which 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 number of time series observations. Dynamic factor models were originally proposed by Geweke (1977) as a time-series extension of factor models previously developed for cross-sectional data. In early influential work, Sargent and Sims (1977) showed that two dynamic factors could explain a large fraction of the variance of important U.S. quarterly macroeconomic variables, including output, employment, and prices. This central empirical finding that a few factors can explain a large fraction of the variance of many macroeconomic series has been confirmed by many studies; see for example Giannone, The aim of this survey is to describe, at a level that is specific enough to be useful to researchers new to the area, the key theoretical results, applications, and empirical findings in the recent literature on DFMs. Bai and Ng (2008) and Stock and Watson 2 (2006) provide complementary surveys of this literature. Bai and Ng's (2008) survey is more technical than this one and focuses on the econometric theory and conditions; Stock and Watson (2006) focus on DFM-based forecasts in the context of other methods for forecasting with many predictors. The premise of a dynamic factor model is that a few latent dynamic factors, f t , drive the comovements of a high-dimensional vector of time-series variables, X t , which is also affected by a vector of mean-zero idiosyncratic disturbances, e t. These idiosyncratic disturbances arise from measurement error and from special features that are specific to an individual series (the effect of a Salmonella scare on restaurant employment, for example). The latent factors follow a time series process, which is …

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Deep Factors for Forecasting (2019, background): “Dynamic factor models (DFMs) have been studied in econo-metrics for decades to model the co-evolvement of multiple time series (Geweke, 1977; Stock & Watson, 2011; Forni et al., 2000; Pan & Yao, 2008).”
Sufficient Forecasting Using Factor Models (2015, methodology): “For example, Forni et al. (2000, 2015) studied factor estimation based on projection, Liu et al. (2012), Xue and Zou (2012) and Fan et al. (2015b) employed rank-based methods for robust estimation of the covariance matrices, and Connor et al. (2012) applied a weighted additive nonparametric…”
LOCALLY STATIONARY FACTOR MODELS: IDENTIFICATION AND NONPARAMETRIC ESTIMATION (2011, methodology): “For example, the dynamic factor model of Forni et al. (2000) has been 1304 GIOVANNI MOTTA ET AL. applied to forecasting, monetary policy analysis business cycle analysis, and construction of economic indicators; see Breitung and Eickmeier (2006) for a recent review.”
Employment, Wages, and the Economic Cycle: Differences between Immigrants and Natives (2009, unknown): “Such a strict factor model in turn is a special case of a dynamic factor model as set out, for instance, in Forni et al. (2000), in which there is an additional set of lagged common factors (see also Breitung and Eickmeier, 2006; Lütkepohl, 2006, Ch. 18.2).”
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, background): “Stock and Watson (2011) provide an overview of the econometric methods with a focus on applications.”
Enhancing Interpretability in Factor Analysis by Means of Mathematical Optimization (2019, methodology): “Obtaining the globally optimal solution to problem (InRot) is challenging due to its nonlinearities and non-convex constraints, Wen and Yin (2013). Nevertheless, even locally optimal solutions to this problem might outperform the traditional rotation techniques in EFA, e.”
Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version (2013, methodology): “The dynamic factor model is reviewed in Stock and Watson (2011).”
Granger Causality: A Review and Recent Advances (2021, methodology): “Factor models have been used extensively in economet-rics (Stock and Watson, 2011).”
Oracle Inequalities for High Dimensional Vector Autoregressions (2012, methodology): “For surveys on factor analysis in the context of time series see Stock and Watson (2006), Bai and Ng (2008) and Stock and Watson (2011).”