Maximum Likelihood Estimation of Factor Models on Data Sets with Arbitrary Pattern of Missing Data

By Marta Bańbura, M. Modugno. Social Science Research Network, 2010

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SUMMARY In this paper we modify the expectation maximization algorithm in order to estimate the parameters of the dynamic factor model on a dataset with an arbitrary pattern of missing data. We also extend the model to the case with a serially correlated idiosyncratic component. The framework allows us to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant, for example, for young economies for which many indicators have been compiled only recently. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting of the euro area gross domestic product. Copyright © 2012 John Wiley & Sons, Ltd.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9f65c3a2da344c9e1136745bdbdae2a3d18cd5de

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Citations in the corpus

Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016, positive): “We then turn to a different concept of news, introduced in the literature by Banbura and Modugno (2014). We show how our set up adds a new dimension to their tool, as one can use draws from the posterior to derive a measure of uncertainty around the news content of each data (or block of data) relea”
A Shadow Policy Rate to Calibrate US Monetary Policy at the Zero Lower Bound (2014, neutral): “To overcome this problem, Bańbura and Modugno (2014) propose the use of the generalized expectation maximization (EM) algorithm of Dempster, Laird, and Rubin (1977).14 13Note that this assumption corresponds to an exact factor model.”
Delphic and odyssean monetary policy shocks: Evidence from the euro area (2018, background): “(19)The selection of variables is pretty standard for the euro area and mimics the choices in Banbura and Modugno (2014). More details on selection and transformation of variables are reported in the appendix; see table 13.”
Tracking the Slowdown in Long-Run GDP Growth (2017, methodology): “Banbura and Modugno (2014) suggest including all of the idiosyncratic components as additional elements of the state vector.”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “Banbura and Modugno (2010) extend the analysis in Banbura and Rünstler (2011) by augmenting the dataset by short history indicators and quarterly series.”
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data (2019, methodology): “Banbura and Modugno (2014), Jungbacker and Koopman (2011), Jungbacker, Koopman, and van der Wel (2011) considered likelihood estimation which is conceptually appealing but non-linear filters are needed to compute the likelihood as Ft and i are both random.”
Macroeconomic Uncertainty Through the Lens of Professional Forecasters (2019, neutral): “See Stock and Watson (2002) and Bańbura and Modugno (2014) for the estimation of a dynamic factor models with missing observations via the EM algorithm.”