Inference in Group Factor Models With an Application to Mixed‐Frequency Data

Andreou, E.; Gagliardini, P.; Ghysels, E.; Rubin, M.

doi:10.3982/ecta14690

Cited by 57 publications

(20 citation statements)

References 41 publications

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“…Furthermore, Foroni, Marcellino and Stevanovic (2018) show analytically, in Monte Carlo simulations, the relevance of considering the moving average (MA) component in MIDAS and U-MIDAS models thus closing the gap in the respective literature. Andreou et al (2019), on the other hand show how the group factor context applies to mixed-frequency data panels.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia

Ramadani¹,

Petrovska²,

Bucevska

2021

South East European Journal of Economics and Business

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Aggregate demand forecasting, also known as nowcasting when it applies to current quarter assessment, is of notable interest to policy makers. This paper concentrates on the empirical methods dealing with mixed-frequency data. In particular, it focuses on the MIDAS approach and its later extension, the Bayesian MFVAR. The two strategies are evaluated in terms of their accuracy to nowcast Macedonian GDP growth, using same monthly frequency data set. The results of this study indicate that the MIDAS regressions demonstrate comparable forecasting performance to that of MF-VAR model. Moreover, it is interesting to note that the two approaches are reciprocal, since in general, their combined forecast demonstrates clear superiority in predicting business cycle turning points. Additionally, the MF-VAR model showed higher precision in times of increased uncertainty.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia

Ramadani¹,

Petrovska²,

Bucevska

2021

South East European Journal of Economics and Business

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“…Andreou et al . (2019) derive asymptotics to identify common and group‐specific factors in such a setting. Specifically, they introduce a test to assess which factors are common across a set of group‐specific vectors.…”

Section: Modelingmentioning

confidence: 99%

“…Similarly, grouped data settings can be used to extract common sentiment in groups of time series -for example, a common factor for every industry group consisting of all firms' sentiment measures. Andreou et al (2019) derive asymptotics to identify common and group-specific factors in such a setting. Specifically, they introduce a test to assess which factors are common across a set of group-specific vectors.…”

Section: Time Series Modelsmentioning

confidence: 99%

Econometrics Meets Sentiment: An Overview of Methodology and Applications

Algaba

Ardia

Bluteau³

et al. 2020

Journal of Economic Surveys

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The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software.

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“…economic sectors, asset classes, markets or countries. Andreou et al (2019) develop inference procedures in a "large n, large T " setting for estimating the common and group-specific numbers of factors and the corresponding spanned factor spaces.…”

Section: Inference In Models With Unobservable Factorsmentioning

confidence: 99%

Estimation of Large Dimensional Conditional Factor Models in Finance

2019

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This chapter provides an econometric methodology for inference in large-dimensional conditional factor models in finance. Changes in the business cycle and asset characteristics induce time variation in factor loadings and risk premia to be accounted for. The growing trend in the use of disaggregated data for individual securities motivates our focus on methodologies for a large number of assets. The beginning of the chapter outlines the concept of approximate factor structure in the presence of conditional information, and develops an arbitrage pricing theory for large-dimensional factor models in this framework. Then we distinguish between two different cases for inference depending on whether factors are observable or not. We focus on diagnosing model specification, estimating conditional risk premia, and testing asset pricing restrictions under increasing cross-sectional and time series dimensions. At the end of the chapter, we review some of the empirical findings and contrast analysis based on individual stocks and standard sets of portfolios. We also discuss the impact on computing time-varying cost of equity for a firm, and summarize differences between results for developed and emerging markets in an international setting.

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Inference in Group Factor Models With an Application to Mixed‐Frequency Data

Cited by 57 publications

References 41 publications

Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia

Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia

Econometrics Meets Sentiment: An Overview of Methodology and Applications

Estimation of Large Dimensional Conditional Factor Models in Finance

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