Asymptotics of the principal components estimator of large factor models with weakly influential factors

Onatski, Alexei

doi:10.1016/j.jeconom.2012.01.034

Cited by 286 publications

(212 citation statements)

References 26 publications

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“…Factor models have also been used extensively in finance and econometrics when the number of economic variables is high (Bai, 2003;Ng, 2002, 2007). RMT has been used to explain the bias of arbitrage pricing models in finite samples (Harding, 2008;Onatski, 2012) and in determining the number of significant factors (Onatski, 2009(Onatski, , 2010.…”

Section: Applications To Financementioning

confidence: 99%

Random matrix theory in statistics: A review

Paul

Aue

2014

Journal of Statistical Planning and Inference

165

115

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b s t r a c tWe give an overview of random matrix theory (RMT) with the objective of highlighting the results and concepts that have a growing impact in the formulation and inference of statistical models and methodologies. This paper focuses on a number of application areas especially within the field of high-dimensional statistics and describes how the development of the theory and practice in high-dimensional statistical inference has been influenced by the corresponding developments in the field of RMT.

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Section: Applications To Financementioning

confidence: 99%

Random matrix theory in statistics: A review

Paul

Aue

2014

Journal of Statistical Planning and Inference

165

115

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“…7 See Onatski (2010;2012) and Chudik, Pesaran and Tosetti (2011) for a discussion of "strong" vs. "weak" factors in factor models. perturbation theory of linear operators to derive the approximation.…”

Section: Introductionmentioning

confidence: 99%

Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects

2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractIn this paper we study the least squares (LS) estimator in a linear panel regression model with unknown number of factors appearing as interactive fixed effects. Assuming that the number of factors used in estimation is larger than the true number of factors in the data we establish the limiting distribution of the LS estimator for the regression coefficients, as the number of time periods and the number of crosssectional units jointly go to infinity. The main result of the paper is that under certain assumptions the limiting distribution of the LS estimator is independent of the number of factors used in the estimation, as long as this number is not underestimated. The important practical implication of this result is that for inference on the regression coefficients one does not necessarily need to estimate the number of interactive fixed effects consistently.

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“…As noted by Onatski (2012), macroeconomic panels may suffer from a weak factor structure. In fact, macroeconomic aggregates and sectoral data are strongly The factor analysis has been applied in finance to characterize the determinants of a large set of returns.…”

Section: Number Of Factors In a Large Panel Of Financial Variablesmentioning

confidence: 99%

“…In particular, we follow Boivin and Ng (2005), Onatski (2012), and Dufour and Stevanovic (2013). Assuming that…”

Section: Monte Carlo Simulation Exercise I: Time-varying Factor Loadingsmentioning

confidence: 99%

Selection of the Number of Factors in Presence of Structural Instability: A Monte Carlo study

Takongmo

Stevanović

2016

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In this paper we study the selection of the number of primitive shocks in exact and approximate factor models in the presence of structural instability. The empirical analysis shows that the estimated number of factors varies substantially across several selection methods and over the last 30 years in standard large macroeconomic and financial panels. Using Monte Carlo simulations, we suggest that the structural instability, expressed as time-varying factor loadings, can alter the estimation of the number of factors and therefore provides an explanation for the empirical findings

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Asymptotics of the principal components estimator of large factor models with weakly influential factors

Cited by 286 publications

References 26 publications

Random matrix theory in statistics: A review

Random matrix theory in statistics: A review

Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects

Selection of the Number of Factors in Presence of Structural Instability: A Monte Carlo study

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