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  1. Factor models with local factors - determining the number of relevant factors
    Autor*in: Freyaldenhoven, Simon
    Erschienen: [2021]
    Verlag:  Research Department, Federal Reserve Bank of Philadelphia, Philadelphia, PA

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    VS 438
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    Sprache: Englisch
    Medientyp: Buch (Monographie)
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    Schriftenreihe: Working papers / Research Department, Federal Reserve Bank of Philadelphia ; 21, 15 (April 2021)
    Schlagworte: high-dimensional data; factor models; weak factors; local factors; sparsity
    Umfang: 1 Online-Ressource (circa 39 Seiten), Illustrationen
  2. Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations
    Autor*in: Jiang, Liang; Phillips, Peter C. B.; Tao, Yubo (Robert); Zhang, Yichong
    Erschienen: 2021
    Verlag:  Cowles Foundation for Research in Economics, Yale University, New Haven, Connecticut

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    Schriftenreihe: Cowles Foundation discussion paper ; no. 2288 (May 2021)
    Schlagworte: Covariate-adaptive randomization; high-dimensional data; regression adjustment; quantile treatment effects
    Umfang: 1 Online-Ressource (circa 44 Seiten), Illustrationen
  3. Spiked eigenvalues of high-dimensional separable sample covariance matrices
    Autor*in: Zhang, Bo; Gao, Jiti; Pan, Guangming; Yang, Yanrong
    Erschienen: December 2019
    Verlag:  Monash University, Department of Econometrics and Business Statistics, [Victoria, Australia]

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    Schriftenreihe: Working paper / Monash University, Department of Econometrics and Business Statistics ; 19, 31
    Schlagworte: Factor model; high-dimensional data; principal component analysis; spiked empirical eigenvalue
    Umfang: 1 Online-Ressource (circa 41 Seiten), Illustrationen
  4. A generalized factor model with local factors
    Autor*in: Freyaldenhoven, Simon
    Erschienen: April 2019
    Verlag:  Research Department, Federal Reserve Bank of Philadelphia, Philadelphia, PA

    I extend the theory on factor models by incorporating local factors into the model. Local factors only affect an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in... mehr

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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    I extend the theory on factor models by incorporating local factors into the model. Local factors only affect an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. I derive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. I then introduce a new class of estimators to determine the number of those relevant factors. Unlike existing estimators, my estimators use not only the eigenvalues of the covariance matrix, but also its eigenvectors. I find strong evidence of local factors in a large panel of US macroeconomic indicators

     
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    Schriftenreihe: Working paper / Research Department, Federal Reserve Bank of Philadelphia ; 19, 23 (April 2019)
    FRB of Philadelphia Working Paper ; No. 19-23
    Schlagworte: high-dimensional data; factor models; weak factors; local factors; sparsity
    Umfang: 1 Online-Ressource (circa 47 Seiten), Illustrationen
  5. High-dimensional forecasting with known knowns and known unknowns
    Autor*in: Pesaran, M. Hashem; Smith, Ron
    Erschienen: January 2024
    Verlag:  CESifo, Munich, Germany

    Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known... mehr

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    Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.

     
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    Weitere Identifier:
    hdl: 10419/296020
    Schriftenreihe: CESifo working papers ; 10931 (2024)
    Schlagworte: forecasting; high-dimensional data; Lasso; OCMT; latent factors; principal components
    Umfang: 1 Online-Ressource (circa 65 Seiten), Illustrationen
  6. The analysis of big data on cites and regions
    some computational and statistical challenges
    Autor*in: Schintler, Laurie A.; Fischer, Manfred M.
    Erschienen: 2018-10-28
    Verlag:  WU Vienna University of Economics and Business, Vienna

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Auflage/Ausgabe: Version: 2018-10-28
    Schriftenreihe: Working papers in regional science ; 2018, 08
    Schlagworte: massive sample size; high-dimensional data; heterogeneity and incompleteness; data storage; scalability; parallel data processing; visualization; statistical methods
    Umfang: 1 Online-Ressource (circa 16 Seiten)
  7. The de-biased group Lasso estimation for varying coefficient models
    Autor*in: Honda, Toshio
    Erschienen: November 2018
    Verlag:  Graduate School of Economics, Hitotsubashi University, Tokyo

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Sprache: Englisch
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    Format: Online
    Weitere Identifier:
    hdl: 10086/29663
    Schriftenreihe: Discussion paper series / Graduate School of Economics, Hitotsubashi University ; no. 2018, 04
    Schlagworte: high-dimensional data; B-spline; varying coefficient models; group Lasso; bias correction
    Umfang: 1 Online-Ressource (circa 38 Seiten)
  8. Program evaluation and causal inference with high-dimensional data
    Autor*in: Belloni, Alexandre; Chernozhukov, Victor; Fernández-Val, Iván; Hansen, Christian Bailey
    Erschienen: March 18, 2016
    Verlag:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, London

    In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control... mehr

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function-valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment condition framework, which arises from structural equation models in econometrics. Here too the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function-valued) parameters within this general framework where any high-quality, modern machine learning methods can be used to learn the nonparametric/high-dimensional components of the model. These include a number of supporting auxilliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity-based estimation of regression functions for function-valued outcomes.

     
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    Weitere Identifier:
    hdl: 10419/130100
    Schriftenreihe: Cemmap working paper ; CWP 16/13
    Schlagworte: Kausalanalyse; Künstliche Intelligenz; high-dimensional data
    Umfang: 1 Online-Ressource (circa 119 Seiten), Illustrationen
  9. Estimation of large volatility matrices with low-rank signal plus sparse noise structures
    Autor*in: Dai, Runyu; Matsuda, Yasumasa
    Erschienen: [2023]
    Verlag:  Center for Data Science and Service Research, Graduate School of Economic and Management, Tohoku University, Sendai, Japan

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    Weitere Identifier:
    hdl: 10097/00137340
    Schriftenreihe: Data science and service research discussion paper ; no. 135
    Schlagworte: Volatility matrix; multivariate GARCH; factor models; thresholding; high-dimensional data; ePOET
    Umfang: 1 Online-Ressource (circa 24 Seiten)
  10. High-dimensional forecasting with known knowns and known unknowns
    Autor*in: Pesaran, M. Hashem; Smith, Ron
    Erschienen: [2024]
    Verlag:  Centre for Econometric Analysis, Bayes Business School, London

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    Schriftenreihe: CEA@Bayes working paper series ; WP-CEA-2024, 01
    Schlagworte: Forecasting; high-dimensional data; Lasso; OCMT; latent factors; principal components
    Umfang: 1 Online-Ressource (circa 66 Seiten), Illustrationen