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Displaying results 1 to 25 of 137.

  1. Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
    Author: Chernozhukov, Victor; Semenova, Vira
    Published: [2018]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

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    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/189761
    Series: Cemmap working paper ; CWP18, 40
    Subjects: Regressionsanalyse; Künstliche Intelligenz
    Scope: 1 Online-Ressource (circa 43 Seiten), Illustrationen
  2. Plug-in regularized estimation of high-dimensional parameters in nonlinear semiparametric models
    Author: Chernozhukov, Victor; Nekipelov, Denis N.; Semenova, Vira; Syrgkanis, Vasilis
    Published: [2018]
    Publisher:  [Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL], [London]

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    Other identifier:
    hdl: 10419/189763
    Series: Cemmap working paper ; CWP18, 41
    Subjects: Nichtlineare Regression; Nichtparametrisches Verfahren
    Scope: 1 Online-Ressource (circa 51 Seiten), Illustrationen
  3. Generic machine learning inference on heterogenous treatment effects in randomized experiments
    Author: Chernozhukov, Victor; Demirer, Mert; Duflo, Esther; Fernández-Val, Iván
    Published: June 2018
    Publisher:  National Bureau of Economic Research, Cambridge, MA

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    Series: Working paper series / National Bureau of Economic Research ; 24678
    Subjects: Künstliche Intelligenz; Intervallschätzung; Kausalanalyse; Finanzmarkt; Künstliche Intelligenz
    Scope: 38 Seiten, Illustrationen
    Notes:

    Erscheint auch als Online-Ausgabe
  4. Best linear approximations to set identified functions
    with an application to the gender wage gap
    Author: Chandrasekhar, Arun G.; Chernozhukov, Victor; Molinari, Francesca; Schrimpf, Paul
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identifi cation literature by allowing the upper and lower functions de ning the band to carry an index,... more

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    This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identifi cation literature by allowing the upper and lower functions de ning the band to carry an index, and to be unknown but parametrically or non-parametrically estimable functions. The identi fication region of the parameters of the best linear approximation is characterized via its support function, and limit theory is developed for the latter. We prove that the support function can be approximated by a Gaussian process and establish validity of the Bayesian bootstrap for inference. Because the bounds may carry an index, the approach covers many canonical examples in the partial identi fication literature arising in the presence of interval valued outcome and/or regressor data: not only mean regression, but also quantile and distribution regression, including sample selection problems, as well as mean, quantile, and distribution treatment effects. In addition, the framework can account for the availability of instruments. An application is carried out, studying female labor force participation using data from Mulligan and Rubinstein (2008) and insights from Blundell, Gosling, Ichimura, and Meghir (2007). Our results yield robust evidence of a gender wage gap, both in the 1970s and 1990s, at quantiles of the wage distribution up to the 0.4, while allowing for completely unrestricted selection into the labor force. Under the assumption that the median wage offer of the employed is larger than that of individuals that do not work, the evidence of a gender wage gap extends to quantiles up to the 0.7. When the assumption is further strengthened to require stochastic dominance, the evidence of a gender wage gap extends to all quantiles, and there is some evidence at the 0.8 and higher quantiles that the gender wage gap decreased between the 1970s and 1990s.

     
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    Language: English
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    Format: Online
    Other identifier:
    hdl: 10419/211102
    Edition: This version: December 11, 2018
    Series: Cemmap working paper ; CWP19, 09
    Scope: 1 Online-Ressource (circa 62 Seiten), Illustrationen
  5. High-dimensional econometrics and regularized GMM
    Author: Belloni, Alexandre; Chernozhukov, Victor; Chetverikov, Denis; Hansen, Christian Bailey; Kato, Kengo
    Published: [2018]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a number of unknown parameters that is not vanishingly small relative to... more

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    This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a number of unknown parameters that is not vanishingly small relative to the sample size. We first present results in a framework where estimators of parameters of interest may be represented directly as approximate means. Within this context, we review fundamental results including high-dimensional central limit theorems, bootstrap approximation of high-dimensional limit distributions, and moderate deviation theory. We also review key concepts underlying inference when many parameters are of interest such as multiple testing with family-wise error rate or false discovery rate control. We then turn to a general high-dimensional minimum distance framework with a special focus on generalized method of moments problems where we present results for estimation and inference about model parameters. The presented results cover a wide array of econometric applications, and we discuss several leading special cases including high-dimensional linear regression and linear instrumental variables models to illustrate the general results.

     
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    Language: English
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    Format: Online
    Other identifier:
    hdl: 10419/189751
    Series: Cemmap working paper ; CWP18, 35
    Subjects: Ökonometrie; Momentenmethode; IV-Schätzung; Gauß-Prozess; Bootstrap-Verfahren
    Scope: 1 Online-Ressource (circa 105 Seiten), Illustrationen
  6. LASSO-driven inference in time and space
    Author: Chernozhukov, Victor; Härdle, Wolfgang; Huang, Chen; Wang, Weining
    Published: [2018]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of... more

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    We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data. Correspondingly, oracle properties with a jointly selected tuning parameter are derived. We further provide high-quality de-biased simultaneous inference on the many target parameters of the system. We provide bootstrap consistency results of the test procedure, which are based on a general Bahadur representation for the Z-estimators with dependent data. Simulations demonstrate good performance of the proposed inference procedure. Finally, we apply the method to quantify spillover effects of textual sentiment indices in a financial market and to test the connectedness among sectors.

     
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    Format: Online
    Other identifier:
    hdl: 10419/189753
    Series: Cemmap working paper ; CWP18, 36
    Scope: 1 Online-Ressource (circa 63 Seiten), Illustrationen
  7. Inference on causal and structural parameters using many moment inequalities
    Author: Chernozhukov, Victor; Chetverikov, Denis; Kato, Kengo
    Published: [2018]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by p, is possibly much larger than the sample size n. There is a variety of economic applications where solving this problem allows... more

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    This paper considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by p, is possibly much larger than the sample size n. There is a variety of economic applications where solving this problem allows to carry out inference on causal and structural parameters; a notable example is the market structure model of Ciliberto and Tamer (2009) where p = 2m+1 with m being the number of firms that could possibly enter the market. We consider the test statistic given by the maximum of p Studentized (or t-type) inequality-specific statistics, and analyze various ways to compute critical values for the test statistic. Specifically, we consider critical values based upon (i) the union bound combined with a moderate deviation inequality for self-normalized sums, (ii) the multiplier and empirical bootstraps, and (iii) two-step and three-step variants of (i) and (ii) by incorporating the selection of uninformative inequalities that are far from being binding and a novel selection of weakly informative inequalities that are potentially binding but do not provide first order information. We prove validity of these methods, showing that under mild conditions, they lead to tests with the error in size decreasing polynomially in n while allowing for p being much larger than n; indeed p can be of order exp(nc) for some c > 0. Importantly, all these results hold without any restriction on the correlation structure between p Studentized statistics, and also hold uniformly with respect to suitably large classes of underlying distributions. Moreover, in the online supplement, we show validity of a test based on the block multiplier bootstrap in the case of dependent data under some general mixing conditions.

     
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    Format: Online
    Other identifier:
    hdl: 10419/189805
    Edition: This version: October 16, 2018
    Series: Cemmap working paper ; CWP18, 60
    Subjects: Schätztheorie; Diskrete Entscheidung; Bootstrap-Verfahren; Marktstruktur
    Scope: 1 Online-Ressource (circa 83 Seiten)
  8. Network and panel quantile effects via distribution regression
    Author: Chernozhukov, Victor; Fernández-Val, Iván; Weidner, Martin
    Published: [2018]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome... more

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    This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are bias corrected to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confidence bands for the quantile functions and effects have correct joint coverage in large samples. An empirical application to gravity models of trade illustrates the applicability of the methods to network data.

     
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    Format: Online
    Other identifier:
    hdl: 10419/211090
    Series: Cemmap working paper ; CWP18, 70
    Subjects: Nichtlineare Regression; Panel; Fixed-Effects-Modell; Bootstrap-Verfahren; Gravitationsmodell; Außenhandel; Welt
    Scope: 1 Online-Ressource (circa 67 Seiten), Illustrationen
  9. Distribution regression with sample selection, with an application to wage decompositions in the UK
    Author: Chernozhukov, Victor; Fernández-Val, Iván; Luo, Siyi
    Published: [2018]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model that accommodates much rich patterns of heterogeneity in the selection process and effect of... more

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    We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model that accommodates much rich patterns of heterogeneity in the selection process and effect of the covariates. The model applies to continuous, discrete and mixed outcomes. We study the identi fication of the model, and develop a computationally attractive two-step method to estimate the model parameters, where the fi rst step is a probit regression for the selection equation and the second step consists of multiple distribution regressions with selection corrections for the outcome equation. We construct estimators of functionals of interest such as actual and counterfactual distributions of latent and observed outcomes via plug-in rule. We derive functional central limit theorems for all the estimators and show the validity of multiplier bootstrap to carry out functional inference. We apply the methods to wage decompositions in the UK using new data. Here we decompose the difference between the male and female wage distributions into four effects: composition, wage structure, selection structure and selection sorting. We uncover positive sorting for single men and negative sorting for married women that accounts for a substantial fraction of the gender wage gap at the top of the distribution. These fi ndings can be interpreted as evidence of assortative matching in the marriage market and glass-ceiling in the labor market.

     
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    Format: Online
    Other identifier:
    hdl: 10419/189818
    Series: Cemmap working paper ; CWP18, 68
    Scope: 1 Online-Ressource (circa 69 Seiten), Illustrationen
  10. Semiparametric estimation of structural functions in nonseparable triangular models
    Author: Chernozhukov, Victor; Fernández-Val, Iván; Newey, Whitney K.; Stouli, Sami; Vella, Francis
    Published: November 2017
    Publisher:  University of Bristol, Department of Economics, Bristol, United Kingdom

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    Series: Discussion paper / University of Bristol, Department of Economics ; 17, 690 (8 November 2017)
    Scope: 1 Online-Ressource (circa 45 Seiten), Illustrationen
  11. The impact of big data on firm performance
    an empirical investigation
    Author: Bajari, Patrick L.; Chernozhukov, Victor; Hortaçsu, Ali; Suzuki, Junichi
    Published: February 2018
    Publisher:  National Bureau of Economic Research, Cambridge, MA

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    Series: Working paper series / National Bureau of Economic Research ; 24334
    Subjects: Big Data; Unternehmenserfolg; Prognoseverfahren
    Scope: 71 Seiten, Illustrationen
    Notes:

    Erscheint auch als Online-Ausgabe
  12. Censored quantile instrumental variable estimation with Stata
    Author: Chernozhukov, Victor; Fernández-Val, Iván; Han, Sukjin; Kowalski, Amanda E.
    Published: February 2018
    Publisher:  Cowles Foundation for Research in Economics, Yale University, New Haven, Connecticut

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    Format: Online
    Series: Cowles Foundation discussion paper ; no. 2120
    Subjects: Generalisiertes lineares Modell; Schätztheorie; Software
    Scope: 1 Online-Ressource (circa 11 Seiten)
    Notes:

    Die ursprüngliche Zählung der Serie war Nr. 3020, später wurde die Zählung auf Nr. 2120 geändert
  13. Network and panel quantile effects via distribution regression
    Author: Chernozhukov, Victor; Fernández-Val, Iván; Weidner, Martin
    Published: [2018]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome... more

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    This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are bias corrected to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confidence bands for the quantile functions and effects have correct joint coverage in large samples. An empirical application to gravity models of trade illustrates the applicability of the methods to network data.

     
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    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/189723
    Series: Cemmap working paper ; CWP18, 21
    Subjects: Nichtlineare Regression; Panel; Fixed-Effects-Modell; Bootstrap-Verfahren; Gravitationsmodell; Außenhandel; Welt
    Scope: 1 Online-Ressource (circa 66 Seiten), Illustrationen
  14. LASSO-driven inference in time and space
    Author: Chernozhukov, Victor; Härdle, Wolfgang; Huang, Chen; Wang, Weining
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of... more

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    We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable selection purpose, and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data. Correspondingly, oracle properties with a jointly selected tuning parameter are derived. We further provide high-quality de-biased simultaneous inference on the many target parameters of the system. We provide bootstrap consistency results of the test procedure, which are based on a general Bahadur representation for the Z-estimators with dependent data. Simulations demonstrate good performance of the proposed inference procedure. Finally, we apply the method to quantify spillover effects of textual sentiment indices in a financial market and to test the connectedness among sectors.

     
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    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/211114
    Series: Cemmap working paper ; CWP19, 20
    Scope: 1 Online-Ressource (circa 71 Seiten), Illustrationen
  15. Best linear approximations to set identified functions
    with an application to the gender wage gap
    Author: Chandrasekhar, Arun G.; Chernozhukov, Victor; Molinari, Francesca; Schrimpf, Paul
    Published: February 2019
    Publisher:  National Bureau of Economic Research, Cambridge, MA

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    Series: Working paper series / National Bureau of Economic Research ; 25593
    Subjects: Lohnstruktur; Geschlechterdiskriminierung; Schätztheorie
    Scope: 60 Seiten, Illustrationen
    Notes:

    Erscheint auch als Online-Ausgabe
  16. Single market nonparametric identification of multi-attribute hedonic equilibrium models
    Author: Chernozhukov, Victor; Galichon, Alfred; Henry, Marc; Pass, Brendan
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characterized by several dimensions of... more

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    This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characterized by several dimensions of unobserved heterogeneity. With products differentiated along a quality index and agents characterized by scalar unobserved heterogeneity, single crossing conditions on preferences and technology provide identifying restrictions. We develop similar shape restrictions in the multi-attribute case. These shape restrictions, which are based on optimal transport theory and generalized convexity, allow us to identify preferences for goods differentiated along multiple dimensions, from the observation of a single market. We thereby extend identification results in Matzkin (2003) and Heckman, Matzkin, and Nesheim (2010) to accommodate multiple dimensions of unobserved heterogeneity. One of our results is a proof of absolute continuity of the distribution of endogenously traded qualities, which is of independent interest.

     
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    Other identifier:
    hdl: 10419/211119
    Edition: The present version is of August 28, 2018
    Series: Cemmap working paper ; CWP19, 27
    Scope: 1 Online-Ressource (circa 37 Seiten)
  17. Subvector inference in PI models with many moment inequalities
    Author: Belloni, Alexandre; Bugni, Federico A.; Chernozhukov, Victor
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper considers inference for a function of a parameter vector in a partially identified model with many moment inequalities. This framework allows the number of moment conditions to grow with the sample size, possibly at exponential rates. Our... more

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    This paper considers inference for a function of a parameter vector in a partially identified model with many moment inequalities. This framework allows the number of moment conditions to grow with the sample size, possibly at exponential rates. Our main motivating application is subvector inference, i.e., inference on a single component of the partially identified parameter vector associated with a treatment effect or a policy variable of interest. Our inference method compares a MinMax test statistic (minimum over parameters satisfying H0 and maximum over moment inequalities) against critical values that are based on bootstrap approximations or analytical bounds. We show that this method controls asymptotic size uniformly over a large class of data generating processes despite the partially identified many moment inequality setting. The finite sample analysis allows us to obtain explicit rates of convergence on the size control. Our results are based on combining non-asymptotic approximations and new high-dimensional central limit theorems for the MinMax of the components of random matrices, which may be of independent interest. Unlike the previous literature on functional inference in partially identified models, our results do not rely on weak convergence results based on Donsker's class assumptions and, in fact, our test statistic may not even converge in distribution. Our bootstrap approximation requires the choice of a tuning parameter sequence that can avoid the excessive concentration of our test statistic. To this end, we propose an asymptotically valid data-driven method to select this tuning parameter sequence. This method generalizes the selection of tuning parameter sequences to problems outside the Donsker's class assumptions and may also be of independent interest. Our procedures based on self-normalized moderate deviation bounds are relatively more conservative but easier to implement.

     
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    Other identifier:
    hdl: 10419/211120
    Edition: Current Version: July 2, 2018
    Series: Cemmap working paper ; CWP19, 28
    Subjects: Induktive Statistik; Partielle Identifikation; Bootstrap-Verfahren
    Scope: 1 Online-Ressource (circa 76 Seiten)
  18. Uniform inference in high-dimensional gaussian graphical models
    Author: Klaassen, Sven; Kück, Jannis; Spindler, Martin; Chernozhukov, Victor
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    Graphical models have become a very popular tool for representing dependencies within a large set of variables and are key for representing causal structures. We provide results for uniform inference on high-dimensional graphical models with the... more

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    Graphical models have become a very popular tool for representing dependencies within a large set of variables and are key for representing causal structures. We provide results for uniform inference on high-dimensional graphical models with the number of target parameters d being possible much larger than sample size. This is in particular important when certain features or structures of a causal model should be recovered. Our results highlight how in high-dimensional settings graphical models can be estimated and recovered with modern machine learning methods in complex data sets. To construct simultaneous confidence regions on many target parameters, sufficiently fast estimation rates of the nuisance functions are crucial. In this context, we establish uniform estimation rates and sparsity guarantees of the square-root estimator in a random design under approximate sparsity conditions that might be of independent interest for related problems in high-dimensions. We also demonstrate in a comprehensive simulation study that our procedure has good small sample properties.

     
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    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/211122
    Edition: Version November 2018
    Series: Cemmap working paper ; CWP19, 29
    Subjects: Induktive Statistik; Multidimensionale Skalierung; Deskriptive Statistik; Gauß-Prozess
    Scope: 1 Online-Ressource (circa 60 Seiten), Illustrationen
  19. Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
    Author: Bach, Philipp; Chernozhukov, Victor; Spindler, Martin
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to... more

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    Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis of treatment heterogeneities. Also the evaluation of potentially more complicated (non-linear) functional forms of the regression relationship leads to many potential variables for which simultaneous inferential statements might be of interest. Here we provide a review of classical and modern methods for simultaneous inference in (high-dimensional) settings and illustrate their use by a case study using the R package hdm. The R package hdm implements valid joint powerful and efficient hypothesis tests for a potentially large number of coefficients as well as the construction of simultaneous confidence intervals and, therefore, provides useful methods to perform valid post-selection inference based on the LASSO.

     
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    hdl: 10419/211123
    Edition: Version: September 14, 2018
    Series: Cemmap working paper ; CWP19, 30
    Subjects: Induktive Statistik; Multidimensionale Skalierung; Lohnstruktur
    Scope: 1 Online-Ressource (circa 26 Seiten), Illustrationen
  20. Inference for heterogeneous effects using low-rank estimations
    Author: Chernozhukov, Victor; Hansen, Christian Bailey; Liao, Yuan; Zhu, Yinchu
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    We study a panel data model with general heterogeneous effects, where slopes are allowed to be varying across both individuals and times. The key assumption for dimension reduction is that the heterogeneous slopes can be expressed as a factor... more

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    We study a panel data model with general heterogeneous effects, where slopes are allowed to be varying across both individuals and times. The key assumption for dimension reduction is that the heterogeneous slopes can be expressed as a factor structure so that the high-dimensional slope matrix is of low-rank, so can be estimated using low-rank regularized regression. Our paper makes an important theoretical contribution on the "post-SVT (singular value thresholding) inference". Formally, we show that the post-SVT inference can be conducted via three steps: (1) apply the nuclear-norm penalized estimation;(2) extract eigenvectors from the estimated low-rank matrices, and (3) run least squares to iteratively estimate the individual and time effect components in the slope matrix. To properly control for the effect of the penalized low-rank estimation, we argue that this procedure should be embedded with "partial out the mean structure" and "sample splitting". The resulting estimators are asymptotically normal and admit valid inferences. Empirically, we apply the proposed methods to estimate the county-level minimum wage effects on the employment.

     
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    hdl: 10419/211124
    Series: Cemmap working paper ; CWP19, 31
    Scope: 1 Online-Ressource (circa 121 Seiten), Illustrationen
  21. Inference on average treatment effects in aggregate panel data settings
    Author: Chernozhukov, Victor; Wüthrich, Kaspar; Zhu, Yinchu
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper studies inference on treatment effects in aggregate panel data settings with a single treated unit and many control units. We propose new methods for making inference on average treatment effects in settings where both the number of... more

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    This paper studies inference on treatment effects in aggregate panel data settings with a single treated unit and many control units. We propose new methods for making inference on average treatment effects in settings where both the number of pre-treatment and the number of post-treatment periods are large. We use linear models to approximate the counterfactual mean outcomes in the absence of the treatment. The counterfactuals are estimated using constrained Lasso, an essentially tuning free regression approach that nests difference-in-differences and synthetic control as special cases. We propose a K-fold cross-fitting procedure to remove the bias induced by regularization. To avoid the estimation of the long run variance, we construct a self-normalized t-statistic. The test statistic has an asymptotically pivotal distribution (a student t-distribution with K - 1 degrees of freedom), which makes our procedure very easy to implement. Our approach has several theoretical advantages. First, it does not rely on any sparsity assumptions. Second, it is fully robust against misspecification of the linear model. Third, it is more efficient than difference-in-means and difference-in-differences estimators. The proposed method demonstrates an excellent performance in simulation experiments, and is taken to a data application, where we re-evaluate the economic consequences of terrorism.

     
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    Other identifier:
    hdl: 10419/211125
    Series: Cemmap working paper ; CWP19, 32
    Subjects: Panel; Kausalanalyse; Monte-Carlo-Simulation
    Scope: 1 Online-Ressource (circa 29 Seiten), Illustrationen
  22. Mastering panel metrics
    causal impact of democracy on growth
    Author: Chen, Shuowen; Chernozhukov, Victor; Fernández-Val, Iván
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    The relationship between democracy and economic growth is of long standing interest. We revisit the panel data analysis of this relationship by Acemoglu et al. (forthcoming) using state of the art econometric methods. We argue that this and lots of... more

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    The relationship between democracy and economic growth is of long standing interest. We revisit the panel data analysis of this relationship by Acemoglu et al. (forthcoming) using state of the art econometric methods. We argue that this and lots of other panel data settings in economics are in fact high-dimensional, resulting in principal estimators - the fixed effects (FE) and Arellano-Bond (AB) estimators - to be biased to the degree that invalidates statistical inference. We can however remove these biases by using simple analytical and sample-splitting methods, and thereby restore valid statistical inference. We find that the debiased FE and AB estimators produce substantially higher esti-mates of the long-run effect of democracy on growth, providing even stronger support for the key hypothesis in Acemoglu et al. (forthcoming). Given the ubiquitous nature of panel data, we conclude that the use of debiased panel data estimators should substantially improve the quality of empirical inference in economics.

     
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    hdl: 10419/211126
    Series: Cemmap working paper ; CWP19, 33
    Subjects: Demokratie; Wirtschaftswachstum; Panel; Fixed-Effects-Modell
    Scope: 1 Online-Ressource (circa 9 Seiten)
  23. Semi-parametric efficient policy learning with continuous actions
    Author: Demirer, Mert; Syrgkanis, Vasilis; Lewis, Gregory; Chernozhukov, Victor
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    We consider off-policy evaluation and optimization with continuous action spaces. We focus on observational data where the data collection policy is unknown and needs to be estimated. We take a semi-parametric approach where the value function takes... more

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    We consider off-policy evaluation and optimization with continuous action spaces. We focus on observational data where the data collection policy is unknown and needs to be estimated. We take a semi-parametric approach where the value function takes a known parametric form in the treatment, but we are agnostic on how it depends on the observed contexts. We propose a doubly robust off-policy estimate for this setting and show that off-policy optimization based on this estimate is robust to estimation errors of the policy function or the regression model. Our results also apply if the model does not satisfy our semi-parametric form, but rather we measure regret in terms of the best projection of the true value function to this functional space. Our work extends prior approaches of policy optimization from observational data that only considered discrete actions. We provide an experimental evaluation of our method in a synthetic data example motivated by optimal personalized pricing and costly resource allocation.

     
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    hdl: 10419/211127
    Series: Cemmap working paper ; CWP19, 34
    Subjects: Wirkungsanalyse; Allokation; Nichtparametrisches Verfahren
    Scope: 1 Online-Ressource (circa 26 Seiten), Illustrationen
  24. Demand analysis with many prices
    Author: Chernozhukov, Victor; Hausman, Jerry A.; Newey, Whitney K.
    Published: [2019]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    From its inception, demand estimation has faced the problem of "many prices." While some aggregation across goods is always necessary, the problem of many prices remains even after aggregation. Although objects of interest may mostly depend on a few... more

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    From its inception, demand estimation has faced the problem of "many prices." While some aggregation across goods is always necessary, the problem of many prices remains even after aggregation. Although objects of interest may mostly depend on a few prices, many prices should be included to control for omitted variables bias. This paper uses Lasso to mitigate the curse of dimensionality in estimating the average expenditure share from cross-section data. We estimate bounds on consumer surplus (BCS) using a novel double/debiased Lasso method. These bounds allow for general, multidimensional, nonseparable heterogeneity and solve the "zeros problem" of demand by including zeros in the estimation. We also use panel data to allow for prices paid to be correlated with preferences. We average ridge regression individual slope estimators and bias correct for the ridge regularization. We find that panel estimates of price elasticities are much smaller than cross section elasticities in the scanner data we consider. Thus, it is very important to allow correlation of prices and preferences to correctly estimate elasticities. We ?nd less sensitivity of consumer surplus bounds to this correlation.

     
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    hdl: 10419/211152
    Series: Cemmap working paper ; CWP19, 59
    Subjects: Preis; Gesamtwirtschaftliche Nachfrage; Panel; Schätzung; Daten
    Scope: 1 Online-Ressource (circa 39 Seiten)
  25. Network and panel quantile effects via distribution regression
    Author: Chernozhukov, Victor; Fernández-Val, Iván; Weidner, Martin
    Published: [2020]
    Publisher:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome... more

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    This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are debiased to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confidence bands for the quantile functions and effects have correct joint coverage in large samples. An empirical application to gravity models of trade illustrates the applicability of the methods to network data.

     
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    hdl: 10419/241902
    Series: Cemmap working paper ; CWP20, 27
    Subjects: Nichtlineare Regression; Panel; Fixed-Effects-Modell; Bootstrap-Verfahren; Gravitationsmodell; Außenhandel; Welt
    Scope: 1 Online-Ressource (circa 72 Seiten), Illustrationen