On the Determinants of Distribution Dynamics

n this paper we propose a novel approach to identify the impact of growth determinants on the distribution dynamics of productivit y. Our approach integrates counterfactual analysis with the estima tion of stochastic kernels. The counterfactuals are constructed from a semi-parametric growth regression, in which the cross-section heterogeneity in the growth determinants is removed. The methodology also allows us to test for potential distributional effects in the residuals. We illustrate the usefulness of the proposed methodology by an application to a cross-section of countries, which highlights the significant impact on inequality and polarization in the world productivity distribution of growth determinants from an augmented Solow model

On the Determinants of Distribution Dynamics

n this paper we propose a novel approach to identify the impact of growth determinants on the distribution dynamics of productivit y. Our approach integrates counterfactual analysis with the estima tion of stochastic kernels. The counterfactuals are constructed from a semi-parametric growth regression, in which the cross-section heterogeneity in the growth determinants is removed. The methodology also allows us to test for potential distributional effects in the residuals. We illustrate the usefulness of the proposed methodology by an application to a cross-section of countries, which highlights the significant impact on inequality and polarization in the world productivity distribution of growth determinants from an augmented Solow model

Econometric analysis of volatile art markets

A new heteroskedastic hedonic regression model is suggested which takes into account time-varying volatility and is applied to a blue chips art market. A nonparametric local likelihood estimator is proposed, and this is more precise than the often used dummy variables method. The empirical analysis reveals that errors are considerably non-Gaussian, and that a student distribution with time-varying scale and degrees of freedom does well in explaining deviations of prices from their expectation. The art price index is a smooth function of time and has a variability that is comparable to the volatility of stock indices.Volatility, art markets, hedonic regression, semiparametric estimation

Large Vector Auto Regressions

One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their) lags are relevant, especially when there is a mixture of serial correlation (temporal dynamics), high dimensional (spatial) dependence structure and moderate sample size (relative to dimensionality and lags). To this end, an integrated solution that addresses these three challenges simultaneously is appealing. We study the large vector auto regressions here with three types of estimates. We treat each variable's own lags different from other variables' lags, distinguish various lags over time, and is able to select the variables and lags simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal dependence. In contrast, our proposed method can still produce an estimate as efficient as an oracle under such scenarios. The tuning parameters are chosen via a data driven "rolling scheme" method to optimize the forecasting performance. A macroeconomic and financial forecasting problem is considered to illustrate its superiority over existing estimators.Time Series, Vector Auto Regression, Regularization, Lasso, Group Lasso, Oracle estimator

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers

Unconditional Quantile Treatment Effects under Endogeneity

This paper develops IV estimators for unconditional quantile treatment effects (QTE) when the treatment selection is endogenous. In contrast to conditional QTE, i.e. the effects conditional on a large number of covariates X, the unconditional QTE summarize the effects of a treatment for the entire population. They are usually of most interest in policy evaluations because the results can easily be conveyed and summarized. Last but not least, unconditional QTE can be estimated at √n rate without any parametric assumption, which is obviously impossible for conditional QTE (unless all X are discrete). In this paper we extend the identification of unconditional QTE to endogenous treatments. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. Several types of estimators are proposed: regression, propensity score and weighting estimators. Root n consistency, asymptotic normality and attainment of the semiparametric efficiency bound are shown for our weighting estimator, which is extremely simple to implement. We also show that including covariates in the estimation is not only necessary for consistency when the instrumental variable is itself confounded but also for efficiency when the instrument is valid unconditionally. Monte Carlo simulations and two empirical applications illustrate the use of the proposed estimators.instrumental variables, quantile treatment effects, nonparametric regression

Monitoring, Information Technology and the Labor Share

This paper assesses empirically the hypotheses by Bental and Demougin (2010) that innovations in ICT (Information and Communication Technology) reduce the labor share in OECD countries by improving the monitoring technology. In a first step, I show that data trends for the labor share, wages in efficiency units, and labor in efficiency units over capital can be matched by a simulation of the model of Bental and Demougin (2010). In a second approach, I confirm increasing monitoring of workers using micro data for Germany. I argue that ICT influences labor not only through substitutability of labor with ICT and foreign work, but also through to lowering rents of workers as monitoring technology improves.Labor Shares, Bargaining, Monitoring

Asymptotically Efficient Estimation of Weighted Average Derivatives with an Interval Censored Variable

This paper studies the identification and estimation of weighted average derivatives of conditional location functionals including conditional mean and conditional quantiles in settings where either the outcome variable or a regressor is interval-valued. Building on Manski and Tamer (2002) who study nonparametric bounds for mean regression with interval data, we characterize the identified set of weighted average derivatives of regression functions. Since the weighted average derivatives do not rely on parametric specifications for the regression functions, the identified set is well-defined without any parametric assumptions. Under general conditions, the identified set is compact and convex and hence admits characterization by its support function. Using this characterization, we derive the semiparametric efficiency bound of the support function when the outcome variable is interval-valued. We illustrate efficient estimation by constructing an efficient estimator of the support function for the case of mean regression with an interval censored outcome