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Generalized propensity scores for multiple continuous treatment variables

Egger, Peter H.; von Ehrlich, Maximilian - 2013

This paper illustrates that the generalized propensity score method can easily be applied with multiple continuous endogenous treatment variables. Consistency proofs carry over straightforwardly to this general case, and the approach is shown to work well in finite samples with various...

Persistent link: https://www.econbiz.de/10010291531

Jointness of growth determinants

Doppelhofer, Gernot; Weeks, Melvyn - 2007

This paper introduces a new measure of dependence or jointness among explanatory variables. Jointness is based on the joint posterior distribution of variables over the model space, thereby taking model uncertainty into account. By looking beyond marginal measures of variable importance,...

Persistent link: https://www.econbiz.de/10010264116

Panel regression with random noise

Ronning, Gerd; Schneeweiss, Hans - 2009

The paper explores the effect of measurement errors on the estimation of a linear panel data model. The conventional fixed effects estimator, which ignores measurement errors, is biased. By correcting for the bias one can construct consistent and asymptotically normal estimators. In addition, we...

Persistent link: https://www.econbiz.de/10010264605

Default predictors and credit scoring models for retail banking

Kocenda, Evézen; Vojtek, Martin - 2009

logistic regression (parametric) and one model using Classification and Regression Trees (CART, nonparametric). The models are …

Persistent link: https://www.econbiz.de/10010265990

Nonparametric regression and the detection of turning points in the Ifo business climate

Abberger, Klaus - 2004

in the data can be calculated with local polynomial regression. A change in the sign of the slope can be interpreted as a …

Persistent link: https://www.econbiz.de/10010261265

Harding, Matthew - 2018

This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and … Pesaran (2015) and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under … new quantile regression estimator. Monte Carlo studies are carried out to study the small sample behavior of the proposed …

Persistent link: https://www.econbiz.de/10012908711

Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks

Chudik, Alexander - 2020

This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies...

Persistent link: https://www.econbiz.de/10012825993

Identification and Estimation of Categorical Random Coefficient Models

Gao, Zhan; Pesaran, M. Hashem - 2022

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of...

Persistent link: https://www.econbiz.de/10013291320

Variable Selection in High Dimensional Linear Regressions with Parameter Instability

Chudik, Alexander; Pesaran, M. Hashem; Sharifvaghefi, Mahrad - 2023

This paper is concerned with the problem of variable selection when the marginal effects of signals on the target variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to any particular model of parameter instabilities. It poses...

Persistent link: https://www.econbiz.de/10014262743

Nonparametric Identification of Production Function, Total Factor Productivity, and Markup from Revenue Data

Kasahara, Hiroyuki; Sugita, Yoichi - 2021

Commonly used methods of production function estimation assume that a firm’s output quantity can be observed as data, but typical datasets contain only revenue, not output quantity. We examine the nonparametric identification of production function from revenue data when a firm faces a general...

Persistent link: https://www.econbiz.de/10013315013

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