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Demystifying Causal Inference : Public Policy Applications with R

Dayal, Vikram; Murugesan, Anand - 2023

-- Experiments -- Matching -- Instrumental Variables -- Regression Discontinuity Design -- Panel Data and fixed effects -- Difference … packages for speciﬁc causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book …, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta …

Persistent link: https://www.econbiz.de/10014375030

An analysis of subjective views of job insecurity

Green, Francis; Dickerson, Andrew P.; Carruth, Alan A.; … - 2001 - [Elektronische Ressource]

In the 1997 and 1998 waves of the British Household Panel Survey, workers are asked to assess their level of job security in terms of the probability of becoming unemployed within the next year. We examine whether these perceptions of insecurity are purely subjective or are systematically...

Persistent link: https://www.econbiz.de/10001646566

GMM Estimation of Fixed Effects Dynamic Panel Data Models with Spatial Lag and Spatial Errors

Čížek, Pavel; Jacobs, Jan; Ligthart, Jenny E.; … - 2015

The three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian and Prucha (2007), which corrects for spatially correlated errors in static panel data models, is extended by introducing fixed effects, a spatial lag, and a one-period lag of the dependent variable as additional...

Persistent link: https://www.econbiz.de/10014175015

Spatial Diffusion of Innovation : A Spatial Panel Analysis of Electronic Toll Collecting Transponders in Japan

Hamaoka, Yutaka - 2012

The spatial panel model is applied to a new data set: monthly data of the number of ETC (Electronic Toll Collecting) transponders newly installed in 47 Japanese prefectures. The model incorporates marketing variables and highway-related variables. Regarding the spatial panel model, this work...

Persistent link: https://www.econbiz.de/10014044708

Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?

Girardin, Eric; Kholodilin, Konstantin - 2009

In this paper, we make multi-step forecasts of the annual growth rates of the real GRP for each of the 31 Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the...

Persistent link: https://www.econbiz.de/10014046018

A Space-Time Filter for Panel Data Models Containing Random Effects

Parent, Olivier; Lesage, James P. - 2009

A space-time filter structure is introduced that can be used to accommodate dependence across space and time in the error components of panel data models that contain random effects. This general specification encompasses several more specific space-time structures that have been used recently...

Persistent link: https://www.econbiz.de/10014046455

Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors

Jacobs, Jan; Ligthart, Jenny E.; Vrijburg, Hendrik - 2009

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory...

Persistent link: https://www.econbiz.de/10014200234

A Spatial Dynamic Panel Model with Random Effects Applied to Commuting Times

Parent, Olivier; Lesage, James P. - 2013

A space–time filter is set forth for spatial panel data situations that include random effects. We propose a general spatial dynamic specification that encompasses several spatiotemporal models previously used in the panel data literature. We apply the model to the case of highway induced...

Persistent link: https://www.econbiz.de/10014156327

Estimation of Average Treatment Effects Using Panel Data when Treatment Effect Heterogeneity Depends on Unobserved Fixed Effects

Sakaguchi, Shosei - 2019

This paper proposes a new panel data approach to identify and estimate the time-varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches...

Persistent link: https://www.econbiz.de/10014104396

Expend, a Gauss Programme for Non-Linear GMM Estimation of Exponential Models with Endogenous Regressors for Cross Section and Panel Data

Windmeijer, Frank - 2002

ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM for cross section data using moment conditions...

Persistent link: https://www.econbiz.de/10014105787