Appreciation to Prof. Cheng HSIAO

thomasyuenwaikee, ,

I would like to express my appreciation to Prof Cheng HSIAO (Department of Economics, University of Southern California) for providing a seminar on “Panel Parametric, Semi-parametric and Nonparametric Construction of Counterfactuals” and a workshop on “Important Considerations in Working Panel Dynamic Models” in Hong Kong Shue Yan University on 16 April, 2019. It is extremely informative, and the active participation illustrated the importance of these topics to our colleagues and students. Thanks Prof HSIAO for his valuable and essential contribution to the project “IIDS – Recent Developments in Theoretical and Applied Econometrics Analysis” .

Project Website: https://ecme.hksyu.edu/index.php/category/dynamic-panel-data-analysis/

Seminar : “Panel Parametric, Semi-parametric and Nonparametric Construction of Counterfactuals”

We consider panel parametric, semiparametric and nonparametric methods of constructing counterfactuals. We show through extensive simulations that no method is able to dominate other methods in all circumstances, since the true data‐generating process is typically unknown. We therefore also suggest a model‐averaging method as a robust method to generate counterfactuals.

The advantage of the parametric model approach is that it allows efficient estimation of the unknown parameters; hence, it is possible to identify the impact of each covariate on the outcome. The disadvantage is that, if the model is misspecified, then the resulting inference could be misleading. The advantage of the panel nonparametric approach is that there is no need to consider the conditional mean or the error distribution. The disadvantage is that there is no structural interpretation of the casual effects, only the measurement of the treatment effects. The semiparametric approach is somewhere in between. It assumes that the conditional mean of the observed covariates is specified correctly, but it lets the data control the impact of unobserved factors. Each method has its advantages and disadvantages. Our simulation results show that, if the observed data are stationary, the panel semi-parametric method appears capable of generating counterfactuals close to the (true) data generating process in a wide array of situations. If the data are nonstationary, then the panel nonparametric method appears to dominate the parametric and semiparametric approaches. However, no method appears capable of dominating all other methods under all different data generating processes and different sample configurations of cross-sectional dimension N and pre-treatment time dimension T0: Since the true data generating process is usually unknown and the statistical findings could be very different for different situations, we have also suggested a model averaging method as a robust method for generating counterfactuals.

Resources:

PPT

Workshop : “Important Considerations in Working Panel Dynamic Models”

“All interesting economic behavior is inherently dynamic, dynamic panel models are the only relevant models; what might superficially appear to be a static model only conceals underlying dynamics, since any state variables presumed to influence present behavior is likely to depend in some way on past behavior.” M. Nerlove (2002, p.46)

Three major issues arise in the analysis of linear dynamic panel data models: (i) initial value distribution; (ii) controlling the impact of incidental parameters to obtain valid inference on structural parameters; and (iii) relative sample size between cross-sectional dimension N and time series dimension T.  For nonlinear  dynamic models, these issues become even more difficult to handle.  The derivation of initial value distribution is much more difficult to handle (e.g.  binary outcomes model). Nor is there any linear transformation to remove the incidental parameters. The identification for the method of moments estimator becomes much harder to derive (e.g. Honore (1993)).

Resources:

PPT

Photo Gallery with Prof. Cheng HSIAO

The IIDS project is fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project no. UGC/IIDS15/B02/18)

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