# thres

**Repository Path**: arlionn/thres

## Basic Information

- **Project Name**: thres
- **Description**: Lin ao group, wild Bootstrap
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2022-10-19
- **Last Updated**: 2022-11-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# thres

## 蒙特卡洛模拟

### 公司面板数据生成相关

#### Estimating dynamic panel models in corporate finance
Mark J. Flannery, Kristine Watson Hankins, Journal of Corporate Finance,
Volume 19,
2013,
Pages 1-19,
ISSN 0929-1199,
https://doi.org/10.1016/j.jcorpfin.2012.09.004.
(https://www.sciencedirect.com/science/article/pii/S092911991200096X)

Abstract: Dynamic panel models play a natural role in several important areas of corporate finance, but the combination of fixed effects and lagged dependent variables introduces serious econometric bias. Several methods of counteracting these biases are available and these methodologies have been tested on small datasets with independent, normally-distributed explanatory variables. However, no one has evaluated the methods' performance with corporate finance data, in which the dependent variable may be clustered or censored and independent variables may be missing, correlated with one another, or endogenous. We find that the data's properties substantially affect the estimators' performances. We provide evidence about the impact of various data set characteristics on the estimators, so that researchers can determine the best approach for their datasets.
Keywords: Dynamic panels; Corporate finance; Econometrics

#### [In search of robust methods for dynamic panel data models in empirical corporate finance](https://doi.org/10.1016/j.jbankfin.2014.12.009)

Viet Anh Dang, Minjoo Kimb, Yongcheol Shinc

Journal of Banking & Finance

Abstract: We examine which methods are appropriate for estimating dynamic panel data models in empirical corporate finance. Our simulations show that the instrumental variable and GMM estimators are unreliable, and sensitive to the presence of unobserved heterogeneity, residual serial correlation, and changes in control parameters. The bias-corrected fixed-effects estimators, based on an analytical, bootstrap, or indirect inference approach, are found to be the most appropriate and robust methods. These estimators perform reasonably well even in models with fractional dependent variables censored at [0, 1]. We verify these results in two empirical applications, on dynamic capital structure and cash holdings.

* 3. Monte Carlo simulation studies

#### [Estimating dynamic panel data models: a guide for macroeconomists](https://doi.org/10.1016/S0165-1765(99)00130-5)

Ruth A Judson, Ann L Owen

Economics Letters

Abstract: Using a Monte Carlo approach, we find that the bias of LSDV for dynamic panel data models can be sizeable, even when T=20. A corrected LSDV estimator is the best choice overall, but practical considerations may limit its applicability. GMM is a second best solution and, for long panels, the computationally simpler Anderson–Hsiao estimator performs well.

* 3. Methodology

### 样本选择偏差相关

#### [Financial constraints and corporate investment in Asian countries](https://doi.org/10.1016/j.asieco.2014.05.004)

Rashid Ameer

Journal of Asian Economics

Abstract: This study overcomes the analytic shortcomings of the linear investment models and applies a Panel Smooth Transition Regression model to examine the investment ratios of 519 non-financial listed firms in six Asian countries over the period of 1991–2004. We find that investment-cash flow sensitivities vary across firms in the sample countries. Additionally, our findings also show that investment-cash flow sensitivity has also been affected by the business cycle in these countries. Furthermore, we find new evidence that tangible assets play a significant role in explaining the increase (decrease) in the investment-cash flow sensitivities for South and East Asian countries. These results imply that possession of the tangible assets increases debt capacity, which in turn reduces under-investment. These new findings have significant implications for financing and investment choices of the firms in the sample countries.

* 3.2. Data: We excluded those firms that had less than ten years of consecutive financial data, to calculate all explanatory variables because a balanced panel is required for estimating the PSTR model.