A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to...

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to...

We argue in this paper that it is more feasible to use binary variables and logistic regression analyses in the assessments of AIDS incidents o or other similar researches in social science. Thus, even in measuring disease persistence we can reach solid and comprehensive results. Statistical...

We show that in models with endogeneity, bunching at the lower or upper boundary of the distribution of the treatment variable may be used to build a correction for endogeneity. We derive the asymptotic distribution of the parameters of the corrected model, provide an estimator of the standard...

High breakdown-point regression estimators protect against large errors and data contamination. We generalize the concept of trimming used by many of these robust estimators, such as the least trimmed squares and maximum trimmed likelihood, and propose a general trimmed estimator, which renders...

The current paper reviews statistical methods as integrated with the System Dynamics modeling. The methods described here are mostly statistical in nature, though some are outside the conventional definitions. The use of these techniques need not necessarily bring out positive results always but...

This paper re-estimates the constant-elasticity-of-substitution (CES) specification of aggregate production function using newly constructed annual data on physical and human capital stocks for a panel of 134 countries observed over the period 1960-2011. In our estimation, we allow parameters to...

This paper considers identification and estimation of the Quantile Treatment Effect on the Treated (QTT) under a straightforward distributional extension of the most commonly invoked Mean Difference in Differences assumption used for identifying the Average Treatment Effect on the Treated (ATT)....

This paper considers identification and estimation of the Quantile Treatment Effect on the Treated (QTT) under a straightforward distributional extension of the most commonly invoked Mean Difference in Differences Assumption used for identifying the Average Treatment Effect on the Treated (ATT)....

This paper studies the impact that pre-IPO cash flow volatility has on the initial and long-term value of a publicly traded firm. From the perspective of corporate risk management theory, higher cash flow volatility should reduce value in the form of higher borrowing costs, reduced investment,...