Economicshttp://hdl.handle.net/2022/141242018-02-25T08:31:24Z2018-02-25T08:31:24ZTrade Liberalization, Heterogeneous Firms and the Soft Budget ConstraintJang, Yong JoonAlexeev, Michaelhttp://hdl.handle.net/2022/143172016-12-20T04:30:46Z2010-01-01T00:00:00ZTrade Liberalization, Heterogeneous Firms and the Soft Budget Constraint
Jang, Yong Joon; Alexeev, Michael
We analyze the interaction between the soft budget constraint (SBC) and international trade by placing Segal’s (1998) SBC model within Melitz’s (2003) framework of international trade with heterogeneous monopolistically competitive firms. As in Segal’s model, SBC may result in moral hazard. The opening to international trade adds another sort of inefficiency. Some firms that would have become exporters in the absence of SBC choose to apply low effort and not export in order to extract a subsidy from the government. This effect takes place when the trade costs are sufficiently low. Overall, however, trade liberalization reduces inefficiencies generated by SBC. The number of firms subject to moral hazard SBC decreases, aggregate effort level increases and aggregate profits lost due to SBC-induced sub-optimal effort decline as trade costs decrease.
2010-01-01T00:00:00ZEndogeneity in Nonlinear Regressions with Integrated Time SeriesPark, Joon Y.Chang, Yonsoonhttp://hdl.handle.net/2022/141282016-12-20T04:30:47Z2010-12-22T00:00:00ZEndogeneity in Nonlinear Regressions with Integrated Time Series
Park, Joon Y.; Chang, Yonsoon
This article considers the nonlinear regression with integrated regressors that are contemporaneously correlated with the regression error. We, in particular, establish the consistency and derive the limit distribution of the nonlinear least squares estimator under such endogeneity. For the regressions with various types of regression functions, it is shown that the estimator is consistent and has the same rate of convergence as for the case of the regressions with no endogeneity. Whether or not the limit distribution is affected by the presence of endogeneity, however, depends upon the functional type of the parameter derivative of regression function. If it is asymptotically homogeneous, the limit distribution of the nonlinear least squares estimator has an additional bias term reflecting the presence of endogeneity. On the other hand, the endogeneity does not have any effect on the nonlinear least squares limit theory, if the parameter derivative of regression function is integrable. Regardless of the presence of endogeneity, the
least squares estimator has the same limit distribution in this case. To illustrate our theory, we consider the nonlinear regressions with logistic and power regression functions with integrated regressors that have contemporaneous correlations with the regression error.
JEL Classification: C13, C22.
2010-12-22T00:00:00Z