Distribution Free Estimation of Spatial Autoregressive Binary Choice Panel Data Models

This paper proposes a semiparametric estimator for spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects. The estimation procedure is based on the observational equivalence between distribution free models with a conditional median restriction and parametric models (such as Logit/Probit) exhibiting (multiplicative) heteroskedasticity and autocorrelation. Without imposing any parametric structure on the error terms, we consider the semiparametric nonlinear least squares (NLLS) estimator for this model and analyze its asymptotic properties under spatial near-epoch dependence. The main advantage of our method over the existing estimators is that it consistently estimates choice probabilities. The finite-dimensional estimator is shown to be consistent and root-n asymptotically normal under some reasonable conditions. Finally, a Monte Carlo study indicates that the estimator performs quite well in finite samples

Currency Crises During the Great Recession: Is This Time Different?

During the 2007-2009 financial crisis the foreign exchange market was characterized by large volatility and wide currency swings. In this paper we evaluate whether during the period of the Great Recession there has been a structural break in the relationship between fundamentals and exchange rates within an early-warning framework. This is done by extending the original data set by Kaminsky and Reinhart (1999) and including not only the most recent period, but also 17 new countries. Our analysis considers two variations of the original early-warning system. First, we propose two new methods to obtain the probability distribution of the early-warning indicator (conditional on the occurrence of a crisis) – one fully parametric and one based on a novel distribution-free semi-parametric approach. Second, we compare the original early-warning indicator with a core indicator that includes only “pseudo-financial variables” (domestic credit/GDP, the real exchange rate, international reserves and the real interest-rate differential) and we evaluate their performance not only for currency crises during the Great Recession, but also for the Asian Crisis. All tests make us conclude that “this time is different”, i.e. early-warning systems based on traditional macroeconomic variables have not only failed to forecast currency crises during the Great Recession, but have also significantly worsened with respect to the period of the Asian crisis.Early Warning Systems; Exchange Rates; Semi-parametric Meth- ods

Identification and Estimation of Outcome Response with Heterogeneous Treatment Externalities

This paper studies the identification and estimation of treatment response with heterogeneous spillovers in a network model. We generalize the standard linear-in-means model to allow for multiple groups with between and within-group interactions. We provide a set of identification conditions of peer effects and consider a 2SLS estimation approach. Large sample properties of the proposed estimators are derived. Simulation experiments show that the estimators perform well in finite samples. The model is used to study the effectiveness of policies where peer effects are seen as a mechanism through which the treatments could propagate through the network. When interactions among groups are at work, a shock on a treated group has effects on the non-treated. Our framework allows for quantifying how much of the indirect treatment effect is due to variations in the characteristics of treated peers (treatment contextual effects) and how much is because of variations in peer outcomes (peer effects)

Currency Crises During the Great Recession: Is This Time Different?

During the 2007-2009 financial crisis the foreign exchange market was characterized by large volatility and wide currency swings. In this paper we evaluate whether during the period of the Great Recession there has been a structural break in the relationship between fundamentals and exchange rates within an early-warning framework. This is done by extending the original data set by Kaminsky and Reinhart (1999) and including not only the most recent period, but also 17 new countries. Our analysis considers two variations of the original early-warning system. First, we propose two new methods to obtain the probability distribution of the early-warning indicator (conditional on the occurrence of a crisis) – one fully parametric and one based on a novel distribution-free semi-parametric approach. Second, we compare the original early-warning indicator with a core indicator that includes only “pseudo-financial variables” (domestic credit/GDP, the real exchange rate, international reserves and the real interest-rate differential) and we evaluate their performance not only for currency crises during the Great Recession, but also for the Asian Crisis. All tests make us conclude that “this time is different”, i.e. early-warning systems based on traditional macroeconomic variables have not only failed to forecast currency crises during the Great Recession, but have also significantly worsened with respect to the period of the Asian crisis

Residential choices of young Americans

Using detailed data on a cohort of young Americans who were in their late twenties and early thirties in 2008, we investigate the importance of forces different from economic incentives in nest-leaving decisions. We apply recent methods from social network econometrics to identify the importance of peers net of confounding factors. For the entire sample, our findings reveal no evidence of peer effects. Indicators of parenting and the social structure of families appear to be the major factors in the decisions to coreside with parents. However, for those who moved back home after a few years of living alone, we find strong peer effects. These findings are consistent with theories of social influences in peer groups in which peers play a critical role for individuals with time-inconsistent preferences. (C) 2016 Elsevier Inc. All rights reserved

Parametric and Semiparametric IV Estimation of Network Models with Selectivity

We propose parametric and semiparametric IV estimators for spatial autoregressive models with network data where the network structure is endogenous. We embed a dyadic network formation process in the control function approach as in Heckman and Robb (1985). In the semiparametric case, we use power series to approximate the correction terms. We establish the consistency and asymptotic normality for both parametric and semiparametric cases. We also investigate their finite sample properties via Monte Carlo simulatio