Has the U.S.-Vietnam Bilateral Trade Agreement Led to Higher FDI into Vietnam?

In December 2001, a Bilateral Trade Agreement (BTA) came into effect that normalized economic relations between the United States and Vietnam. The resulting surge in trade surpassed most expectations. The impact of the BTA on FDI, however, has been less visible, especially with regard to U.S. FDI into Vietnam. This paper uses new data that accounts for FDI by U.S. subsidiaries resident in third counties to show that U.S. firms have been much more aggressive investors in Vietnam than normally reported in typical bilateral FDI data using Balance of Payments definitions of capital flows. While the U.S. is widely reported as the 11th largest investor into Vietnam, the new data shows that U.S.-related FDI exceeded all other countries in 2004. Although a formal model is not developed, descriptive data supports strongly the conclusion that the BTA has had a major impact on FDI into Vietnam, especially with regard to FDI from U.S. multinationals.FDI; Trade Agreement

A natural experiment of social network formation and dynamics

10.1073/pnas.1404770112Proceedings of the National Academy of Sciences of the United States of America112216595-660

Carleman estimate for infinite cylindrical quantum domains and application to inverse problems

We consider the inverse problem of determining the time independent scalar potential of the dynamic Schrödinger equation in an infinite cylindrical domain, from one Neumann boundary observation of the solution. Assuming that this potential is known outside some fixed compact subset of the waveguide, we prove that it may be Lipschitz stably retrieved by choosing the Dirichlet boundary condition of the system suitably. Since the proof is by means of a global Carleman estimate designed specifically for the Schrödinger operator acting in an unbounded cylindrical domain, the Neumann data is measured on an infinitely extended subboundary of the cylinder

Weak factor model in large dimension

This thesis presents some extensions to the current literature in high-dimensional static factor models. When the cross-section dimension (represented by N hence-forth) is very large, the standard assumption for each common factor is to have the number of non-zero loadings grow linearly with N . On the other hand, an idiosyncratic error for each component can only be correlated with a finite number of other components in the cross-section. These two assumptions are crucial in standard high-dimensional factor analysis, as they allow us to obtain consistent estimators for the factors, the loadings and the number of factors. However, together they rule out the possibility that we may have some factors that have strictly less than N but still non-negligible number of non-zero loadings, e.g. N for some 0 < < 1 . The existence of these weak factors will decrease the signal-to-noise ratio as now the gap between the systematic and idiosyncratic eigenvalues is more narrow. As the consequence, in such model it is harder to establish the consistency of the factors estimated by sample principle components. Furthermore, the number of factors is even more challenging to identify because most existing methods rely on the large signal-to-noise ratio. In this thesis, I consider a factor model that allows general strength for each factor, i.e. both strong and weak factors can exist. Chapter 1 gives more discussions about the current literature on this and the motivation for my contribution. In Chapter 2, I show that the sample principle components are still the consistent estimators for the factors (up to the spanning space), provided that the factors are not too weak. In addition, I derive the lower bound that the strength of the weakest factor needs to achieve for being consistently estimated. More precisely, what I mean by strength is the order of the number of non-zero loadings of the factor. Chapter 3 presents a novel method to determine the number of factors, which is asymptotically consistent even when the factors are weak. I run extensive Monte Carlo simulations to compare the performance of this method to the two well-known ones, i.e. the class of criteria proposed in Bai and Ng (2002) and the eigenvalue ratio method in Ahn and Horenstein (2013). In Chapter 4 and 5, I show some applications that are based on the work of this thesis. I mainly focus on two issues: selecting the factor models in practice and using factor analysis to compute the large static covariance matrix

INVESTIGATING THE EFFECTS OF SELFPRESENTATION AT SOCIAL NETWORK SITES ON PURCHASE BEHAVIOR: A TEXT MINING AND ECONOMETRIC APPROACH

With advances in information and communication technologies (ICT), companies and platforms look to use the increasing volume and diversity of user-generated content (UGC) to predict consumer behavior, but with mixed results. In this study, we propose a text mining technique to find support for self-presentation in online social media and show that this is correlated with the content producer’s offline purchase behaviour. We use unique datasets from a social network site (SNS) and an offline fashion retailer to find that: 1) while public and private volume and sentiment metrics leads to non-significant predictions, the sentiment divergence can significantly explain offline purchases, 2) users who engage in SNS for self-presentation spend less money and buy less quantities, and 3) however, they spend more when exposed to specific site features that inspire self-presentation, like brand pages. Marketers and platform owners can benefit from our results by designing appropriate features to target such users