Studying the shape of US intra-national trade
with G Magkonis, S Rudkin and P Dlotko
In this paper we examine US intra-national trade patterns by applying the Topological Data Analysis (TDA) Ball Mapper(BM) algorithm to generate two-dimensional representations of state-to-state trade flows and their associated characteristics. These representations enable a rapid appraisal of the shape of the data being studied, while paying particular attention to the areas that defy gravity. We use a residual analysis, based on our gravity estimations, to highlight the interesting cases and in doing so, we illustrate the usefulness of BM plots as part of the trade analysis toolkit. For US policy makers, keen to pursue policies such as 'Made in America' it is important to be able to access tools that can shed light on the patterns behind the aggregate figures. For example, our findings suggest there there are less well understood patterns of manufacturing, wood and leather trade from poorer states that ship products to other nearby locations. It is these types of cases which we highlight for greater policy attention.
The role of global and regional factors driving international trade
with K Beck
This paper examines the relative importance of global, regional, country and idiosyncratic factors, as well as the determinants that underpin fluctuations in international trade flows across different regions of the world. Our analysis uses a two-step process, starting with a Bayesian dynamic latent factor model (BDFM) to simultaneously estimate the four dynamic factors, followed by the application of Bayesian model averaging to identify the variables that explain the shares of volatility. Our key findings are: (i) international factors are the most important in explaining fluctuations in international trade, suggesting that the interconnections between economies, and policies/shocks at the regional and global level, tend to be more important than country-level factors (ii) regional integration, particularly when the agreement goes beyond trade in goods, is positively related to the share of the regional factor, and inversely related to the importance of the global factor. Furthermore, the regional factor is more important in the case of economically large trade blocks. Overall, our analysis illustrates the usefulness of applying a BDFM model to study the co-movements of international trade series.
Working paper available
Risky business: Political stability along the Belt and Road
with O Shepotylo
This paper explores the impact of improvements in the political environment on trade and welfare. We conduct a counterfactual analysis using a structural gravity approach, to investigate how the Belt and Road Initiative (BRI) combined with more assertive and active Chinese foreign policy would impact on global trade flows and global welfare. In our model, the BRI benefits come from reduced trade costs, reduced bilateral political uncertainty, military alliances, and greater political stability in BRI countries. Based on our full general equilibrium results, our key findings are that (i) military alliances between BRI countries and China are expected to have the most positive effect on welfare, with particularly positive effects on China and South Asia (ii) improved political stability across BRI countries is expected to have the most beneficial impact on South Asia. These results suggest that it is important to look beyond economic gains derived from trade cost reductions; politically aligning countries participating in the BRI and providing security to the countries where China invests in transport and infrastructure has the potential to deliver significant benefits.
Exchange Rate Predictability: Fact or Fiction?
with G Magkonis
The present study investigates the factors that affect the forecasting performance of several models that have been used for exchange rate prediction. We provide a quantitative survey collecting 5,202 reported forecast errors and we investigate which forecasting characteristics tend to improve forecasting ability. According to our evidence, predictions can beat random walk when certain types of predictors and models are used. Furthermore, the data frequency and the forecasting horizon are also important factors of forecasting performance. In this way, we identify under which conditions it is feasible to solve the `Meese-Rogoff' puzzle.