Suchergebnisse

Que Se Sepa focuses on the perspectives of individuals of Puerto Rican descent living in the Diaspora here in the city of Hartford, Connecticut. These individuals represent a community that comprises of first, second, and third generation Puerto Ricans. Through discussions surrounding the topics of food, religion, politics, race, language, and music/arts, Que Se Sepa presents an array of perspectives regarding experiences both in Hartford's Puerto Rican Diaspora and in Puerto Rico. Que Se Sepa is two things: an effort to promote dialogue and a personal journey. Que Se Sepa the dialogue gives direct agency to a community. Within the documentary, the voice of the researcher is taken out which allows 18 individuals featured in the film to directly share their story with an audience. Before embarking on this research project, I profess to knowing relatively little about vibrant community beyond the gates of Trinity College. Que Se Sepa has allowed me to establish a personal connection and appreciation for the individuals living and working within the Hartford community. I hope that both the documentary and written essay ignites a yearning for greater communication and understanding within and outside the city of Hartford.

The analysis of time-series data is fraught with problems of specification uncertainty and dynamic instability. Vector autoregression (VAR) is one attempt to overcome specification problems in time-series analysis, but this methodology has been criticized for being unparsimonious and potentially unstable through time.1 This article describes an important extension of VAR, one using Bayesian methods and allowing for time-varying parameters. These extensions improve VAR, making analysis less vulnerable to these criticisms. These VAR methods, developed by Doan, Litterman, and Sims (1984), provide a reasonable method for dealing with general time variation when theory does not provide useful a priori specification restrictions.

At one time, the lag time for the implementation of methods from economics and other disciplines in political science was quite long, reflecting the newness of political methodology as well as a lack of statistical training. The articles by Ostrom and Smith and by Durr (in this volume) represent a departure from this longstanding lag time associated with political methodology. These articles, as well as others (e.g., Beck 1992), are using the methodology of unit root econometrics and error correction models with much smaller lag time. The Ostrom and Smith article represents little lag time at all, as some of their results are using methods not as yet printed in econometrics journals!

Conventional wisdom and some research indicate that macroeconomic policies follow cycles corresponding to political, as well as economic, forces. Using vector autoregression analysis, I test three models of monetary policy determination for the United States, 1953–1984: the electoral cycle model (that reelection motivations on the part of presidents create a policy cycle), the party differences model (that policy changes reflect revolving presidential party administrations), and the referendum model (that changes in presidential approval create, in effect, a continuing referendum, allowing presidents to monitor their success and change macroeconomic policies when necessary). Analysis shows that monetary policies, as measured by the monetary base and short-term interest rates, respond to the election cycle and presidential approval (although the effect on macroeconomic outcomes is ambiguous). Party differences are found in real income but are not very significant in other variables.