Their names are iconic: Eugene O’Neill, Willem de Kooning, Josef and Anni Albers, Emma Goldman, Mary McCarthy, Edward Hopper, Walter Gropius—the list goes on and on. Scorning the devastation that industrialization had wrought on the nation’s workforce and culture in the early decades of the twentieth century, they gathered in the streets of Greenwich Village and on the beachfronts of Cape Cod. They began as progressives but soon turned to socialism, then communism. They founded theatres, periodicals, and art schools. They formed editorial boards that met in beach shacks and performed radical new plays in a shanty on the docks, where they could see the ocean through cracks in the floor. They welcomed the tremendous wave of talent fleeing Europe in the 1930s. At the end of their era, in the 1960s, as the post-war economy boomed, they took shelter in liberalism when the anti-capitalist movement fragmented into other causes. John Taylor “Ike” Williams, who married into the Cape’s artistic world and has spent half a century talking about and walking along its shores with these cultural and political luminaries, renders the twisting lives and careers of a generation of staggering American thinkers and creators. The Shores of Bohemia records a great set of shifts in American culture and the ideas and arguments fuelled by drink, infidelity, and competition that made for a fifty-year conversation among intellectual leaders and creative revolutionaries. Together they found a community as they created some of the great works of the American Century. This is their story. Welcome to the party!

Many analyses of time series data involve multiple, related variables. Modeling Multiple Time Series presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these models is discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available. Key Features: * Offers a detailed comparison of different time series methods and approaches. * Includes a self-contained introduction to vector autoregression modeling. * Situates multiple time series modeling as a natural extension of commonly taught statistical models.