Forecasting Using Long-Order Autoregressive Processes: An Example Using Housing Starts

A long autoregressive (AR) modeling procedure for monthly U.S. housing starts data is considered. Neither differencing to remove the trend, nor differencing to remove the seasonal component is required in this method. The model is fitted by a Householder transformation-Akaike AIC criterion algorithm. Forecast performance is compared to that obtained by the Box-Jenkins ARIMA method. The prediction error variance of the long AR model method tends to be smaller than the prediction error variance of the Box-Jenkins model method. The long AR method is well suited for housing market time-series which are characterized by both strong seasonal and slowly changing trend components.     

Abstracts

Quality & Quantity -     

Urban Politics as the Unfolding of Social Relations in Place: The Case of Sexually Transmitted Disease Investigation in Mid‐Twentieth‐Century Gay Seattle

Recent debates in urban politics stress the need to broaden conceptions of what counts as urban politics, as well as of where they take place. This means shifting attention to include more quotidian and prosaic social relations, including those taking place in spaces of civil society. We answer this call with a case study of the relations between an emerging gay male community in mid‐twentieth‐century Seattle, USA and the local public health department’s disease investigators (DIs). We focus on both the biopolitics and cultural politics of the investigation process, from the perspectives of both DIs and gay men. We point out certain tensions and paradoxes in these processes as a form of governmentality, and interpret them through a ‘noir’ cultural lens that is consistent with a notion of urban politics as the unfolding of social relations in place. We conclude by stressing how our findings and framework can augment urban political inquiry both intellectually and empirically.     

The role of geographic distance in completing related acquisitions: Evidence from U.S. chemical manufacturers

Acquisitions often do not reach completion when buyers' initial evaluations change during post‐announcement due diligence investigations, but research offers only limited explanations for when such deal‐cancelling new information will be most common. Drawing from the spatial geography and acquisition strategy literatures, we argue that successful completion of acquisitions can be partially explained by their spatial characteristics. We start by predicting that geographic distance has a particularly strong impact in reducing the likelihood of completing related acquisitions; we then identify contingencies based on multiple forms of direct, contextual, and vicarious experience that can help acquirers overcome the constraints of distance. We test the arguments with a sample of 1,603 domestic acquisitions announced by 724 U.S. chemical manufacturing firms between 1980 and 2004. Copyright © 2015 John Wiley & Sons, Ltd.