Forecasting sales response for multiple time horizons and temporally aggregated data: A comparison of constant and stochastic coefficient models

Past research on time-varying sales-response models emphasized the application of different estimation techniques in examining variation in advertising effectiveness over time. This study focuses on comparing sales forecasts using constant and stochastic coefficients sales-response models. Selected constant and stochastic coefficient models are applied to six sets of bimonthly and one set of annual advertising and sales data to assess forecasting accuracy for time horizons of various lengths. Results show improved forecasting accuracy for a first-order autoregressive stochastic coefficient model, particularly in short-run forecasting applications.     

Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators

Previous work has highlighted the difficulty of obtaining accurate and economically significant predictions of VIX futures prices. We show that both low prediction errors and a significant amount of profitability can be obtained by using a neural network model to predict VIX futures returns. In particular, we focus on open-to-close returns (OTCRs) and consider intraday trading strategies, taking into account non-lagged exogenous variables that closely reflect the information possessed by traders at the time when they decide to invest. The neural network model with only the most recent exogenous variables (namely, the return on the Indian BSESN index) is superior to an unconstrained specification with ten lagged and coincident regressors, which is actually a form of weak efficiency involving markets of different countries. Moreover, the neural network turns out to be more profitable than either a logistic specification or heterogeneous autoregressive models.     

The housing bubble and a new approach to accounting for housing in a CPI

Over the course of the recent house price bubble in the United States, the price of homes rose rapidly from 1999 Q4 to 2005 Q4 (11.3% annually as measured by the Case-Shiller index, and 8.4% annually as measured by the Federal Housing Financing Agency) but slowly as measured by owner equivalent rents (3.4%), so measured core inflation remained relatively docile during this period, since only rents are used to measure inflation for housing services in the United States. Over the last several decades, the US Bureau of Labor Statistics (BLS) has experimented with both rental equivalence and user cost approaches for accounting for owner occupied housing (OOH) services in the CPI. We explain the basics of these approaches, and outline the BLS experiences with using them. This assessment leads us to conclude that the time has come to try a new approach: the opportunity cost approach. We argue this approach has advantages over both the conventional rental equivalence and user cost approaches, though it embeds components of the measures for both those approaches and builds solidly on the research of Verbrugge and others at the BLS. Also, we take up empirical issues that must be faced regardless of which of the approaches discussed is adopted. We explain how the repeat-sales and various hedonic regression methods can be placed in a common framework, thereby facilitating understanding of the properties of and the tradeoffs between the methods. We also consider measurement complications that arise because the land and structure components of properties depreciate at different rates.