Advances in data collection have made it increasingly easy to collect data on consumer-level purchases that are linked to those same customers’ advertising exposures. However, advances in advertising response modeling (i.e., marketing mix modeling) have lagged behind the availability of this granular data. Extending extant models to multi-channel consumer-level data, we develop a Bayesian Tobit...

This paper describes numerically simple estimators that can be used to estimate binary choice and other related models (such as selection and ordered choice models) when some regressors are endogenous or mismeasured. Simple estimators are provided that allow for discrete or otherwise limited endogenous regressors, lagged dependent variables and other dynamic effects, heteroskedastic and autocor...

This paper describes numerically simple estimators that can be used to estimate binary choice and other related models (such as selection and ordered choice models) when some regressors are endogenous or mismeasured. Simple estimators are provided that allow for discrete or otherwise limited endogenous regressors, lagged dependent variables and other dynamic effects, heteroskedastic and autocor...

soil moisture is one of the critical parameters in climatic, ecological and hydrological modeling and vegetation growth. soil moisture is important for environmental studies but due to ground-based point measurement of soil moisture is expensive and impractical for watershed measurement, this parameter is not be used widely to simulate and predict models. overall and uniform view on different p...

This study uses the Autoregressive Distributed Lag (ARDL) model to study the dynamic determinant factors that influence both long and short-term international tourism demand in Cambodia from 12 countries. The annual time series data (1994–2013) are used in this study. The study finds that only the international tourism demand model of Australia, Canada, Thailand, the United Kingdom, and USA are...

because of its extensive applications in financial analysis, stock market volatility modeling is a significantly important issue for stock market practitioners and academicians. using garch models to formulate the conditional variance heteroskedasticity and the taking advantages of panel data technique such as higher degrees of freedom, more flexibility in the control of the omitted or unobserv...

Intraindividual patterns of time-lagged relationships among self-reports of worldviews/religious beliefs, self-concept, and physical and psychological wellbeing were investigated. Participants were older adults (mean age = 77 years) who were measured weekly covering a total of 25 weeks. Dynamic Factor Models were fitted to multivariate repeated measures data pooled over subsets of participants....

Case, Quigley, and Shiller (2005) persuasively argue that the well-known conceptual difficulties in measuring aggregate “wealth effects” might be lessened by the use of state-level data. Unfortunately, the data required for a convincing implementation of their idea have been either virtually nonexistent (for financial wealth) or of questionable quality (for consumption). Our main contributions ...

Children in single-parent families, particularly children born to unmarried parents, are at high risk for experiencing material hardship. Previous research based on cross-sectional data suggests that father involvement, especially visitation, diminishes hardship. This article uses longitudinal data to examine the associations between nonresident fathers' involvement with their children and mate...

An Econometric Model of Credit Spreads with Rebalance, ARCH, and Jump Effects In this paper, we examine the dynamic behavior of credit spreads on corporate bond portfolios. We propose an econometric model of credit spreads that incorporates portfolio rebalancing, the near unit root property of spreads, the autocorrelation in spread changes, the ARCH conditional heteroscedasticity, jumps, and la...