Moderating Factors of Immediate, Dynamic, and Long-run Cross-Price Effects

In this article the authors describe their comprehensive analysis of moderating factors of cross-brand effects of price changes and contribute to the literature in five major ways. (1) They consider an extensive set of potential variables influencing cross-brand effects of price changes. (2) They examine moderators for the immediate as well as the dynamic cross-price effect. (3) They decompose price into regular and promotional price and study both cross-price effects separately. (4) They compare their findings with previous literature on the moderating factors of own-price effects to understand which factors influence own-price elasticity through affecting brand switching. (5) The authors use an advanced Bayesian estimation technique. The results show evidence of the neighborhood price effect and suggest that it is conditional on whether the promoted brand is priced above or below its competitor. The promoted brand's activities turn out to play a much more important role in determining the cross-price promotional effects than its competitor's similar activities. The authors outline conditions when cross-brand post-promotion dips tend to occur. Finally, they argue that the brand choice portion of the overall own-brand effect of a promotion depends on the brand's marketing strategy and on category-specific characteristics.dynamic effects;asymmetry;hierarchical Bayes;cross-price elasticity

Picard group of isotropic realizations of twisted poisson manifolds

Let B be a twisted Poisson manifold with a fixed tropical affine structure given by a period bundle P. In this paper, we study the classification of almost symplectically complete isotropic realizations (ASCIRs) over B in the spirit of [DD]. We construct a product among ASCIRs in analogy with tensor product of line bundles, thereby introducing the notion of the Picard group of B. We give descriptions of the Picard group in terms of exact sequences involving certain sheaf cohomology groups, and find that the ‘N´eron-Severi group’ is isomorphic to H(2) (B,P). An example of an ASCIR over a certain open subset of a compact Lie group is discussed.Chi-Kwong Fo

Bayesian field theoretic reconstruction of bond potential and bond mobility in single molecule force spectroscopy

Quantifying the forces between and within macromolecules is a necessary first step in understanding the mechanics of molecular structure, protein folding, and enzyme function and performance. In such macromolecular settings, dynamic single-molecule force spectroscopy (DFS) has been used to distort bonds. The resulting responses, in the form of rupture forces, work applied, and trajectories of displacements, have been used to reconstruct bond potentials. Such approaches often rely on simple parameterizations of one-dimensional bond potentials, assumptions on equilibrium starting states, and/or large amounts of trajectory data. Parametric approaches typically fail at inferring complex-shaped bond potentials with multiple minima, while piecewise estimation may not guarantee smooth results with the appropriate behavior at large distances. Existing techniques, particularly those based on work theorems, also do not address spatial variations in the diffusivity that may arise from spatially inhomogeneous coupling to other degrees of freedom in the macromolecule, thereby presenting an incomplete picture of the overall bond dynamics. To solve these challenges, we have developed a comprehensive empirical Bayesian approach that incorporates data and regularization terms directly into a path integral. All experiemental and statistical parameters in our method are estimated empirically directly from the data. Upon testing our method on simulated data, our regularized approach requires fewer data and allows simultaneous inference of both complex bond potentials and diffusivity profiles.Comment: In review - Python source code available on github. Abridged abstract on arXi

A hierarchical Bayes error correction model to explain dynamic effects

For promotional planning and market segmentation it is important to understand the short-run and long-run effects of the marketing mix on category and brand sales. In this paper we put forward a sales response model to explain the differences in short-run and long-run effects of promotions on sales. The model consists of a vector autoregression rewritten in error-correction format which allows us to disentangle the long-run effects from the short-run effects. In a second level of the model, we correlate the short-run and long-run elasticities with various brand-specific and category-specific characteristics. The model is applied to weekly sales of 100 different brands in 25 product categories. Our empirical results allow us to make generalizing statements on the dynamic effects of promotions in a statistically coherent way.vector autoregression;sales;hierarchical Bayes;short and long run effects

Moderating Factors of Immediate, Dynamic, and Long-run Cross-Price Effects

In this article the authors describe their comprehensive analysis of moderating factors of cross-brand effects of price changes and contribute to the literature in five major ways. (1) They consider an extensive set of potential variables influencing cross-brand effects of price changes. (2) They examine moderators for the immediate as well as the dynamic cross-price effect. (3) They decompose price into regular and promotional price and study both cross-price effects separately. (4) They compare their findings with previous literature on the moderating factors of own-price effects to understand which factors influence own-price elasticity through affecting brand switching. (5) The authors use an advanced Bayesian estimation technique. The results show evidence of the neighborhood price effect and suggest that it is conditional on whether the promoted brand is priced above or below its competitor. The promoted brand's activities turn out to play a much more important role in determining the cross-price promotional effects than its competitor's similar activities. The authors outline conditions when cross-brand post-promotion dips tend to occur. Finally, they argue that the brand choice portion of the overall own-brand effect of a promotion depends on the brand's marketing strategy and on category-specific characteristics

Random Coefficient Logit Model for Large Datasets

We present an approach for analyzing market shares and products price elasticities based on large datasets containing aggregate sales data for many products, several markets and for relatively long time periods. We consider the recently proposed Bayesian approach of Jiang et al [Jiang, Renna, Machanda, Puneet and Peter Rossi, 2009. Journal of Econometrics 149 (2) 136-148] and we extend their method in four directions. First, we reduce the dimensionality of the covariance matrix of the random effects by using a factor structure. The dimension reduction can be substantial depending on the number of common factors and the number of products. Second, we parametrize the covariance matrix in terms of correlations and standard deviations, like Barnard et al. [Barnard, John, McCulloch, Robert and Xiao-Li Meng, 2000. Statistica Sinica 10 1281-1311] and we present a Metropolis sampling scheme based on this specification. Third, we allow for long term trends in preferences using time-varying common factors. Inference on these factors is obtained using a simulation smoother for state space time series. Finally, we consider an attractive combination of priors applied to each market and globally to all markets to speed up computation time. The main advantage of this prior specification is that it let us estimate the random coefficients based on all data available. We study both simulated data and a real dataset containing several markets each consisting of 30 to 60 products and our method proves to be promising with immediate practical applicability

The ionospheric outflow feedback loop

AbstractFollowing a long period of observation and investigation beginning in the early 1970s, it has been firmly established that Earth׳s magnetosphere is defined as much by the geogenic plasma within it as by the geomagnetic field. This plasma is not confined to the ionosphere proper, defined as the region within a few density scale heights of the F-region plasma density peak. Rather, it fills the flux tubes on which it is created, and circulates throughout the magnetosphere in a pattern driven by solar wind plasma that becomes magnetically connected to the ionosphere by reconnection through the dayside magnetopause. Under certain solar wind conditions, plasma and field energy is stored in the magnetotail rather than being smoothly recirculated back to the dayside. Its release into the downstream solar wind is produced by magnetotail disconnection of stored plasma and fields both continuously and in the form of discrete plasmoids, with associated generation of energetic Earthward-moving bursty bulk flows and injection fronts. A new generation of global circulation models is showing us that outflowing ionospheric plasmas, especially O+, load the system in a different way than the resistive F-region load of currents dissipating energy in the plasma and atmospheric neutral gas. The extended ionospheric load is reactive to the primary dissipation, forming a time-delayed feedback loop within the system. That sets up or intensifies bursty transient behaviors that would be weaker or absent if the ionosphere did not “strike back” when stimulated. Understanding this response appears to be a necessary, if not sufficient, condition for us to gain accurate predictive capability for space weather. However, full predictive understanding of outflow and incorporation into global simulations requires a clear observational and theoretical identification of the causal mechanisms of the outflows. This remains elusive and requires a dedicated mission effort

The Bipolar II depression questionnaire: A self-report tool for detecting Bipolar II depression

Bipolar II (BP-II) depression is often misdiagnosed as unipolar (UP) depression, resulting in suboptimal treatment. Tools for differentiating between these two types of depression are lacking. This study aimed to develop a simple, self-report screening instrument to help distinguish BP-II depression from UP depressive disorder. A prototype BP-II depression questionnaire (BPIIDQ-P) was constructed following a literature review, panel discussions and a field trial. Consecutively assessed patients with a diagnosis of depressive disorder or BP with depressive episodes completed the BPIIDQ-P at a psychiatric outpatient clinic in Hong Kong between October and December 2013. Data were analyzed using discriminant analysis and logistic regression. Of the 298 subjects recruited, 65 (21.8%) were males and 233 (78.2%) females. There were 112 (37.6%) subjects with BP depression [BP-I = 42 (14.1%), BP-II = 70 (23.5%)] and 182 (62.4%) with UP depression. Based on family history, age at onset, postpartum depression, episodic course, attacks of anxiety, hypersomnia, social phobia and agoraphobia, the 8-item BPIIDQ-8 was constructed. The BPIIDQ-8 differentiated subjects with BP-II from those with UP depression with a sensitivity/specificity of 0.75/0.63 for the whole sample and 0.77/0.72 for a female subgroup with a history of childbirth. The BPIIDQ-8 can differentiate BP-II from UP depression at the secondary care level with satisfactory to good reliability and validity. It has good potential as a screening tool for BP-II depression in primary care settings. Recall bias, the relatively small sample size, and the high proportion of females in the BP-II sample limit the generalization of the results