Essays on capacity underutilization and demand driven business cycles

In Chapter 1, I build a macroeconomic model that features chronic excess capacity. In my model, if one firm expands its capacity while other firms do not, it \steals" profitable demand from others. This capacity competition externality can cause an over-investment in capacity. I show that with the existence of chronic excess capacity, capital resources can be slack and consumption demand shocks can generate realistic business cycles. If consumption demand increases, more capacity will be utilized, heating up the capacity competition: firms invest with haste until the capacity utilization rate falls back to normal. If consumption demand decreases, more capacity will be left idle, cooling down the capacity competition: firms dis-invest with haste until the capacity utilization rate goes back to normal. In Chapter 2, I show that the above results cannot be obtained in models with efficient utilization of capital or capacity. In these models, there is no capacity competition externality. None of these models could feature chronic excess capacity nor capital resource slackness. Thus, the response of output to demand shocks is limited and it is difficult to obtain demand driven business cycles in these models. In Chapter 3, I study what kind of goods market structure features the capacity competition externality that can cause chronic excess capacity. The following assumptions are identified. First, if a firm expands its capacity while other firms do not, it can \steal" demand from others. Second, firms can charge a sufficiently high price to make a positive net profit. These two assumptions imply a negative capacity competition externality and are sufficient to cause long-term capacity underutilization at the firm-level. Third, if the invested capital has no positive externality that can potentially offset the negative externality, the capacity competition externality will be dominant and the economy will exhibit chronic excess capacity. I present several different ways to micro-found this kind of goods market structure, demonstrating the generality of the results obtained in the previous chapters

Excess capacity and demand driven business cycles

I build a macroeconomic model that features chronic excess capacity. Firms can use capacity to compete for buyers who are not fully attentive to prices. If one firm expands capacity while other firms do not, it “steals” or attracts profitable demand from others. Theoretically, I show that this capacity competition can cause an over-accumulation of capacity. In the presence of chronic excess capacity, capital resources can be slack, and demand shocks can have large effects on output. The model is consistent with stylized facts about capacity utilization and survey evidence from Switzerland. Quantitatively, when the model is estimated to match the U.S. macro data, demand shocks turn out to be the main driving forces of business cycles

Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter

The likelihood calculation of a vast number of particles is the computational bottleneck for the particle filter in applications where the observation information is rich. For fast computing the likelihood of particles, a numerical fitting approach is proposed to construct the Likelihood Probability Density Function (Li-PDF) by using a comparably small number of so-called fulcrums. The likelihood of particles is thereby analytically inferred, explicitly or implicitly, based on the Li-PDF instead of directly computed by utilizing the observation, which can significantly reduce the computation and enables real time filtering. The proposed approach guarantees the estimation quality when an appropriate fitting function and properly distributed fulcrums are used. The details for construction of the fitting function and fulcrums are addressed respectively in detail. In particular, to deal with multivariate fitting, the nonparametric kernel density estimator is presented which is flexible and convenient for implicit Li-PDF implementation. Simulation comparison with a variety of existing approaches on a benchmark 1-dimensional model and multi-dimensional robot localization and visual tracking demonstrate the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a draft/preprint of one paper submitted to the IEEE Transaction

Attribute-preserving gamut mapping of measured BRDFs

Reproducing the appearance of real-world materials using current printing technology is problematic. The reduced number of inks available define the printer's limited gamut, creating distortions in the printed appearance that are hard to control. Gamut mapping refers to the process of bringing an out-of-gamut material appearance into the printer's gamut, while minimizing such distortions as much as possible. We present a novel two-step gamut mapping algorithm that allows users to specify which perceptual attribute of the original material they want to preserve (such as brightness, or roughness). In the first step, we work in the low-dimensional intuitive appearance space recently proposed by Serrano et al. [SGM*16], and adjust achromatic reflectance via an objective function that strives to preserve certain attributes. From such intermediate representation, we then perform an image-based optimization including color information, to bring the BRDF into gamut. We show, both objectively and through a user study, how our method yields superior results compared to the state of the art, with the additional advantage that the user can specify which visual attributes need to be preserved. Moreover, we show how this approach can also be used for attribute-preserving material editing