The Changing Size Distribution of California's North Coast Wineries

2014 ◽  
Vol 9 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Don Cyr ◽  
Joseph Kushner ◽  
Tomson Ogwang

AbstractIn this paper, we use three different goodness-of-fit tests for log-normality in conjunction with kernel nonparametric density estimation methods to examine both the size distribution of California North Coast wineries over time and by age. Our kernel density estimates indicate that the size distribution of wineries has changed from positively skewed to bimodal. These results are inconsistent with those in other industries, but are consistent with recent empirical research in the wine industry, which finds that smaller firms are comprising a larger component of market share. In terms of the distribution of firm size by age, our results indicate that as wineries age, the size distribution of firms becomes less skewed and more bimodal, which is also inconsistent with the research on other industries which finds that as firms age, the size distribution becomes more normal. Our results indicate that unlike other industries, where entry is very difficult, small firms can enter the wine industry and survive. (JEL Classifications: L11, L22, L25)

2016 ◽  
Vol 21 (6) ◽  
pp. 1508-1518 ◽  
Author(s):  
Alexis Akira Toda

The cross-sectional distribution of consumption is commonly approximated by the lognormal distribution. This note shows that consumption is better described by the double Pareto-lognormal distribution (dPlN), which has a lognormal body with two Pareto tails and arises as the stationary distribution in recently proposed dynamic general equilibrium models. dPlN outperforms other parametric distributions and is often not rejected by goodness-of-fit tests. The analytical tractability and parsimony of dPlN may be convenient for various economic applications.


2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mei Ling Huang ◽  
Vincenzo Coia ◽  
Percy Brill

The Pareto distribution is a heavy-tailed distribution with many applications in the real world. The tail of the distribution is important, but the threshold of the distribution is difficult to determine in some situations. In this paper we consider two real-world examples with heavy-tailed observations, which leads us to propose a mixture truncated Pareto distribution (MTPD) and study its properties. We construct a cluster truncated Pareto distribution (CTPD) by using a two-point slope technique to estimate the MTPD from a random sample. We apply the MTPD and CTPD to the two examples and compare the proposed method with existing estimation methods. The results of log-log plots and goodness-of-fit tests show that the MTPD and the cluster estimation method produce very good fitting distributions with real-world data.


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