This article examines the anticompetitive effects of land use regulation using microdata on midscale chain hotels in Texas. I construct a dynamic entry-exit model that endogenizes hotel chains' reactions to land use regulation. My estimates indicate that imposing stringent regulation increases costs considerably. Hotel chains nonetheless enter highly regulated markets even if entry probabilities are lower, anticipating fewer rivals and hence greater market power. Consumers incur the costs of regulation indirectly in the form of higher prices.

1. Impact of Data Size on Quality of Forecasting in a Theoretical Model Retail companies use proprietary forecasting models, which are often the result of a complicated engineering effort, tailored to the particularities of the business. Theoretical properties of such complicated forecasting models may never be understood completely. Hence we give a theoretical benchmark based on a well-known, state-of-the-art model used in academia to model panel time series and forecasting. In particular, we utilize the Augmented Factor model; see, for example, Bai and Ng (2002), Bernanke et al (2004), and Bai (2009). Within this fairly general, yet tractable model, we ask the question of how forecast errors would be determined and what role do the data size, the number of products and number of time periods in the available history, have.The quantity , sold by a retailer of a product at time , obeys the following equation:where , ≥ 1 is a known velocity variable, which describes the stochastic, possibly nonstationary level of the series, and , ≥ 1 is a reference demand level, where , stationary for each . Note that , = 0 if and only if , = 1 and , = 1. The quantity , (plus 1) is determined by the base demand times the multiplier , that captures the ``size of the firm" in product sales as well as "velocity" of the product. Velocity , reflects the notional popularity of the product at time , and represents the product-specific size of the retailer, as specific to the product. A simple example of , is given by the lagged sales , = ( , + 1). We can normalize the velocity at , =1 for = 1. The vector of velocities , characterizes the overall ``size of the retailer" over time. In the model, the base quantity index , is determined by latent factor components plus observed components plus stochastic shocks: (i) Time-varying factors are latent time-varying common factors, such as macro-economic factors, seasonality, and fashion factors. (ii) The product varying factors are the product-specific loadings on the latent factors , to which product demand responds differently. They include the conventional fixed effects model, when = 1. (iii) The observed component is determined by adimensional vector , of observed time-varying product features, such as prices of the product

This paper addresses a fundamental identification problem in the structural estimation of dynamic oligopoly models of market entry and exit. Using the standard datasets in existing empirical applications, three components of a firm's profit function are not separately identified: the fixed cost of an incumbent firm, the entry cost of a new entrant, and the scrap value of an exiting firm. We study the implications of this result on the power of this class of models to identify the effects of different comparative static exercises and counterfactual public policies. First, we derive a closed-form relationship between the three unknown structural functions and the two functions that are identified from the data. We use this relationship to provide the correct interpretation of the estimated objects that are obtained under the 'normalization assumptions' considered in most applications. Second, we characterize a class of counterfactual experiments that are identified using the estimated model, despite the non-separate identification of the three primitives. Third, we show that there is a general class of counterfactual experiments of economic relevance that are not identified. We present a numerical example that illustrates how ignoring the non-identification of these counterfactuals (i.e., making a 'normalization assumption' on some of the three primitives) generates sizable biases that can modify even the sign of the estimated effects. Finally, we discuss possible solutions to address these identification problems.

IntroductionUrethral caruncles are the most frequent benign tumors of the female urethra. Most of them are found in post-menopausal women, and they are rare in childhood. Only a few pediatric cases have been published in the literature. In this report, we present an unusual case of a pediatric patient with a urethral caruncle, along with a review of the literature.Case presentationA 9-year-old Mongolian girl was referred to our hospital with a 2-week history of frequent adherence of a small amount of blood to her underwear. We found a sessile smooth margin, a clear boundary and an elastic, soft red tumor over the entire circumference of the urethral meatus. At the beginning, because of the child’s age, urethral prolapse was suspected. There was no response after 3 weeks of conservative treatment with steroid ointment. With the patient under general anesthesia, a partial tumor resection was performed for the purpose of histological examination. The tumor excision was limited to about 1/2 laps of the urethral meatus to prevent the development of urethral stricture. On the basis of clinical and histopathological examinations, a diagnosis of a urethral caruncle was made. Post-operatively, steroid ointment application to residual masses was continued, and these disappeared about 6 months later. Our patient was free of recurrence and had had no complications after 3 years of follow-up.ConclusionsUrethral caruncles are rare in children, and the possibility of malignancy is slight during this period. Biopsy of the mass is not required for diagnosis. It should be indicated only if the mass has other characteristics that raise suspicion of malignancy. In previously reported cases, all of the tumor was removed. However, the trigger of the caruncle in childhood is chronic inflammation. Conservative therapy with steroid ointment should be the core treatment. However, it may be necessary to proceed to treatment because caruncles take a long time to heal. The case that we describe in this report will serve as an example for similar cases in the future.

We examine the impact of “big data” on firm performance in the context of forecast accuracy using proprietary retail sales data obtained from Amazon. We measure the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors diminishing at rate 1/sqrt(N)+1/sqrt(T). Empirical results indicate gains in forecast improvement in the T dimension but essentially flat N effects.