Identification in Differentiated Products Markets Using Market Level Data

We consider nonparametric identification in models of differentiated products markets, using only market level observables. On the demand side we consider a nonparametric random utility model nesting random coefficients discrete choice models widely used in applied work. We allow for product/market-specific unobservables, endogenous product characteristics (e.g., prices), and high-dimensional taste shocks with arbitrary correlation and heteroskedasticity. On the supply side we specify marginal costs nonparametrically, allow for unobserved firm heterogeneity, and nest a variety of equilibrium oligopoly models. We pursue two approaches to identification. One relies on instrumental variables conditions used previously to demonstrate identification in a nonparametric regression framework. With this approach we can show identification of the demand side without reference to a particular supply model. Adding the supply side allows identification of firms' marginal costs as well. Our second approach, more closely linked to classical identification arguments for supply and demand models, employs a change of variables approach. This leads to constructive identification results relying on exclusion and support conditions. Our results lead to a testable restriction that provides the first general formalization of Bresnahan's (1982) intuition for empirically discriminating between alternative models of oligopoly competition.

Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers

We consider identification of nonparametric random utility models of multinomial choice using "micro data," i.e., observation of the characteristics and choices of individual consumers. Our model of preferences nests random coefficients discrete choice models widely used in practice with parametric functional form and distributional assumptions. However, the model is nonparametric and distribution free. It allows choice-specific unobservables, endogenous choice characteristics, unknown heteroskedasticity, and high-dimensional correlated taste shocks. Under standard "large support" and instrumental variables assumptions, we show identifiability of the random utility model. We demonstrate robustness of these results to relaxation of the large support condition and show that when it is replaced with a weaker "common choice probability" condition, the demand structure is still identified. We show that key maintained hypotheses are testable.Nonparametric identification, Discrete choice demand, Differentiated products

Identification in a Class of Nonparametric Simultaneous Equations Models

We consider identification in a class of nonseparable nonparametric simultaneous equations models introduced by Matzkin (2008). These models combine standard exclusion restrictions with a requirement that each structural error enter through a "residual index" function. We provide constructive proofs of identification under several sets of conditions, demonstrating tradeoffs between restrictions on the support of the instruments, restrictions on the joint distribution of the structural errors, and restrictions on the form of the residual index function.Simultaneous equations, Nonseparable models, Nonparametric identification

Identification of a Heterogeneous Generalized Regression Model with Group Effects

We consider identification in a "generalized regression model" (Han, 1987) for panel settings in which each observation can be associated with a "group" whose members are subject to a common unobserved shock. Common examples of groups include markets, schools or cities. The model is fully nonparametric and allows for the endogeneity of group-specific observables, which might include prices, policies, and/or treatments. The model features heterogeneous responses to observables and unobservables, and arbitrary heteroskedasticity. We provide sufficient conditions for full identification of the model, as well as weaker conditions sufficient for identification of the latent group effects and the distribution of outcomes conditional on covariates and the group effect.Nonparametric identification, Binary choice, Threshold crossing, Censored regression, Proportional hazard model

Lessons from upstream soil conservation measures to mitigate soil erosion and its impact on upstream and downstream users of the Nile River

A study was conducted to evaluate the effects of soil bunds stabilized with vetiver grass (V. zizanioides) and tree lucerne (C. palmensis) on selected soil physical and chemical properties, bund height, inter-terrace slope and barley (Hordeum vulgare L.) yield in Absela site, Banja Shikudad District, Awi administrative Zone of the Amhara National Regional State (ANRS) located in the Blue Nile Basin. The experiment had five treatments that included non-conserved land (control), a 9-year old soil bund stabilized with tree lucerne, a 9-year old soil bund stabilized with vetiver grass, a 9-year old sole soil bund, and a 6-year old soil bund stabilized with tree lucerne. Data were analyzed using one-way analysis of variance (ANOVA) and mean values for the treatments were separated using the Duncan Multiple Range Test. Results of the experiment indicated that organic carbon (OC), total nitrogen (N), bulk density, infiltration rate, bund height, and inter-terrace slope are significantly (p?0.05) affected by soil conservation measures. The non-conserved fields had significantly lower OC, total N, and infiltration rate; whereas higher bulk density as compared to the conserved fields with different conservation measures. However, no significant differences in bulk density were observed among the conservation methods. The field treated with 9-year old soil bund stabilized with tree lucerne or sole soil bund had significantly higher OC content than all other treatments. Fields having 6-year old soil bunds had lower OC and total N when compared to fields having 9-year old soil bunds irrespective of their method of stabilization. Fields with soil bunds stabilized with vetiver grass had the highest bund height and the lowest inter-terrace slope than fields with the remaining conservation measures. Barley grain and straw yields were significantly (P<0.05) greater in both the soil accumulation and loss zones of the conserved fields than the non-conserved (control) ones. In the accumulation zone, fields with the 9-year old soil bund stabilized with tree lucerne and those with the 9-year old sole soil bund gave higher grain yields (1878.5 kg ha-1 and 1712.5 kg ha-1, respectively) than fields having 9-year old soil bund stabilized with vetiver grass (1187 kg ha-1) and 6-year old soil bund stabilized with tree lucerne (1284.25 kg ha-1). When we compare the accumulation and the loss zones, the average grain yield obtained from the accumulation zones (averaged over all the Lessons from Upstream Soil Conservation Measures to Mitigate Soil Erosion and its Impact on Upstream and Downstream Users of the Nile River.Length: pp.170-183ErosionLand degradationSoil conservationBundsWater conservationSoil properties

Comparison of genomic selection models for wheat breeding

Non-Peer Reviewe

Defect chemistry and transport properties of BaxCe0.85M0.15O3-d

The site-incorporation mechanism of M3+ dopants into A2+B4+O3 perovskites controls the overall defect chemistry and thus their transport properties. For charge-balance reasons, incorporation onto the A2+-site would require the creation of negatively charged point defects (such as cation vacancies), whereas incorporation onto the B4+-site is accompanied by the generation of positively charged defects, typically oxygen vacancies. Oxygen-vacancy content, in turn, is relevant to proton-conducting oxides in which protons are introduced via the dissolution of hydroxyl ions at vacant oxygen sites. We propose here, on the basis of x-ray powder diffraction studies, electron microscopy, chemical analysis, thermal gravimetric analysis, and alternating current impedance spectroscopy, that nominally B-site doped barium cerate can exhibit dopant partitioning as a consequence of barium evaporation at elevated temperatures. Such partitioning and the presence of significant dopant concentrations on the A-site negatively impact proton conductivity. Specific materials examined are BaxCe0.85M0.15O3-d (x = 0.85 - 1.20; M = Nd, Gd, Yb). The compositional limits for the maximum A-site incorporation are experimentally determined to be: (Ba0.919Nd0.081)(Ce0.919Nd0.081)O3, (Ba0.974Gd0.026)(Ce0.872Gd0.128)O2.875, and Ba(Ce0.85Yb0.15)O2.925. As a consequence of the greater ability of larger cations to exist on the Ba site, the H2O adsorption and proton conductivities of large-cation doped barium cerates are lower than those of small-cation doped analogs

Nonparametric Identification of Differentiated Products Demand Using Micro Data

We examine identification of differentiated products demand when one has “micro data” linking the characteristics and choices of individual consumers. Our model nests standard specifications featuring rich observed and unobserved consumer heterogeneity as well as product/market-level unobservables that introduce the problem of econometric endogeneity. Previous work establishes identification of such models using marketlevel data and instruments for all prices and quantities. Micro data provides a panel structure that facilitates richer demand specifications and reduces requirements on both the number and types of instrumental variables. We address identification of demand in the standard case in which non-price product characteristics are assumed exogenous, but also cover identification of demand elasticities and other key features when these product characteristics are endogenous and not instrumented. We discuss implications of these results for applied work

Identification in Differentiated Products Markets

Empirical models of demand for — and, often, supply of — differentiated products are widely used in practice, typically employing parametric functional forms and distributions of consumer heterogeneity. We review some recent work studying identification in a broad class of such models. This work shows that parametric functional forms and distributional assumptions are not essential for identification. Rather, identification relies primarily on the standard requirement that instruments be available for the endogenous variables — here, typically, prices and quantities. We discuss the kinds of instruments needed for identification and how the reliance on instruments can be reduced by nonparametric functional form restrictions or better data. We also discuss results on discrimination between alternative models of oligopoly competition