Bias from ignoring price dispersion in demand estimation

Consumers often pay different prices for the same product bought in the same store at the same time. However, the demand estimation literature has ignored that fact using, instead, aggregate measures such as the “list” or average price. In this paper we show that this will lead to biased price coefficients. Furthermore, we perform simple comparative statics simulation exercises for the logit and random coefficient models. In the “list” price case we find that the bias is larger when discounts are higher, proportion of consumers facing discount prices is higher and when consumers are more unwilling to buy the product so that they almost only do it when facing discount. In the average price case we find that the bias is larger when discounts are higher, proportion of consumers that have access to discount are similar to the ones that do not have access and when consumers willingness to buy is very dependent on idiosyncratic shocks. Also bias is less problematic in the average price case in markets with a lot of bargain deals, so that prices are as good as individual. We conclude by proposing ways that the econometrician can reduce this bias using different information that he may have available.

Adoption of financial technologies: Implications for money demand and monetary policy

In this paper we argue that inventory models are probably not usefulmodels of household money demand because the majority of households does nothold any interest bearing assets. The relevant decision for most people is notthe fraction of assets to be held in interest bearing form, but whether to holdany of such assets at all. The implications of this realization are interesting and important. We find that(a) the elasticity of money demand is very small when the interest rate is small,(b) the probability that a household holds any amount of interest bearing assetsis positively related to the level of financial assets, and (c) the cost ofadopting financial technologies is positively related to age and negatively relatedto the level of education. Unlike the traditional methods of money demand estimation, our methodology allowsfor the estimation of the interest--elasticity at low values of the nominalinterest rate. The finding that the elasticity is very small for interest ratesbelow 5 percent suggests that the welfare costs of inflation are small. At interest rates of 6 percent, the elasticity is close to 0.5. We find thatroughly one half of this elasticity can be attributed to the Baumol--Tobin orintensive margin and half of it can be attributed to the new adopters or extensivemargin. The intensive margin is less important at lower interest rates and moreimportant at higher interest rates.

An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^

The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Environmental Goods

This paper develop and estimates a model of demand estimation for environmental public goods which allows for consumers to learn about their preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. We then estimate the model using data collected for two environmental goods. We find that the predictions of the theoretical exercise that additional experience makes consumers more certain over their preferences in both mean and variance are supported in each case.

How do domestic policies affect the integration of Ethiopian fertiliser markets with world markets?

Frequent shifts in policy on fertiliser markets have occurred in Ethiopia with the aim of facilitating both physical and economic access of farmers to fertiliser. The last shift was the introduction of a monopoly on each stage of the supply chain in 2008. Furthermore, government control of prices and margins as well as stockholding programmes are also present on the markets. This paper evaluates the effect of these policies on the integration of domestic with world markets of fertiliser, using cointegration methods. Time series data of diammonium phosphate (DAP) and urea prices on world, import and retail markets between 1971 and 2012 are used. The findings show high transmission of price signals from world markets to import prices for both DAP and urea. However, between import and retail prices there is no evidence of cointegration for urea, while for DAP full price transmission is concluded. In the retail market, domestic transaction costs associated with storing large volumes of fertiliser act as a buffer between import and retail prices, especially for urea. Therefore, economic benefits could be achieved by reducing the size of stocks and revising the demand estimation process.

Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016 – 2018

O objetivo desta dissertação foi estimar a demanda de tratores agrícolas para o mercado brasileiro no triênio 2016-2018, utilizando-se para isto de técnicas de econometria de séries temporais, neste caso, modelos univariados da classe ARIMA e SARIMA e ou multivariados SARIMAX. Justifica-se esta pesquisa quando se observa a indústria de máquinas agrícolas no Brasil, dados os ciclos econômicos e outros fatores exógenos aos fundamentos econômicos da demanda, onde esta enfrenta muitos desafios. Dentre estes, a estimação de demanda se destaca, pois exerce forte impacto, por exemplo, no planejamento e custo de produção de curto e médio prazo, níveis de inventários, na relação com fornecedores de materiais e de mão de obra local, e por consequência na geração de valor para o acionista. Durante a fase de revisão bibliográfica foram encontrados vários trabalhos científicos que abordam o agronegócio e suas diversas áreas de atuação, porém, não foram encontrados trabalhos científicos publicados no Brasil que abordassem a previsão da demanda de tratores agrícolas no Brasil, o que serviu de motivação para agregar conhecimento à academia e valor ao mercado através deste. Concluiu-se, após testes realizados com diversos modelos que estão dispostos no texto e apêndices, que o modelo univariado SARIMA (15,1,1) (1,1,1) cumpriu as premissas estabelecidas nos objetivos específicos para escolha do modelo que melhor se ajusta aos dados, e foi escolhido então, como o modelo para estimação da demanda de tratores agrícolas no Brasil. Os resultados desta pesquisa apontam para uma demanda de tratores agrícolas no Brasil oscilando entre 46.000 e 49.000 unidades ano entre os anos de 2016 e 2018.

Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

Essays on Machine Learning and Hedonic Models

Thesis (Ph.D.)--University of Washington, 2016-08

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.

Testing symmetry and homogeneity in the almost ideal demand system with co-integrated data using fully modified estimation and the bootstrap

Conventional seemingly unrelated estimation of the almost ideal demand system is shown to lead to small sample bias and distortions in the size of a Wald test for symmetry and homogeneity when the data are co-integrated. A fully modified estimator is developed in an attempt to remedy these problems. It is shown that this estimator reduces the small sample bias but fails to eliminate the size distortion.. Bootstrapping is shown to be ineffective as a method of removing small sample bias in both the conventional and fully modified estimators. Bootstrapping is effective, however, as a method of removing. size distortion and performs equally well in this respect with both estimators.

Estimation of random coefficients logit demand models: an application to the Brazilian fixed income fund market

Estimation of demand and supply in differentiated products markets is a central issue in Empirical Industrial Organization and has been used to study the effects of taxes, merges, introduction of new goods, market power, among others. Logit and Random Coefficients Logit are examples of demand models used to study these effects. For the supply side it is generally supposed a Nash equilibrium in prices. This work presents a detailed discussion of these models of demand and supply as well as the procedure for estimation. Lastly, is made an application to the Brazilian fixed income fund market.

Aggregate vs disaggregate data analysis: a paradox in the estimation of money demand function of Japan under the low interest rate policy

We investigate the issue of whether there was a stable money demand function for Japan in 1990's using both aggregate and disaggregate time series data. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of liquidity trapo Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We also conduct simulation analysis to show that when heterogeneity among micro units is present. The prediction of aggregate outcomes, using aggregate data is less accurate than the prediction based on micro equations. Moreover. policy evaluation based on aggregate data can be grossly misleading.