The Experts below are selected from a list of 68346 Experts worldwide ranked by ideXlab platform

Jeanpierre Dube - One of the best experts on this subject based on the ideXlab platform.

  • improving the numerical performance of static and dynamic aggregate discrete choice random coefficients Demand Estimation
    Econometrica, 2012
    Co-Authors: Jeanpierre Dube, Jeremy T Fox
    Abstract:

    The widely used estimator of Berry, Levinsohn, and Pakes (1995 )p roduces estimates of consumer preferences from a discrete-choice Demand model with random coefficients, market-level Demand shocks, and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP’s estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast Estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking Demand models where the Bellman equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization. For static BLP, the constrained optimization approach can be as much as ten to forty times faster for large-dimensional problems with many markets.

  • improving the numerical performance of blp static and dynamic discrete choice random coefficients Demand Estimation
    Social Science Research Network, 2009
    Co-Authors: Jeanpierre Dube, Jeremy T Fox
    Abstract:

    The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces consistent instrumental variables estimates of consumer preferences from a discrete-choice Demand model with random coecients, market-level Demand shocks and potentially endogenous regressors (prices). The estimator is computationally intensive and dicult to program, largely because a system of market share equations must be repeatedly numerically inverted. We provide numerical theory results that characterize the properties of typical nested fixed-point implementations. We use these results to discuss several problems with typical computational implementations and, in particular, cases which can lead to incorrect parameter estimates. As a solution, we introduce a new computational formulation of the estimator that re-casts Estimation as a mathematical programming problem with equilibrium constraints (MPEC). We demonstrate through Monte Carlo experiments that this alternative formulation converges to correct parameter estimates. We also discuss estimating static BLP using maximum likelihood instead of GMM. Finally, our method is particularly attractive for forward-looking Demand models where both Bellman’s equations and the market share equations must be repeatedly solved.

  • improving the numerical performance of blp static and dynamic discrete choice random coefficients Demand Estimation
    National Bureau of Economic Research, 2009
    Co-Authors: Jeanpierre Dube, Jeremy T Fox
    Abstract:

    The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces estimates of consumer preferences from a discrete-choice Demand model with random coefficients, market-level Demand shocks and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP's estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast Estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking Demand models where Bellman's equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization.

Jeremy T Fox - One of the best experts on this subject based on the ideXlab platform.

  • improving the numerical performance of static and dynamic aggregate discrete choice random coefficients Demand Estimation
    Econometrica, 2012
    Co-Authors: Jeanpierre Dube, Jeremy T Fox
    Abstract:

    The widely used estimator of Berry, Levinsohn, and Pakes (1995 )p roduces estimates of consumer preferences from a discrete-choice Demand model with random coefficients, market-level Demand shocks, and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP’s estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast Estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking Demand models where the Bellman equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization. For static BLP, the constrained optimization approach can be as much as ten to forty times faster for large-dimensional problems with many markets.

  • improving the numerical performance of blp static and dynamic discrete choice random coefficients Demand Estimation
    Social Science Research Network, 2009
    Co-Authors: Jeanpierre Dube, Jeremy T Fox
    Abstract:

    The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces consistent instrumental variables estimates of consumer preferences from a discrete-choice Demand model with random coecients, market-level Demand shocks and potentially endogenous regressors (prices). The estimator is computationally intensive and dicult to program, largely because a system of market share equations must be repeatedly numerically inverted. We provide numerical theory results that characterize the properties of typical nested fixed-point implementations. We use these results to discuss several problems with typical computational implementations and, in particular, cases which can lead to incorrect parameter estimates. As a solution, we introduce a new computational formulation of the estimator that re-casts Estimation as a mathematical programming problem with equilibrium constraints (MPEC). We demonstrate through Monte Carlo experiments that this alternative formulation converges to correct parameter estimates. We also discuss estimating static BLP using maximum likelihood instead of GMM. Finally, our method is particularly attractive for forward-looking Demand models where both Bellman’s equations and the market share equations must be repeatedly solved.

  • improving the numerical performance of blp static and dynamic discrete choice random coefficients Demand Estimation
    National Bureau of Economic Research, 2009
    Co-Authors: Jeanpierre Dube, Jeremy T Fox
    Abstract:

    The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces estimates of consumer preferences from a discrete-choice Demand model with random coefficients, market-level Demand shocks and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP's estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast Estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking Demand models where Bellman's equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization.

  • evaluating wireless carrier consolidation using semiparametric Demand Estimation published quantitative marketing and economics
    2008
    Co-Authors: Patrick Bajari, Jeremy T Fox, Stephen P Ryan
    Abstract:

    The US mobile phone service industry has dramatically consolidated over the last two decades. One justification for consolidation is that merged firms can provide consumers with larger coverage areas at lower costs. We estimate the willingness to pay for national coverage to evaluate this justification for past consolidation. As market level quantity data are not publicly available, we devise an econometric procedure that allows us to estimate the willingness to pay using market share ranks collected from the popular online retailer Amazon. Our semiparametric maximum score estimator controls for consumers’ heterogeneous preferences for carriers, handsets and minutes of calling time. We find that national coverage is strongly valued by consumers, providing an efficiency justification for across-market mergers. The methods we propose can estimate Demand for other products using data from online retailers where product ranks, but not quantities, are observed.

Patrick Bajari - One of the best experts on this subject based on the ideXlab platform.

  • Demand Estimation with machine learning and model combination
    Social Science Research Network, 2015
    Co-Authors: Patrick Bajari, Denis Nekipelov, Stephen Ryan, Miaoyu Yang
    Abstract:

    We survey and apply several techniques from the statistical and computer science literature to the problem of Demand Estimation. We derive novel asymptotic properties for several of these models. To improve out-of-sample prediction accuracy and obtain parametric rates of convergence, we propose a method of combining the underlying models via linear regression. Our method has several appealing features: it is robust to a large number of potentially-collinear regressors; it scales easily to very large data sets; the machine learning methods combine model selection and Estimation; and the method can flexibly approximate arbitrary non-linear functions, even when the set of regressors is high dimensional and we also allow for fixed effects. We illustrate our method using a standard scanner panel data set to estimate promotional lift and find that our estimates are considerably more accurate in out of sample predictions of Demand than some commonly used alternatives. While Demand Estimation is our motivating application, these methods are likely to be useful in other microeconometric problems.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

  • Demand Estimation with machine learning and model combination
    National Bureau of Economic Research, 2015
    Co-Authors: Patrick Bajari, Denis Nekipelov, Stephen Ryan, Miaoyu Yang
    Abstract:

    We survey and apply several techniques from the statistical and computer science literature to the problem of Demand Estimation. We derive novel asymptotic properties for several of these models. To improve out-of-sample prediction accuracy and obtain parametric rates of convergence, we propose a method of combining the underlying models via linear regression. Our method has several appealing features: it is robust to a large number of potentially-collinear regressors; it scales easily to very large data sets; the machine learning methods combine model selection and Estimation; and the method can flexibly approximate arbitrary non-linear functions, even when the set of regressors is high dimensional and we also allow for fixed effects. We illustrate our method using a standard scanner panel data set to estimate promotional lift and find that our estimates are considerably more accurate in out of sample predictions of Demand than some commonly used alternatives. While Demand Estimation is our motivating application, these methods are likely to be useful in other microeconometric problems.

  • a dynamic model of housing Demand Estimation and policy implications a dynamic model of housing Demand
    International Economic Review, 2013
    Co-Authors: Patrick Bajari, Phoebe Chan, Dirk Krueger, Daniel Miller
    Abstract:

    Using data from the Panel Study of Income Dynamics (PSID) we specify, estimate and simulate a dynamic structural model of housing Demand. Our model generalizes previous applied econometric work by incorporating realistic features of the housing market including non-convex adjustment costs from buying and selling a home, credit constraints from minimum downpayment requirements and uncertainty about the evolution of incomes and home prices. We argue that these features are critical for capturing salient features of housing Demand observed in the PSID. After estimating the model we use it to simulate how consumer behavior responds to house price and income declines as well as tightening credit. These experiments are motivated by the U.S. recession starting in December of 2007 that saw large falls in home prices, large negative income shocks for many households and tightening credit standards. In the short run, relatively few households adjust their housing stock. Households respond instead by reducing non-housing consumption and reducing wealth because they wish to avoid losing their home and the associated adjustment costs. Households that adjust in the short run are those hit with a series of bad shocks, such as a negative income shock and a home price decline. A larger proportion of households do adjust their consumption in the long run, increasing their housing stock since housing is less expensive. However, such changes may occur several years after the shocks listed above.

  • a dynamic model of housing Demand Estimation and policy implications
    International Economic Review, 2013
    Co-Authors: Patrick Bajari, Phoebe Chan, Dirk Krueger, Daniel Miller
    Abstract:

    In the U.S., macroeconomic policy makers are concerned about how consumers will respond to falling incomes, nominal home prices, falling income, rising mortgage interest rates and tightening credit standards. In order to address these questions, we estimate and simulate a dynamic structural model of housing Demand. In the model, consumers maximize expected discounted lifetime utility from housing services and a composite consumption good. The model allows for realistic features of the housing market including non-convex adjustment costs from buying and selling a home and credit constraints from minimum downpayment requirements. We use the forward simulation procedure of Bajari, Benkard and Levin (2007) to estimate the structural parameters, especially the elasticity of substitution between consumption and housing services, using data from the Panel Study of Income Dynamics. Given the estimated model parameters, we simulate the partial equilibrium consumption and housing and

  • a dynamic model of housing Demand Estimation and policy implications
    National Bureau of Economic Research, 2010
    Co-Authors: Patrick Bajari, Phoebe Chan, Dirk Krueger, Daniel Miller
    Abstract:

    Using data from the Panel Study of Income Dynamics (PSID) we specify, estimate and simulate a dynamic structural model of housing Demand. Our model generalizes previous applied econometric work by incorporating realistic features of the housing market including non-convex adjustment costs from buying and selling a home, credit constraints from minimum downpayment requirements and uncertainty about the evolution of incomes and home prices. We argue that these features are critical for capturing salient features of housing Demand observed in the PSID. After estimating the model we use it to simulate how consumer behavior responds to house price and income declines as well as tightening credit. These experiments are motivated by the U.S. recession starting in December of 2007 that saw large falls in home prices, large negative income shocks for many households and tightening credit standards. In the short run, relatively few households adjust their housing stock. Households respond instead by reducing non-housing consumption and reducing wealth because they wish to avoid losing their home and the associated adjustment costs. Households that adjust in the short run are those hit with a series of bad shocks, such as a negative income shock and a home price decline. A larger proportion of households do adjust their consumption in the long run, increasing their housing stock since housing is less expensive. However, such changes may occur several years after the shocks listed above.

Hani S Mahmassani - One of the best experts on this subject based on the ideXlab platform.

  • day to day learning framework for online origin destination Demand Estimation and network state prediction
    Transportation Research Record, 2019
    Co-Authors: Eunhye Kim, Hani S Mahmassani, Haleh Aleahmad, Marija Ostojic
    Abstract:

    Origin–destination (O–D) Demand is a critical component in both online and offline dynamic traffic assignment (DTA) systems. Recent advances in real-time DTA applications in large networks call for...

  • data mining and pattern matching for dynamic origin destination Demand Estimation improving online network traffic prediction
    Transportation Research Record, 2015
    Co-Authors: Ying Chen, Hani S Mahmassani, Zihan Hong
    Abstract:

    Historical traffic data are widely used in the Estimation of origin–destination (O-D) Demand patterns in simulation-based dynamic traffic assignment models, in the prediction of traffic states, and as a basis for defining traffic management scenarios. This study investigated the determination of historical traffic patterns by applying a classical clustering algorithm to a very large data set of sensor observations over an extended period and identified appropriate patterns for use in the application of traffic Estimation and prediction systems. Systematic identification of similarity and dissimilarity of traffic flow data can lead to a systematic process for defining critical Demand scenarios for traffic state prediction. The objective was to explore the impact of various Demand scenarios on real-time traffic Estimation and prediction. A detailed procedure for clustering traffic flow data that is based on the K-means clustering algorithm is presented. The procedure was applied to a subnetwork in Salt Lake...

  • dynamic origin destination Demand Estimation with multiday link traffic counts for planning applications
    Transportation Research Record, 2003
    Co-Authors: Xuesong Zhou, Hani S Mahmassani
    Abstract:

    A dynamic origin-destination Demand Estimation model for planning applications with real-time link counts from multiple days is presented. Based on an iterative bilevel Estimation framework, the upper-level problem is to minimize both the deviation between estimated link flows and real-time link counts and the deviation between estimated time-dependent Demand and given historical static Demand. These two types of deviations are combined into a weighted objective function, where the weighting value is determined by an interactive approach to obtain the best compromise solution. The single-day formulation is further extended to use link counts from multiple days to estimate the variation in traffic Demand over multiple days. A case study based on the Irvine test bed network is conducted to illustrate the methodology and estimate day-to-day Demand patterns. The application illustrates considerable benefits in analyzing the Demand dynamics with multiday data.

Xuesong Zhou - One of the best experts on this subject based on the ideXlab platform.

  • an information theoretic sensor location model for traffic origin destination Demand Estimation applications
    Transportation Science, 2010
    Co-Authors: Xuesong Zhou, George F List
    Abstract:

    To design a transportation sensor network, the decision maker needs to determine what sensor investments should be made, as well as when, how, where, and with what technologies. This paper focuses on locating a limited set of traffic counting stations and automatic vehicle identification (AVI) readers in a network, so as to maximize the expected information gain for the subsequent origin-destination (OD) Demand Estimation problem. The proposed sensor design model explicitly takes into account several important error sources in traffic OD Demand Estimation, such as the uncertainty in historical Demand information, sensor measurement errors, as well as approximation errors associated with link proportions. Based on a mean square measure, this paper derives analytical formulations to describe Estimation variance propagation for a set of linear measurement equations. A scenario-based (SB) stochastic optimization procedure and a beam search algorithm are developed to find suboptimal point and point-to-point sensor locations subject to budget constraints. This paper also provides a number of illustrative examples to demonstrate the effectiveness of the proposed methodology.

  • dynamic origin destination Demand Estimation with multiday link traffic counts for planning applications
    Transportation Research Record, 2003
    Co-Authors: Xuesong Zhou, Hani S Mahmassani
    Abstract:

    A dynamic origin-destination Demand Estimation model for planning applications with real-time link counts from multiple days is presented. Based on an iterative bilevel Estimation framework, the upper-level problem is to minimize both the deviation between estimated link flows and real-time link counts and the deviation between estimated time-dependent Demand and given historical static Demand. These two types of deviations are combined into a weighted objective function, where the weighting value is determined by an interactive approach to obtain the best compromise solution. The single-day formulation is further extended to use link counts from multiple days to estimate the variation in traffic Demand over multiple days. A case study based on the Irvine test bed network is conducted to illustrate the methodology and estimate day-to-day Demand patterns. The application illustrates considerable benefits in analyzing the Demand dynamics with multiday data.