Estimating Fully Observed Recursive Mixed-Process Models with cmp

At the heart of many econometric models is a linear function and a normal error. Examples include the classical small-sample linear regression model and the probit, ordered probit, multinomial probit, Tobit, interval regression, and truncateddistribution regression models. Because the normal distribution has a natural multidimensional generalization, such models can be combined into multi-equation systems in which the errors share a multivariate normal distribution. The literature has historically focused on multi-stage procedures for estimating mixed models, which are more efficient computationally, if less so statistically, than maximum likelihood (ML). But faster computers and simulated likelihood methods such as the Geweke, Hajivassiliou, and Keane (GHK) algorithm for estimating higherdimensional cumulative normal distributions have made direct ML estimation practical. ML also facilitates a generalization to switching, selection, and other models in which the number and types of equations vary by observation. The Stata module cmp fits Seemingly Unrelated Regressions (SUR) models of this broad family. Its estimator is also consistent for recursive systems in which all endogenous variables appear on the right-hand-sides as observed. If all the equations are structural, then estimation is full-information maximum likelihood (FIML). If only the final stage or stages are, then it is limited-information maximum likelihood (LIML). cmp can mimic a dozen built-in Stata commands and several user-written ones. It is also appropriate for a panoply of models previously hard to estimate. Heteroskedasticity, however, can render it inconsistent. This paper explains the theory and implementation of cmp and of a related Mata function, ghk2(), that implements the GHK algorithm.econometrics, cmp, GHK algorithm, seemingly unrelated regressions

ANALYZING FRESH VEGETABLE CONSUMPTION FROM HOUSEHOLD SURVEY DATA

To analyze fresh vegetable consumption using household survey data, the tobit model and a more flexible parameterization to the tobit model - the "double hurdle" model - were considered. Based on the likelihood ratio test, the tobit model was rejected against the "double hurdle" specification. Moreover, the results suggest that the tobit model underestimated the impact of the explanatory variables on fresh vegetable expenditures. Other results indicate that total food expenditures (proxy for income), age, household composition, sex, race, marital status, urbanization, region, and seasonality are all important determinants of fresh vegetable expenditures.Food Consumption/Nutrition/Food Safety,

A Bayesian Model of Sample Selection with a Discrete Outcome Variable

Relatively few published studies apply Heckman’s (1979) sample selection model to the case of a discrete endogenous variable and those are limited to a single outcome equation. However, there are potentially many applications for this model in health, labor and financial economics. To fill in this theoretical gap, I extend the Bayesian multivariate probit setup of Chib and Greenberg (1998) into a model of non-ignorable selection that can handle multiple selection and discrete-continuous outcome equations. The first extension of the multivariate probit model in Chib and Greenberg (1998) allows some of the outcomes to be missing. In addition, I use Cholesky factorization of the variance matrix to avoid the Metropolis-Hastings algorithm in the Gibbs sampler. Finally, using artificial data I show that the model is capable of retrieving the parameters used in the data-generating process and also that the resulting Markov Chain passes all standard convergence tests.Markov Chain Monte Carlo; sample selection; multivariate probit

Revenue Impacts of MPP Branded Funds: A Firm-Level Analysis

The USDA's Market Access Program (formerly Market Promotion Program) recently underwent a major change to redirect all branded products export promotion funds to small domestic firms and cooperatives. The redirection responded to criticisms by the General Accounting Office of past allocations of branded products export promotion funds to large, experienced exporters. This study uses a firm-level analysis to examine whether firm size and export experience matter in how effectively firms use the promotion funds to increase their revenues. The results support neither the GAO criticisms nor the recent program redirection.International Relations/Trade,

Econometric essays on specification and estimation of demand systems

This dissertation focuses on two research themes related to econometric estimation of linear almost ideal demand systems (LAIDS) for U.S. meats. The first theme addresses whether nonstationarity (unit-roots and cointegration) contributes to a dynamic specification of LAIDS models. The results of the effect of nonstationarity are reported in two case studies. The second theme explores the relationship between age and household size with budget shares to specify semiparametric LAIDS model. The results are reported in a third case study that compares parametric and semiparametric models estimates of price and expenditure elasticities. The first case study conducts a comparative analysis of elasticity estimates from static and dynamic LAIDS models. Historical meat consumption data (1975:1-2002:4) for beef, pork and poultry products were used. Hylleberg et al. (1990) seasonal unit roots tests were conducted. Unit roots and cointegration analysis lead to the specification of an ECM of the Engle-Granger type for the LAIDS model. Marshallian and compensated elasticities were generated from the static and dynamic LAIDS models. The study found some model differences in elasticity estimates and rejected homogeneity in the dynamic model. The second case study evaluates the forecasting performance of static and dynamic LAIDS models. Forecast evaluation was based on mean square error (MSE) criteria and recently developed MSE-tests. The study found ECM-LAIDS model performs uniformly better under all forecasting horizons for the beef equation. However, in the case of the pork equation the static model performed better in one-step-ahead and two-step-ahead forecasting horizons while the dynamic model was superior in the three-step-ahead and four-step-ahead forecasting horizons using MSE comparisons. In testing, only the two-steps ahead was superior for pork. The third case study specifies a semiparametric LAIDS model that maintains the linearity assumption of prices and total expenditures and allows nonparametric effects of age and household size. 2003 U.S. Consumer Expenditure Survey data for four meat products (beef, pork, poultry and seafood) were used in the study. Model fit and elasticity estimates revealed negligible differences exist between parametric and semiparametric models

The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence

The most-noted studies on the impact of microcredit on households are based on a survey fielded in Bangladesh in the 1990s. Contradictions among them have produced lasting controversy and confusion. Pitt and Khandker (PK, 1998) apply a quasi-experimental design to 1991–92 data; they conclude that microcredit raises household consumption, especially when lent to women. Khandker (2005) applies panel methods using a 1999 resurvey; he concurs and extrapolates to conclude that microcredit helps the extremely poor even more than the moderately poor. But using simpler estimators than PK, Morduch (1999) finds no impact on the level of consumption in the 1991–92 data, even as he questions PK’s identifying assumptions. He does find evidence that microcredit reduces consumption volatility. Partly because of the sophistication of PK’s Maximum Likelihood estimator, the conflicting results were never directly confronted and reconciled. We end the impasse. A replication exercise shows that all these studies’ evidence for impact is weak. As for PK’s headline results, we obtain opposite signs. But we do not conclude that lending to women does harm. Rather, all three studies appear to fail in expunging endogeneity. We conclude that for non-experimental methods to retain a place in the program evaluator’s portfolio, the quality of the claimed natural experiments must be high and demonstrated.microcredit; impact evaluation; Grameen Bank; Bangladesh; replication; mixed-process models

Crash Risk Reduction at Signalized Intersections Using Longitudinal Data

This study extends the previous work of Burkey and Obeng (2004) that examined the impact of red light cameras on the type and severity of crashes at signalized intersections in Greensboro, NC. The extension takes the following form. First, we extend the data to cover 57 months, and to include demographics, technology variables, the condition of a driver at the time of the crash, vehicle characteristics, land use and visual obstruction. Second, instead of examining the impact of red light cameras, we focus on identifying the determinants of crash severity, two-vehicle crashes, and property damage costs. The major findings are that the safety impacts of seatbelt use outweigh the impacts of airbags deploying because the latter tends to increase evident injuries and property damage costs, while the former reduces these injuries. We also find that head-on collisions and under rides increase evident injuries. For two-vehicle crashes, we find that the risk of severe injuries increases in pickup-pickup crashes and SUV-pickup crashes, while the risk of possible injuries increases in car-truck crashes. For property damage costs, we found the condition of the driver at the time of the crash (i.e., illness, impaired, medical condition, driver falling asleep, driver apparently normal) to be important determinants in increasing these costs. The types of accidents that we found to increase property damage costs are running into a fixed object and under rides. Finally, we found that property damage costs of crashes are low where the land uses are commercial and institutional suggesting that the accidents that occur in these areas are minor.longitudinal data; accidents; intersections

Novel Regression Models For High-Dimensional Survival Analysis

Survival analysis aims to predict the occurrence of specific events of interest at future time points. The presence of incomplete observations due to censoring brings unique challenges in this domain and differentiates survival analysis techniques from other standard regression methods. In this thesis, we propose four models to deal with the high-dimensional survival analysis. Firstly, we propose a regularized linear regression model with weighted least-squares to handle the survival prediction in the presence of censored instances. We employ the elastic net penalty term for inducing sparsity into the linear model to effectively handle high-dimensional data. As opposed to the existing censored linear models, the parameter estimation of our model does not need any prior estimation of survival times of censored instances. The second model we proposed is a unified model for regularized parametric survival regression for an arbitrary survival distribution. We employ a generalized linear model to approximate the negative log-likelihood and use the elastic net as a sparsity-inducing penalty to effectively deal with high-dimensional data. The proposed model is then formulated as a penalized iteratively reweighted least squares and solved using a cyclical coordinate descent-based method.Considering the fact that the popularly used survival analysis methods such as Cox proportional hazard model and parametric survival regression suffer from some strict assumptions and hypotheses that are not realistic in many real-world applications. we reformulate the survival analysis problem as a multi-task learning problem in the third model which predicts the survival time by estimating the survival status at each time interval during the study duration. We propose an indicator matrix to enable the multi-task learning algorithm to handle censored instances and incorporate some of the important characteristics of survival problems such as non-negative non-increasing list structure into our model through max-heap projection. And the proposed formulation is solved via an Alternating Direction Method of Multipliers (ADMM) based algorithm. Besides above three methods which aim at solving standard survival prediction problem, we also propose a transfer learning model for survival analysis. During our study, we noticed that obtaining sufficient labeled training instances for learning a robust prediction model is a very time consuming process and can be extremely difficult in practice. Thus, we proposed a Cox based model which uses the L2,1-norm penalty to encourage source predictors and target predictors share similar sparsity patterns and hence learns a shared representation across source and target domains to improve the model performance on the target task. We demonstrate the performance of the proposed models using several real-world high-dimensional biomedical benchmark datasets and our experimental results indicate that our model outperforms other state-of-the-art related competing methods and attains very competitive performance on various datasets

Effect of Nutrition Merchandising and Consumer Preferences on Willingness to Pay for Local Tomatoes and Strawberries in Kentucky and Ohio

This project investigates the impacts of nutrition merchandising on consumers’ willingness to pay for local tomatoes and strawberries. The data come from survey of Kentucky and Ohio residents in June 2011. Two thousand one hundred twelve individuals from Kentucky and Ohio were surveyed, to find out the impact of selfawareness of health benefits and health benefits information on their willingness to pay. The consumers were offered one of the three survey versions. The versions varied by how much nutrition information was provided to the consumer related to both strawberries and tomatoes – otherwise identical. A had the most, B had text only, and C omitted any nutritional benefits. This nutrition preamble was offered just before doing a payment card willingness-to-pay experiment. Standard demographic data were also included. The goal of the study was to see if and in what way the provision (or nonprovision) of this information, as well as consumers’ own knowledge of nutritional benefits of local foods, their beliefs and lifestyle influenced their willingness to pay for these local products

Labor supply models: unobserved heterogeneity, nonparticipation and dynamics

This chapter is concerned with the identification and estimation of models of labor supply. The focus is on the key issues that arise from unobserved heterogeneity, nonparticipation and dynamics. We examine the simple ‘static’ labor supply model with proportional taxes and highlight the problems surrounding nonparticipation and missing wages. The difference in differences approach to estimation and identification is developed within the context of the labour supply model. We also consider the impact of incorporating nonlinear taxation and welfare programme participation. Family labor supply is looked at from botht e unitary and collective persepctives. Finally we consider intertemporal models focusing on the difficulties that arise with participation and heterogeneity