Econometrics Commons^™

The Relationship Between Commuting Habits And Mortality Rates In The United States, Samuel Earl Supplee-Niederman

Graduate Student Theses, Dissertations, & Professional Papers

In recent years, policy makers have invested in public transportation and infrastructure to promote walking and cycling to work. There is also a large body of economic research that has found mortality rates increase during economic expansions. While there has been a number of epidemiological studies that investigate the impact of commuting mode choice on individual health outcomes, there is a lack of research on the aggregate health effects of alternative transportation methods, such as biking, walking, or using public transportation. This paper uses a fixed-effect model to investigate the impact of an increase in total employment on mortality rates, …

A Full Characterization Of Best-Response Functions In The Lottery Colonel Blotto Game, Dan Kovenock, David Rojo Arjona

ESI Working Papers

We fully characterize best-response functions in Colonel Blotto games with lottery contest success functions.

Do You Want To Run A Regression? Nah, I Think I’Ll Dance One Instead: Physicalizing Econometrics, Rachel Lau

Senior Independent Study Theses

This project asked how can one make use of an econometric tool, specifically the regression model, as a source for choreography. Through integrating my research on dance choreography and econometrics, I created a dance piece that demonstrates the numerical relationships of a realized regression equation. The dance was separated into three sections, with each examining an econometric concept. Taking the primary question at hand one step further, this project also explored how this approach to choreography can be generalized so that almost any regression model can be a choreographic tool. Subsequently, a guideline was devised to provide other choreographers various …

Econometric Estimates Of Earth's Transient Climate Sensitivity, Peter C. B. Phillips, Thomas Leirvik, Trude Storelvmo

Research Collection School Of Economics

How sensitive is Earth's climate to a given increase in atmospheric greenhouse gas (GHG) concentrations? This long-standing question in climate science was recently analyzed by dynamic panel data methods using extensive spatio-temporal data of global surface temperatures, solar radiation, and GHG concentrations over the last half century to 2010 (Storelvmo et al, 2016). Those methods revealed that atmospheric aerosol effects masked approximately one-third of the continental warming due to increasing GHG concentrations over this period, thereby implying greater climate sensitivity to GHGs than previously thought. The present study provides regularity conditions and asymptotic theory justifying the use of time series …

Nonstationary Panel Models With Latent Group Structures And Cross-Section Dependence, Wenxin Huang, Sainan Jin, Peter C. B. Phillips, Liangjun Su

Research Collection School Of Economics

This paper proposes a novel Lasso-based approach to handle unobserved parameter heterogeneity and cross-section dependence in nonstationary panel models. In particular, a penalized principal component (PPC) method is developed to estimate group-specific long-run relationships and unobserved common factors and jointly to identify the unknown group membership. The PPC estimators are shown to be consistent under weakly dependent innovation processes. But they suffer an asymptotically non-negligible bias from correlations between the nonstationary regressors and unobserved stationary common factors and/or the equation errors. To remedy these shortcomings we provide three bias-correction procedures under which the estimators are re-centered about zero as both …

On Factor Models With Random Missing: Em Estimation, Inference, And Cross Validation, Liangjun Su, Ke Miao, Sainan Jin

Research Collection School Of Economics

We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross validation-based method to determine the number of factors in factor models with or without missing values and justify its consistency. Simulations demonstrate that our cross validation method is robust to fat tails in the error distribution and …

Specification Tests For Temporal Heterogeneity In Spatial Panel Models With Fixed Effects, Yuhong Xu, Zhenlin Yang

Research Collection School Of Economics

We propose score type tests for testing the existence of temporal heterogeneity in slope and spatial parameters in spatial panel data (SPD) models, allowing for the presence of individual-specific and/or time-specific fixed effects (or in general intercept heterogeneity). The SPD model with spatial lag effect is treated in detail by first considering the model with individual-specific effects only, and then extending it to the model with both individual and time specific effects. Two types of tests (naive and robust) are proposed, and their asymptotic properties are presented. These tests are then fully extended to an SPD model with both spatial …

Three Essays On Nonparametric Hypothesis Testing And Stochastic Frontier Analysis, Taining Wang

Graduate Theses, Dissertations, and Problem Reports (ETD)

The first chapter proposes a nonparametric test of significant variables in the partial derivative of a regression mean function. The test is constructed through a variation based measure of the derivative in the directions of the significant variables, with the derivative estimation through a local polynomial estimator. The test is shown to have the asymptotic null distribution and demonstrated to be consistent. The chapter further proposes a wild-bootstrap test, which exhibits the same null distribution regardless of whether the null is valid or not. Through Monte Carlo studies, the test shows encouraging finite sample performances. Through an empirical application, the …

Money Is More Than Memory, Maria Bigoni, Gabriele Camera, Marco Casari

ESI Working Papers

Impersonal exchange is the hallmark of an advanced society and money is one key institution that supports it. Economic theory regards money as a crude arrangement for monitoring counterparts’ past conduct. If so, then a public record of past actions—or memory—should supersede the function performed by money. This intriguing theoretical postulate remains untested. In an experiment, we show that the suggested functional equivalence between money and memory does not translate into an empirical equivalence: money removed the incentives to free ride, while memory did not. Monetary systems performed a richer set of functions than just revealing past behaviors.

Modeling Interactions Between Risk, Time, And Social Preferences, Mark Schneider

ESI Working Papers

Recent studies have observed systematic interactions between risk, time, and social preferences that constitute violations of `dimensional independence' and are not explained by the leading models of decision making. This note provides a simple approach to modeling such interaction effects while predicting new ones. In particular, we present a model of rational-behavioral preferences that takes the convex combination of `behavioral' System 1 preferences and `rational' System 2 preferences. The model provides a unifying approach to analyzing risk, time, and social preferences, and predicts how these preferences are correlated with reliance on System 1 or System 2 thinking.

A Dual System Model Of Risk And Time Preferences, Mark Schneider

ESI Working Papers

Discounted Expected Utility theory has been a workhorse in economic analysis for over half a century. However, it cannot explain empirical violations of 'dimensional independence' demonstrating that risk interacts with time preference and time interacts with risk preference, nor does it explain present bias or magnitude-dependence in risk and time preferences, or correlations between risk preference, time preference, and cognitive reflection. We demonstrate that these and other anomalies are explained by a dual system model of risk and time preferences that unless models of a rational economic agent, models based on prospect theory, and dual process models of decision making.

Essays On Energy Economics And Environmental Policies, Janak R. Joshi

Economics ETDs

This dissertation contains three distinct empirical chapters in applied energy and environmental economics. Each chapter focuses on a unique set of research questions, methods, and data. The unifying motivation therein concerns the development of renewable or alternative low-carbon energy sources as a policy response to the challenges of climate change mitigation, local and regional environmental quality issues, and energy security concerns. Economic and environmental evaluation of the energy policies coupled with understanding energy use patterns is of paramount importance. Together, the empirical chapters focus on demand, supply, and policy aspects of energy markets in the United States (US).

First, Chapter …

A Snowball's Chance: Debt Snowball Vs. Debt Avalanche, Evan Mcallister

Senior Honors Projects, 2010-2019

Traditional mathematical analysis states that the most efficient way to pay off interest-bearing consumer debt is to pay the individual debts in order from largest to smallest interest rate. In doing this, the debtor will eliminate the largest sources of interest first, thus shortening the overall time-to-pay. This method is known as the “Debt Avalanche.” The “Debt Snowball” method, popularized in large part by investor-author David Ramsey, recommends that consumers pay debts in order from smallest to largest, regardless of interest rate. In this paper, I conduct an empirical analysis of the Federal Reserve’s Survey of Consumer Finance (SCF), calculating …

The Principal Problem With Principal Components Regression, Heidi Margaret Artigue, Gary Smith

Pomona Faculty Publications and Research

Principal components regression (PCR) reduces a large number of explanatory variables down to a small number of principal components. PCR is thought to be more useful, the more numerous the potential explanatory variables. The reality is that a large number of candidate explanatory variables does not make PCR more valuable; instead, it magnifies the failings of PCR.

Using Response Times To Measure Ability On A Cognitive Task, Aleksandr Alekseev

ESI Working Papers

I show how using response times as a proxy for effort coupled with an explicit process-based model can address a long-standing issue of how to separate the effect of cognitive ability on performance from the effect of motivation. My method is based on a dynamic stochastic model of optimal effort choice in which ability and motivation are the structural parameters. I show how to estimate these parameters from the data on outcomes and response times in a cognitive task. In a laboratory experiment, I find that performance on a Digit-Symbol test is a noisy and biased measure of cognitive ability. …

Rhode Island Current Conditions Index -- December 2018, Leonard Lardaro

The Rhode Island Current Conditions Index

No abstract provided.

Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Xin Zheng, Yichong Zhang

Research Collection School Of Economics

This paper studies the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose three estimation methods: (1) the simple quantile regression (QR), (2) the QR with strata fixed effects, and (3) the inverse propensity score weighted QR. For the three estimators, we derive their asymptotic distributions uniformly over a set of quantile indexes and show that the estimator obtained from inverse propensity score weighted QR weakly dominates the other two in terms of efficiency, for a wide range of randomization schemes. For inference, we show that the weighted bootstrap tends to be conservative for methods (1) …

Root-N Consistency Of Intercept Estimators In A Binary Response Model Under Tail Restrictions, Lili Tan, Yichong Zhang

Research Collection School Of Economics

The intercept of the binary response model is irregularly identified when the supports of both the special regressor V and the error term ε are the whole real line. This leads to the estimator of the intercept having potentially a slower than √n convergence rate, which can result in a large estimation error in practice. This paper imposes addition tail restrictions which guarantee the regular identification of the intercept and thus the √n-consistency of its estimator. We then propose an estimator that achieves the √n rate. Finally, we extend our tail restrictions to a full-blown model with endogenous regressors.

Semiparametric Maximum Likelihood Inference For Nonignorable Nonresponse With Callbacks, Zhong Guan, Denis H. Y. Leung, Jing Qin

Research Collection School Of Economics

We model the nonresponse probabilities as logistic functions ofthe outcome variable and other covariates in the survey sampling study withcallback. The identification aspect of this callback model is investigated. Semiparametricmaximum likelihood estimators of the parameters in the responseprobabilities are proposed and studied. As a result, an efficient estimator ofthe mean of the outcome variable is constructed using the estimated responseprobabilities. Moreover, if a regression model for conditional mean of the outcomevariable given some covariate is available, then we can obtain an evenmore efficient estimate of the mean of the outcome variable by fitting the regressionmodel using an adjusted least squares …

Mild-Explosive And Local-To-Mild-Explosive Autoregressions With Serially Correlated Errors, Yiu Lim Lui, Weilin Xiao, Jun Yu

Research Collection School Of Economics

This paper firstly extends the results of Phillips and Magdalinos (2007a) by allowing for anti-persistent errors in mildly explosive autoregressive models. It is shown that the Cauchy asymptotic theory remains valid for the least squares (LS) estimator. The paper then extends the results of Phillips, Magdalinos and Giraitis (2010) by allowing for serially correlated errors of various forms in local-to-mild-explosive autoregressive models. It is shown that the result of smooth transition in the limit theory between local-to-unity and mild-explosiveness remains valid for the LS estimator. Finally, the limit theory for autoregression with intercept is developed.