Social and Behavioral Sciences Commons^™

The Estimation Of Production Functions With Monetary Values, Jesus Felipe, John Mccombie, Aashish Mehta

Angelo King Institute for Economic and Business Studies (AKI)

For decades, the literature on the estimation of production functions has focused on the elimination of endogeneity biases through different estimation procedures to obtain the correct factor elasticities and other relevant parameters. Theoretical discussions of the problem correctly assume that production functions are relationships among physical inputs and output. However, in practice, they are most often estimated using deflated monetary values for output (value added or gross output) and capital. This introduces two additional problems—an errors-invariables problem, and a tendency to recover the factor shares in value added instead of their elasticities. The latter problem derives from the fact that …

The Study Of Followers In Leadership Research: A Systematic And Critical Review, Burak Oc, Kraivin Chintakananda, Michael Ramsay Bashshur, David V. Day

Research Collection Lee Kong Chian School Of Business

Despite the significant amount of existing research examining the relationship of follower-related factors with leadership outcomes, there is no systematic, critical review that integrates and helps leadership scholars make sense of this rapidly growing body of research. To address this gap in the literature, we first briefly discuss the leading perspectives explaining the role of followers in leadership. Next, we identify and discuss the most frequently studied theoretical narratives explaining the relationship between follower-related predictors and leadership outcomes. Because theoretical arguments generally make causal claims, we identify and examine how methodological concerns including power analysis, multicollinearity, and endogeneity might prevent …

A General Limit Theory For Nonlinear Functionals Of Nonstationary Time Series, Qiying Wang, Peter C. B. Phillips

Cowles Foundation Discussion Papers

Limit theory is provided for a wide class of covariance functionals of
a nonstationary process and stationary time series. The results are relevant
to estimation and inference in nonlinear nonstationary regressions that involve unit root, local unit root or fractional processes and they include both parametric and nonparametric regressions. Self normalized versions of these
statistics are considered that are useful in inference. Numerical evidence reveals a strong bimodality in the finite sample distributions that persists for very large sample sizes although the limit theory is Gaussian. New self normalized versions are introduced that deliver improved approximations.

Crime, Crisis And Economic Growth: An Investigation Of Socio-Economic Determinants Of Crimes In The Indian States, Ankita Thapa

Masters Theses

This paper investigates the impact of socio-economic conditions on five major crime heads from 2001-2019 using a panel data set for the Indian states. The paper focus on the great recession of 2008-09, economic growth of the states, and deterrence variables. The paper employed two estimation procedures: panel Fixed-Effect and two-stage least square-fixed effect (2SLS-FE). The 2SLS-FE is preferred over the fixed effect method, where poverty is treated as an endogenous variable with higher education and social sector expenditure as instrumental variables. A dummy variable is used for the period of the great recession. A square of state GDP per …

Network Competition And Team Chemistry In The Nba, William C. Horrace, Hyunseok Jung, Shane Sanders

Center for Policy Research

We consider a heterogeneous social interaction model where agents interact with peers within their own network but also interact with agents across other (non-peer) networks. To address potential endogeneity in the networks, we assume that each network has a central planner who makes strategic network decisions based on observable and unobservable characteristics of the peers in her charge. The model forms a simultaneous equation system that can be estimated by Quasi-Maximum Likelihood. We apply a restricted version of our model to data on National Basketball Association games, where agents are players, networks are individual teams organized by coaches, and competition …

Nonlinear Cointegrating Power Function Regression With Endogeneity, Zhishui Hu, Peter C.B. Phillips, Qiying Wang

Cowles Foundation Discussion Papers

This paper develops an asymptotic theory for nonlinear cointegrating power function regression. The framework extends earlier work on the deterministic trend case and allows for both endogeneity and heteroskedasticity, which makes the models and inferential methods relevant to many empirical economic and financial applications, including predictive regression. Accompanying the asymptotic theory of nonlinear regression, the paper establishes some new results on weak convergence to stochastic integrals that go beyond the usual semi-martingale structure and considerably extend existing limit theory, complementing other recent findings on stochastic integral asymptotics. The paper also provides a general framework for extremum estimation limit theory that …

Inference And Specification Testing In Threshold Regression With Endogeneity, Ping Yu, Qin Liao, Peter C.B. Phillips

Cowles Foundation Discussion Papers

We propose three new methods of inference for the threshold point in endogenous threshold regression and two specification tests designed to assess the presence of endogeneity and threshold effects without necessarily relying on instrumentation of the covariates. The first inferential method is a parametric two-stage least squares method and is suitable when instruments are available. The second and third methods are based on smoothing the objective function of the integrated difference kernel estimator in different ways and these methods do not require instrumentation. All three methods are applicable irrespective of endogeneity of the threshold variable. The two specification tests are …

A Time-Varying True Individual Effects Model With Endogenous Regressors, Levent Kutlu, Kien C. Tran, Mike G. Tsionas

Economics and Finance Faculty Publications and Presentations

We propose a fairly general individual effects stochastic frontier model, which allows both heterogeneity and inefficiency to change over time. Moreover, our model handles the endogeneity problems if either at least one of the regressors or one-sided error term is correlated with the two-sided error term. Our Monte Carlo experiments show that our estimator performs well. We employed our methodology to the US banking data and found a negative relationship between return on revenue and cost efficiency. Estimators ignoring time-varying heterogeneity or endogeneity did not perform well and gave very different estimates compared to our estimator.

Model And Analysis Of Labor Supply For Ride-Sharing Platforms In The Presence Of Sample Self-Selection And Endogeneity, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Computing and Information Systems

With the popularization of ride-sharing services, drivers working as freelancers on ride-sharing platforms can design their schedules flexibly. They make daily decisions regard- ing whether to participate in work, and if so, how many hours to work. Factors such as hourly income rate affect both the participation decision and working-hour decision, and evaluation of the impacts of hourly income rate on labor supply becomes important. In this paper, we propose an econometric framework with closed-form measures to estimate both the participation elasticity (i.e., extensive margin elasticity) and working-hour elasticity (i.e., intensive margin elasticity) of labor supply. We model the sample …

Attribute Sentiment Scoring With Online Text Reviews: Accounting For Language Structure And Missing Attributes, Ishita Chakraborty, Minkyung Kim, K. Sudhir

Cowles Foundation Discussion Papers

The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop …

Attribute Sentiment Scoring With Online Text Reviews : Accounting For Language Structure And Attribute Self-Selection, Ishita Chakraborty, Minkyung Kim, K. Sudhir

Cowles Foundation Discussion Papers

The authors address two novel and significant challenges in using online text reviews to obtain attribute level ratings. First, they introduce the problem of inferring attribute level sentiment from text data to the marketing literature and develop a deep learning model to address it. While extant bag of words based topic models are fairly good at attribute discovery based on frequency of word or phrase occurrences, associating sentiments to attributes requires exploiting the spatial and sequential structure of language. Second, they illustrate how to correct for attribute self-selection—reviewers choose the subset of attributes to write about—in metrics of attribute level …

Attribute Sentiment Scoring With Online Text Reviews: Accounting For Language Structure And Missing Attributes, Ishita Chakraborty, Minkyung Kim, K. Sudhir

Cowles Foundation Discussion Papers

The authors address two significant challenges in using online text reviews to obtain finegrained attribute level sentiment ratings. First, in contrast to methods that rely on word frequency, they develop a deep learning convolutional-LSTM hybrid model to account for language structure. The convolutional layer accounts for spatial structure (adjacent word groups or phrases) and LSTM accounts for sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in constructing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based …

Structural Changes In Heterogeneous Panels With Endogenous Regressors, Badi Baltagi, Qu Feng, Chihwa Kao

Center for Policy Research

This paper extends Pesaran (2006) common correlated e¤ects (CCE) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure. Since endogenous regressors and structural breaks are often encountered in empirical studies with large panels, this extension makes the Pesaran’s (2006) CCE approach empirically more appealing. In addition to allowing for slope heterogeneity and cross-sectional dependence, we find that Pesaran’s CCE approach is also valid when dealing with unobservable factors in the presence of endogenous regressors and structural changes in slopes and error factor loadings. This is supported by Monte Carlo …

Inference In Partially Identified Panel Data Models With Interactive Fixed Effects, Shengjie Hong, Liangjun Su, Yaqi Wang

Research Collection School Of Economics

This paper develops methods for statistical inferences in a partially identified nonparametric panel data model with endogeneity and interactive fixed effects. We consider the case where the number of cross-sectional units (N) is large and the number of time series periods (T).as well as the number of unobserved common factors (R) are fixed. Under some normalization rules, wecan concentrateout thelarge dimen-sional parameter vector of factor loadings and specify a set of conditional moment restriction that are involved with only the finite dimensional factor parameters along with the infinite dimensional nonpara-metric component. For a conjectured restriction on the parameter, we consider …

Essays On Health, Healthcare, Job Insecurity And Health Outcomes, Ichiro Nakamoto

USF Tampa Graduate Theses and Dissertations

This doctoral dissertation proposal is comprised of three separate chapters, all of which uses the nationally representative uniform survey Health and Retirement Survey (HRS) to examine the relationship between health, insurance, health care and health outcomes. Below, the brief introduction for each section is provided:

 Chapter I: Medicare Part D and Patients' Well-being

 Chapter II: Parent's Health Insurance and Informal Care

 Chapter III: Job Insecurity and Health (with Dr. Ayyagari)

In chapter I, I explore how Medicare Part D (MD) affects the well-being of the severely sick patients both in the short- and in the long- term. …

The Effect Of Per Capita Relief Spending On County Level Joblessness In The United States In 1937 & 1940, Mohammad S. Ahmed

Theses and Dissertations

This paper uses the 1937 and 1940 county level census data to estimate what effect did additional per capita relief spending have on joblessness in the United States in 1937 and 1940. To account for endogeneity in relief spending and its unequal/non-random distribution, an instrumental variables approach is used. The results show that additional per capita relief spending lowered joblessness in the United States in both years: 1937 and 1940.

Monte-Carlo Simulation Study Of Two-Stage Quantile Regression For Dynamic Panel Data, Hossameldin Ahmed, Alaa Ahmed Prof, Aya Afify Ms

Economics

No abstract provided.

Does Medicaid Increase Emergency Room Use: Evidence From Oregon Health Program?, Md Fourkan

Electronic Theses and Dissertations

This thesis paper strives to identify the relationship between Medicaid expansion and Emergency Department use. I use a Monte Carlo simulation for demonstrating the endogeneity problem and a copula model using the Oregon Health Program (OHP) data to show the previous literature has exaggerated the causal relation between Medicaid expansion and Emergency Department use. This paper can be divided into two parts. First, it tries to focus on the under-identification of multiple endogenous variables problem in typical econometrics papers, where researchers correct for a single endogenous variable but intentionally or unintentionally ignore the endogeneity of one or more other independent …

Censored Quantile Instrumental Variable Estimation With Stata, Victor Chernozhukov, Iván Fernández-Val, Sukjin Han, Amanda E. Kowalski

Cowles Foundation Discussion Papers

Many applications involve a censored dependent variable and an endogenous independent variable. Chernozhukov et al. (2015) introduced a censored quantile instrumental variable estimator (CQIV) for use in those applications, which has been applied by Kowalski (2016), among others. In this article, we introduce a Stata command, cqiv, that simplifies application of the CQIV estimator in Stata. We summarize the CQIV estimator and algorithm, we describe the use of the cqiv command, and we provide empirical examples.

A Distribution-Free Stochastic Frontier Model With Endogenous Regressors, Levent Kutlu

Economics and Finance Faculty Publications and Presentations

We provide a guideline for estimating a distribution-free panel data stochastic frontier model in the presence of endogenous variables. In particular, we consider variations of the within estimator of Cornwell et al. (1990) to allow endogenous regressors.

Estimation Of A Partially Linear Regression In Triangular Systems, Xin Geng, Carlos Martins-Filho, Feng Yao

Economics Faculty Working Papers Series

We propose kernel-based estimators for the components of a partially linear regression in a triangular system where endogenous regressors appear both in the linear and nonparametric components of the regression. Compared with other estimators currently available in the literature, e.g. the sieve estimators proposed in Ai and Chen (2003) or Otsu (2011), our estimators have explicit functional form and are much easier to implement. They rely on a set of assumptions introduced by Newey et al. (1999) that characterize what has become known as the “control function” approach for endogeneity in regression. We explore conditional moment restrictions that make this …

School District Consolidation Policies: Endogenous Cost Inefficiency And Saving Reversals, Mustafa U. Karakaplan, Levent Kutlu

Economics and Finance Faculty Publications and Presentations

Some education policy studies suggest that consolidation of public school districts saves resources. However, endogeneity in cost models would result in incorrect estimates of the effects of consolidation. We use a new stochastic frontier methodology to examine district expenditures while handling endogeneity. Using the data from California, we find that the effects of student achievement and education market concentration on expenditure per pupil are substantially larger when endogeneity is handled. Our findings are robust to concerns such as instrumental variable adequacy and spatial interactions. Our consolidation simulations indicate that failure to address endogeneity can result in unrealistic expectations of savings.

Endogeneity In Panel Stochastic Frontier Models: An Application To The Japanese Cotton Spinning Industry, Mustafa U. Karakaplan, Levent Kutlu

Economics and Finance Faculty Publications and Presentations

We present a panel stochastic frontier model that handles the endogeneity problem. This model can treat the endogeneity of both frontier and inefficiency variables. We apply our method to examine the technical efficiency of Japanese cotton spinning industry. Our results indicate that market concentration is endogenous, and when its endogeneity is properly handled, it has a larger negative impact on the technical efficiency of cotton spinning plants. We find that the exogenous model substantially overestimates efficiency in concentrated markets.

Structural Inference From Reduced Forms With Many Instruments, Peter C. B. Phillips, Wayne Yuan Gao

Research Collection School Of Economics

This paper develops exact finite sample and asymptotic distributions for structural equation tests based on partially restricted reduced form estimates. Particular attention is given to models with large numbers of instruments, wherein the use of partially restricted reduced form estimates is shown to be especially advantageous in statistical testing even in cases of uniformly weak instruments. Comparisons are made with methods based on unrestricted reduced forms, and numerical computations showing finite sample performance of the tests are reported. Some new results are obtained on inequalities between noncentral chi-squared distributions with different degrees of freedom that assist in analytic power comparisons.

Reduced Forms And Weak Instrumentation, Peter C. B. Phillips

Research Collection School Of Economics

This paper develops exact finite sample and asymptotic distributions for a class of reduced form estimators and predictors, allowing for the presence of unidentified or weakly identified structural equations. Weak instrument asymptotic theory is developed directly from finite sample results, unifying earlier findings and showing the usefulness of structural information in making predictions from reduced form systems in applications. Asymptotic results are reported for predictions from models with many weak instruments. Of particular interest is the finding that, in unidentified and weakly identified structural models, partially restricted reduced form predictors have considerably smaller forecast mean square errors than unrestricted reduced …

Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshina

Liangjun Su

In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We approximate the unknown functional coefficients by some basis functions and estimate them by the IVQR technique. We establish the uniform consistency and asymptotic normality of the estimators of the functional coefficients. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis, a sequence of local alternatives and global alternatives, and propose a wild-bootstrap procedure …

Estimating Smooth Structural Change In Cointegration Models, Peter C. B. Phillips, Degui Li, Jiti Gao

Research Collection School Of Economics

This paper studies nonlinear cointegration models in which the structural coefficients may evolve smoothly over time, and considers time-varying coefficient functions estimated by nonparametric kernel methods. It is shown that the usual asymptotic methods of kernel estimation completely break down in this setting when the functional coefficients are multivariate. The reason for this breakdown is a kernel induced degeneracy in the weighted signal matrix associated with the nonstationary regressors, a new phenomenon in the kernel regression literature. Some new techniques are developed to address the degeneracy and resolve the asymptotics, using a path-dependent local coordinate transformation to reorient coordinates and …

How Endogeneity Matters In Framing Legalization: A Case Study Of Urban Self Help Groups In Ethiopia, Bisrat Kabeta

Sustainability and Social Justice

The future of an estimated 20,000 Self Help Groups (SHGs) in Ethiopia is uncertain because they lack legal status and, therefore, are unable to access funds and service for their members. The Government of Ethiopia (GoE) does not recognize the SHGs as unique development groups, but only offers to register them as Micro and Small Enterprises (MSEs) or cooperative societies, which are solely economic entities that serve more narrow functions than SHGs do. There has not been any coherent explanation for why the SHGs need a formal status, but should not register as anything but SHGs. From May to August …

Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino

Research Collection School Of Economics

In this paper we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We estimate the functional coefficients by the sieve-IVQR technique and establish the uniform consistency and asymptotic normality of the estimators. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients and study its asymptotic. We conduct simulations to evaluate the finite sample behavior of our estimator and test statistic, and apply our method to study the estimation of quantile Engel curves.

Quantile Treatment Effects With Endogeneity: A Monte Carlo Comparison Of 3 Quantile Iv Estimators, Alexander Poulsen, Brigham Frandsen

Journal of Undergraduate Research

Quantile instrumental variables estimators are a relatively new development in the econometric literature. Modern quantile regression was introduced in Koenker and Basset (1978), and has been used in many important applications in which researchers are interested in learning about the effects of variables on the distribution of an outcome variable, rather than just mean effects. Examples of these applications include changes in U.S. wage structure (Buchinsky 1994), the effect of school quality on student performance (Eide and Showalter 1998), and the relationship between innovation and firm growth (Coad and Rao 2008).