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Articles 1 - 10 of 10
Full-Text Articles in Social and Behavioral Sciences
Nonlinear Cointegrating Power Function Regression With Endogeneity, Zhishui Hu, Peter C.B. Phillips, Qiying Wang
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
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
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
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
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
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
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 …
Inference In Partially Identified Panel Data Models With Interactive Fixed Effects, Shengjie Hong, Liangjun Su, Yaqi Wang
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 …
Structural Changes In Heterogeneous Panels With Endogenous Regressors, Badi Baltagi, Qu Feng, Chihwa Kao
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 …
Monte-Carlo Simulation Study Of Two-Stage Quantile Regression For Dynamic Panel Data, Hossameldin Ahmed, Alaa Ahmed Prof, Aya Afify Ms
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.