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Articles 31 - 60 of 771
Full-Text Articles in Econometrics
Granulocyte Colony Stimulating Factor Promotes Scarless Tissue Regeneration, Jianhe Huang, Satish: Murphy Sati, Casey A. Spencer, Emmanuel Rapp, Stephen M. Prouty, Scott Korte, Olivia Ahart, Emily Sheng, Anna E. Kersh, Denis Leung, Thomas H. Leung, Leung, Denis H. Y.
Granulocyte Colony Stimulating Factor Promotes Scarless Tissue Regeneration, Jianhe Huang, Satish: Murphy Sati, Casey A. Spencer, Emmanuel Rapp, Stephen M. Prouty, Scott Korte, Olivia Ahart, Emily Sheng, Anna E. Kersh, Denis Leung, Thomas H. Leung, Leung, Denis H. Y.
Research Collection School Of Economics
Mammals typically heal with fibrotic scars, and treatments to regenerate human skin and hair without a scar remain elusive. We discovered that mice lacking C-X-C motif chemokine receptor 2 (CXCR2 knockout [KO]) displayed robust and complete tissue regeneration across three different injury models: skin, hair follicle, and cartilage. Remarkably, wild-type mice receiving plasma from CXCR2 KO mice through parabiosis or injections healed wounds scarlessly. A comparison of circulating proteins using multiplex ELISA revealed a 24-fold higher plasma level of granulocyte colony stimulating factor (G-CSF) in CXCR2 KO blood. Local injections of G-CSF into wild-type (WT) mouse wound beds reduced scar …
Estimating Firm-Level Production Functions With Spatial Dependence, Pao-Li Chang, Ryo Makioka, Bo Lin Ng, Zhenlin Yang
Estimating Firm-Level Production Functions With Spatial Dependence, Pao-Li Chang, Ryo Makioka, Bo Lin Ng, Zhenlin Yang
Research Collection School Of Economics
This paper proposes a three-stage efficient GMM estimation algorithm for estimating firm-level production functions given spatial dependence across firms due to supplier-customer relationships, sharing of input markets, or knowledge spillover. The procedure builds on Ackerberg, Caves and Frazer (2015) and Wooldridge (2009), but in addition, allows the productivity process to depend on the lagged output levels and lagged input usages of related firms, and spatially correlated productivity shocks across firms, where the set of related firms can differ across the three dimensions of spatial dependence. We establish the asymptotic properties of the proposed estimator, and conduct Monte Carlo simulations to …
Quantifying Delay Propagation In Airline Networks, Liyu Dou, Jakub Kastl, John Lazarev
Quantifying Delay Propagation In Airline Networks, Liyu Dou, Jakub Kastl, John Lazarev
Research Collection School Of Economics
We develop a framework for quantifying delay propagation in airline networks by integrating structural modeling and machine learning methods to estimate causal effects. Using a comprehensive dataset on actual delays and a model-selection algorithm (elastic net), we estimate a weighted directed graph of delay propagation for each major airline in the United States and establish conditions under which the propagation coefficients are causal. These estimates enable a decomposition of airline performance into "luck" and "ability." Our findings indicate that luck accounts for approximately 38% of the performance difference between Delta and American Airlines in our data. Additionally, we leverage these …
Recruitment Of Cxcr4+Type 1 Innate Lymphoid Cells Distinguishes Sarcoidosis From Other Skin Granulomatous Diseases, S. Sati, J. Huang, A.E. Kersh, P. Jones, O. Ahart, C. Murphy, S.M. Prouty, M.L. Hedberg, V. Jain, S.G. Gregory, Leung, Denis H. Y., J.T. Seykora, M. Rosenbach, T.H. Leung
Recruitment Of Cxcr4+Type 1 Innate Lymphoid Cells Distinguishes Sarcoidosis From Other Skin Granulomatous Diseases, S. Sati, J. Huang, A.E. Kersh, P. Jones, O. Ahart, C. Murphy, S.M. Prouty, M.L. Hedberg, V. Jain, S.G. Gregory, Leung, Denis H. Y., J.T. Seykora, M. Rosenbach, T.H. Leung
Research Collection School Of Economics
Sarcoidosis is a multiorgan granulomatous disease that lacks diagnostic biomarkers and targeted treatments. Using blood and skin from patients with sarcoid and non-sarcoid skin granulomas, we discovered that skin granulomas from different diseases exhibit unique immune cell recruitment and molecular signatures. Sarcoid skin granulomas were specifically enriched for type 1 innate lymphoid cells (ILC1s) and B cells and exhibited molecular programs associated with formation of mature tertiary lymphoid structures (TLSs), including increased CXCL12/CXCR4 signaling. Lung sarcoidosis granulomas also displayed similar immune cell recruitment. Thus, granuloma formation was not a generic molecular response. In addition to tissue-specific effects, patients with sarcoidosis …
Recruitment Of Cxcr4+ Type 1 Innate Lymphoid Cells Distinguishes Sarcoidosis From Other Skin Granulomatous Diseases, Satish Sati, Jianhe Huang, Anna E. Kersh, Parker Jones, Olivia Ahart, Christina Murphy, Stephen M. Prouty, Matthew L. Hedberg, Vaibhav Jain, Simon G. Gregory, Leung, Denis H. Y., John T. Seykora, Misha Rosenbach, Thomas H. Leung
Recruitment Of Cxcr4+ Type 1 Innate Lymphoid Cells Distinguishes Sarcoidosis From Other Skin Granulomatous Diseases, Satish Sati, Jianhe Huang, Anna E. Kersh, Parker Jones, Olivia Ahart, Christina Murphy, Stephen M. Prouty, Matthew L. Hedberg, Vaibhav Jain, Simon G. Gregory, Leung, Denis H. Y., John T. Seykora, Misha Rosenbach, Thomas H. Leung
Research Collection School Of Economics
Sarcoidosis is a multiorgan granulomatous disease that lacks diagnostic biomarkers and targeted treatments. Using blood and skin from patients with sarcoid and non-sarcoid skin granulomas, we discovered that skin granulomas from different diseases exhibit unique immune cell recruitment and molecular signatures. Sarcoid skin granulomas were specifically enriched for type 1 innate lymphoid cells (ILC1s) and B cells and exhibited molecular programs associated with formation of mature tertiary lymphoid structures (TLSs), including increased CXCL12/CXCR4 signaling. Lung sarcoidosis granulomas also displayed similar immune cell recruitment. Thus, granuloma formation was not a generic molecular response. In addition to tissue-specific effects, patients with sarcoidosis …
Gradient Wild Bootstrap For Instrumental Variable Quantile Regressions With Weak And Few Clusters, Wenjie Wang, Yichong Zhang
Gradient Wild Bootstrap For Instrumental Variable Quantile Regressions With Weak And Few Clusters, Wenjie Wang, Yichong Zhang
Research Collection School Of Economics
We study the gradient wild bootstrap-based inference for instrumental variable quantile regressions in the framework of a small number of large clusters in which the number of clusters is viewed as fixed, and the number of observations for each cluster diverges to infinity. For the Wald inference, we show that our wild bootstrap Wald test, with or without studentization using the cluster-robust covariance estimator (CRVE), controls size asymptotically up to a small error as long as the parameter of endogenous variable is strongly identified in at least one of the clusters. We further show that the wild bootstrap Wald test …
Bubbly Booms And Welfare, Feng Dong, Yang Jiao, Haoning Sun
Bubbly Booms And Welfare, Feng Dong, Yang Jiao, Haoning Sun
Research Collection School Of Economics
We show the competing effects of a housing bubble on the real economy by developing a multi-sector dynamic model with housing production. On the one hand, firms can sell or collateralize their housing, so a housing bubble helps firms obtain credit to finance their investment and expand production. On the other hand, a boom in the housing sector crowds out labor in the non-housing sector. We show that housing booms can reduce social welfare both in the steady state and in the transitional dynamics only when the production externalities in the non-housing sector are sufficiently large. We quantitatively evaluate our …
Hiv Estimation Using Population-Based Surveys With Non-Response: A Partial Identification Approach, Oyelola A. Adegboye, Tomoki Fujii, Denis H. Y. Leung, Siyu Li
Hiv Estimation Using Population-Based Surveys With Non-Response: A Partial Identification Approach, Oyelola A. Adegboye, Tomoki Fujii, Denis H. Y. Leung, Siyu Li
Research Collection School Of Economics
HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non-response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non-respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and …
Reading The Candlesticks: An Ok Estimator For Volatility, Jia Li, Dishen Wang, Qiushi Zhang
Reading The Candlesticks: An Ok Estimator For Volatility, Jia Li, Dishen Wang, Qiushi Zhang
Research Collection School Of Economics
We propose an Optimal candlesticK (OK) estimator for the spot volatility using high-frequency candlestick observations. Under a standard infill asymptotic setting, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. Its estimation error can be coupled by a Brownian functional, which permits valid inference. Our theoretical and numerical results suggest that the proposed candlestick-based estimator is much more accurate than the conventional spot volatility estimator based on high-frequency returns. An empirical illustration documents the intraday volatility dynamics of various assets during the Fed chairman's recent congressional testimony.
Trade Imbalance, Heavy Goods, And Pollution, Jungho Lee, Shang-Jin Wei, Jianhuan Xu
Trade Imbalance, Heavy Goods, And Pollution, Jungho Lee, Shang-Jin Wei, Jianhuan Xu
Research Collection School Of Economics
We propose a new welfare effect of trade surplus. Guided by insight from several fields in economics, we provide evidence that an increase in a country's trade surplus alters the unit shipping costs for its trade and the composition of its imports in ways that induce more pollution in the country in equilibrium. This new form of welfare loss of trade imbalance has not been accounted for in the existing international economics literature
Better The Devil You Know: Improved Forecasts From Imperfect Models, Dong Hwan Oh, Andrew John Patton
Better The Devil You Know: Improved Forecasts From Imperfect Models, Dong Hwan Oh, Andrew John Patton
Research Collection School Of Economics
Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a misspecified model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecification of the model. We theoretically consider the forecast environments in which our approach is likely to offer improvements over standard methods, and we find significant forecast improvements from applying the proposed method across four distinct empirical analyses including volatility …
Uniform Nonparametric Inference For Spatially Dependent Panel Data, Jia Li, Zhipeng Liao, Wenyu Zhou
Uniform Nonparametric Inference For Spatially Dependent Panel Data, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
This article proposes a uniform functional inference method for nonparametric regressions in a panel-data setting that features general unknown forms of spatio-temporal dependence. The method requires a long time span, but does not impose any restriction on the size of the cross section or the strength of spatial correlation. The uniform inference is justified via a new growing-dimensional Gaussian coupling theory for spatio-temporally dependent panels. We apply the method in two empirical settings. One concerns the nonparametric relationship between asset price volatility and trading volume as depicted by the mixture of distribution hypothesis. The other pertains to testing the rationality …
Housing Markets Since Shapley And Scarf, Mustafa Oguz Afacan, Gaoji Hu, Jiangtao Li
Housing Markets Since Shapley And Scarf, Mustafa Oguz Afacan, Gaoji Hu, Jiangtao Li
Research Collection School Of Economics
Shapley and Scarf (1974) appeared in the first issue of the Journal of Mathematical Economics, and is one of the journal’s most impactful publications. As we approach the remarkable milestone of the journal’s 50th anniversary (1974–2024), this article serves as a commemorative exploration of Shapley and Scarf (1974) and the extensive body of literature that follows it.
Wild Bootstrap Inference For Instrumental Variables Regressions With Weak And Few Clusters, Wenjie Wang, Yichong Zhang
Wild Bootstrap Inference For Instrumental Variables Regressions With Weak And Few Clusters, Wenjie Wang, Yichong Zhang
Research Collection School Of Economics
We study the wild bootstrap inference for instrumental variable regressions under an alternative asymptotic framework that the number of independent clusters is fixed, the size of each cluster diverges to infinity, and the within cluster dependence is sufficiently weak. We first show that the wild bootstrap Wald test controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. Second, we establish the conditions for the bootstrap tests to have power against local alternatives. We further develop a wild bootstrap Anderson–Rubin test for the full-vector …
Hiv Estimation Using Population Based Surveys With Non-Response: A Partial Identification Approach, Oyelola A Adegboye, Tomoki Fujii, Denis H. Y. Leung, Siyu Li
Hiv Estimation Using Population Based Surveys With Non-Response: A Partial Identification Approach, Oyelola A Adegboye, Tomoki Fujii, Denis H. Y. Leung, Siyu Li
Research Collection School Of Economics
HIV estimation using data from the Demographic and Health Surveys (DHS) is lim-ited by the presence of non-response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non-respondents, but their performance depends critically on the validity of the instru-ment. Using Manski’s partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and …
Panel Data Models With Time-Varying Latent Group Structures, Yiren Wang, Peter C. B. Phillips, Liangjun Su
Panel Data Models With Time-Varying Latent Group Structures, Yiren Wang, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism, the model may have different numbers of groups and/or different group memberships before and after the break. With preliminary nuclear norm regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. It is …
Optimal Inference For Spot Regressions, Tim Bollerslev, Jia Li, Yuexuan Ren
Optimal Inference For Spot Regressions, Tim Bollerslev, Jia Li, Yuexuan Ren
Research Collection School Of Economics
Betas from return regressions are commonly used to measure systematic financial market risks. "Good" beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas with high-frequency data. The "local Gaussian" property of the generic continuous-time benchmark model enables optimal "finite-sample" inference in a well-defined sense. It also affords more reliable inference in empirically realistic settings compared to conventional large-sample approaches. Two applications pertaining to the tracking performance of leveraged ETFs and an intraday event study illustrate the practical usefulness of the new procedures.
Testing The Dimensionality Of Policy Shocks, Jia Li, Viktor Todorov, Qiushi Zhang
Testing The Dimensionality Of Policy Shocks, Jia Li, Viktor Todorov, Qiushi Zhang
Research Collection School Of Economics
This paper provides a nonparametric test for deciding the dimensionality of a policy shock as manifest in the abnormal change in asset returns' stochastic covariance matrix, following the release of a macroeconomic announcement. We use high-frequency data in local windows before and after the event to estimate the covariance jump matrix, and then test its rank. We find a one-factor structure in the covariance jump matrix of the yield curve resulting from the Federal Reserve's monetary policy shocks prior to the 2007-2009 financial crisis. The dimensionality of policy shocks increased afterwards due to the use of unconventional monetary policy tools.
Robust Inference On Correlation Under General Heterogeneity, Liudas Giraitis, Yuefei Li, Peter C. B. Phillips
Robust Inference On Correlation Under General Heterogeneity, Liudas Giraitis, Yuefei Li, Peter C. B. Phillips
Research Collection School Of Economics
Considerable evidence in past research shows size distortion in standard tests for zero autocorrelation or zero cross-correlation when time series are not independent identically distributed random variables, pointing to the need for more robust procedures. Recent tests for serial correlation and cross-correlation in Dalla, Giraitis, and Phillips (2022) provide a more robust approach, allowing for heteroskedasticity and dependence in uncorrelated data under restrictions that require a smooth, slowly-evolving deterministic heteroskedasticity process. The present work removes those restrictions and validates the robust testing methodology for a wider class of innovations and regression residuals allowing for heteroscedastic uncorrelated and non-stationary data settings. …
High Frequency Principal Component Analysis Based On Correlation Matrix That Is Robust To Jumps, Microstructure Noise And Asynchronous Observation Times, Dachuan Chen
Research Collection School Of Economics
This paper developed the high frequency estimation for the principal component analysis (PCA) based on correlation matrix. This estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously, which is enabled by the newly proposed Truncated and Smoothed Two-Scales Realized Volatility (Truncated S-TSRV) estimator. The general framework of our methodology is constructed based on the estimation of realized spectral functions with respect to the spot correlation matrix. A new asymptotic representation for the element-wise estimation error of the spot correlation matrix estimate has been derived, resulting in a new bias correction term which is much more complex …
Bootstrap Inference For Quantile Treatment Effects In Randomized Experiments With Matched Pairs, Liang Jiang, Xiaobin Liu, Peter C B Phillips, Yichong Zhang
Bootstrap Inference For Quantile Treatment Effects In Randomized Experiments With Matched Pairs, Liang Jiang, Xiaobin Liu, Peter C B Phillips, Yichong Zhang
Research Collection School Of Economics
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair and is therefore conservative. Analytical inference involves estimating multiple functional quantities that require several tuning parameters. Instead, this paper proposes two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. Most especially, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.
High-Dimensional Iv Cointegration Estimation And Inference, Peter C. B. Phillips, Igor L. Kheifets
High-Dimensional Iv Cointegration Estimation And Inference, Peter C. B. Phillips, Igor L. Kheifets
Research Collection School Of Economics
A semiparametric triangular systems approach shows how multicointegrating linkages occur naturally in an I(1) cointegrated regression model when the long run error variance matrix in the system is singular. Under such singularity, cointegrated I(1) systems embody a multicointegrated structure that makes them useful in many empirical settings. Earlier work shows that such systems may be analyzed and estimated without appealing to the associated I(2) system but with suboptimal convergence rates and potential asymptotic bias. The present paper develops a robust approach to estimation and inference of such systems using high dimensional IV methods that have appealing asymptotic properties like those …
Equal Predictive Ability Tests Based On Panel Data With Applications To Oecd And Imf Forecasts, Oguzhan Akgun, Alain Pirotte, Giovanni Urga, Zhenlin Yang
Equal Predictive Ability Tests Based On Panel Data With Applications To Oecd And Imf Forecasts, Oguzhan Akgun, Alain Pirotte, Giovanni Urga, Zhenlin Yang
Research Collection School Of Economics
We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, S-statistics, focuses on the overall EPA hypothesis, which states that the EPA holds, on average, over all panel units and over time. The second type, C-statistics, focuses on the clustered EPA hypothesis where the EPA holds jointly for a fixed number of clusters of panel units. The asymptotic properties of the proposed tests are evaluated under weak and strong cross-sectional dependence. An extensive Monte Carlo simulation shows that the proposed tests have very good finite sample properties, …
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yui Lim Lui, Peter C. B. Phillips, Jun Yu
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yui Lim Lui, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite …
On The Optimal Forecast With The Fractional Brownian Motion, Xiaohu Wang, Jun Yu, Chen Zhang
On The Optimal Forecast With The Fractional Brownian Motion, Xiaohu Wang, Jun Yu, Chen Zhang
Research Collection School Of Economics
This paper investigates the performance of different forecasting formulas with fractional Brownian motion based on discrete and finite samples. Existing literature presents two formulas for generating optimal forecasts when continuous records are available. One formula relies on a history over an infinite past, while the other is designed for a record limited to a finite past. In reality, only observations at discrete time points over a finite past are available. In this case, the forecasting formula, which has been widely used in the literature, is the one obtained by Gatheral et al. (2018) that truncates and discretizes the formula based …
A Conditional Linear Combination Test With Many Weak Instruments, Dennis Lim, Wenjie Wang, Yichong Zhang
A Conditional Linear Combination Test With Many Weak Instruments, Dennis Lim, Wenjie Wang, Yichong Zhang
Research Collection School Of Economics
We consider a linear combination of jackknife Anderson-Rubin (AR) and orthogonalized Lagrangian multiplier (LM) tests for inference in IV regressions with many weak instruments and heteroskedasticity. We choose the weight in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed linear combination test controls asymptotic size and is admissible. Under strong identification, we further show that our linear combination test is the uniformly most powerful test against local alternatives among all tests that are constructed based on the jackknife AR and LM tests only and invariant …
Optimal Nonparametric Range-Based Volatility Estimation, Tim Bollerslev, Jia Li, Qiyuan Li
Optimal Nonparametric Range-Based Volatility Estimation, Tim Bollerslev, Jia Li, Qiyuan Li
Research Collection School Of Economics
We present a general framework for optimal nonparametric spot volatility estimation based on intraday range data, comprised of the first, highest, lowest, and last price over a given time-interval. We rely on a decision-theoretic approach together with a coupling-type argument to directly tailor the form of the nonparametric estimator to the specific volatility measure of interest and relevant loss function. The resulting new optimal estimators offer substantial efficiency gains compared to existing commonly used range-based procedures.
Dynamic Factor Copula Models With Estimated Cluster Assignments, Dong Hwan Oh, Andrew John Patton
Dynamic Factor Copula Models With Estimated Cluster Assignments, Dong Hwan Oh, Andrew John Patton
Research Collection School Of Economics
This paper proposes a dynamic multi-factor copula for use in high-dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five …
Customer Capital And Trade Intermediaries: Evidence From China, Jungho Lee, Jianhuan Xu
Customer Capital And Trade Intermediaries: Evidence From China, Jungho Lee, Jianhuan Xu
Research Collection School Of Economics
Using a unique dataset that links the production and sales of Chinese exporting firms, we document that the value of export goods a firm produces often differs from the value of export goods that the firm sells in foreign markets. We show that this empirical pattern reflects that some exporters act as trade intermediaries, which we refer to as producer intermediaries. We further show that firms with higher accumulated marketing expenditures are more likely to become producer intermediaries. To understand the implications of our empirical findings, we develop a theoretical framework in which firms can lend and borrow customer capital …
Limit Theory For Locally Flat Functional Coefficient Regression, Peter C. B. Phillips, Ying Wang
Limit Theory For Locally Flat Functional Coefficient Regression, Peter C. B. Phillips, Ying Wang
Research Collection School Of Economics
Functional coefficient (FC) regressions allow for systematic flexibility in the responsiveness of a dependent variable to movements in the regressors, making them attractive in applications where marginal effects may depend on covariates. Such models are commonly estimated by local kernel regression methods. This paper explores situations where responsiveness to covariates is locally flat or fixed. The paper develops new asymptotics that take account of shape characteristics of the function in the locality of the point of estimation. Both stationary and integrated regressor cases are examined. The limit theory of FC kernel regression is shown to depend intimately on functional shape …