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Specification testing for regression models with dependent data

JOURNAL OF ECONOMETRICS 2008年第1期143卷 143-165页

作者： Hidalgo, J. London Sch Econ Dept Econ London WC2A 2AE England

We examine a consistent test for the correct specification of a regression function with dependent data. The test is based on the supremum of the difference between the parametric and nonparametric estimates of the regression model. Rather surprisingly, the behaviour of the test depends on whether the regressors are deterministic or stochastic. In the former situation, the normalization constants necessary to obtain the limiting Gumbel distribution are data dependent and difficult to estimate, so it may be difficult to obtain valid critical values, whereas, in the latter, the asymptotic distribution may not be even known. Because of that, under very mild regularity conditions, we describe a bootstrap analogue for the test, showing its asymptotic validity and finite sample behaviour in a small Monte-Carlo experiment. (c) 2007 Elsevier B.V. All rights reserved.

关键词： functional specification variable selection nonparametric kernel regression frequency domain bootstrap

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Semiparametric Double Balancing Score Estimation for Incomplete Data With Ignorable Missingness

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2012年第497期107卷 247-257页

作者： Hu, Zonghui Follmann, Dean A. Qin, Jing NIAID Biostat Res Branch NIH Bethesda MD 20892 USA

When estimating the marginal mean response with missing observations, a critical issue is robustness to model misspecification. In this article, we propose a semiparametric estimation method with extended double robustness that attains the optimal efficiency under less stringent requirement for model specifications than the doubly robust estimators. In this semiparametric estimation, covariate information is collapsed into a two-dimensional score S. with one dimension for (i) the pattern of missingness and the other for (ii) the pattern of response, both estimated from some working parametric models. The mean response E(Y) is then estimated by the sample mean of E(Y vertical bar S), which is estimated via nonparametric regression. The semiparametric estimator is consistent if either the "core" of (i) or the "core" of (ii) is captured by S, and attains the optimal efficiency if both are captured by S. As the "cores" can be obtained without correctly specifying the full parametric models for (i) or (ii), the proposed estimator can be more robust than other doubly robust estimators. As S contains the propensity score as one component, the proposed estimator avoids the use and the shortcomings of inverse propensity weighting. This semiparametric estimator is most appealing for high-dimensional covariates, where fully correct model specification is challenging and nonparametric estimation is not feasible due to the problem of dimensionality. Numerical performance is investigated by simulation studies.

关键词： Doubly robust nonparametric kernel regression Propensity score

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Sequential design for local bandwidth response curve estimator

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JOURNAL OF nonparametric STATISTICS 2000年第4期12卷 539-548页

作者： Park, DR Hanshin Univ Dept Stat Osan 447791 Kyunggi Do South Korea

Suppose we want to estimate the response curve using a kernel estimator. When we perform an experiment to estimate the response curve, we usually decide on the location of all the design points in advance of the experiment. Muller and Schmitt (1988) propose the optimal design, but in practice, we need some prior information about response curve to construct such a design. Park and Faraway (1998) present the sequential design for the global bandwidth estimator and show that their sequential design is asymptotically equivalent to the optimal design and is more efficient than the evenly spaced design. However, the sequential design points will be unequally spaced and so using a global bandwidth may not be appropriate. In this paper, we present the sequential design for the local bandwidth response curve estimator.

关键词： nonparametric kernel regression response curve local bandwidth choice sequential design

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Forecasting Performance of Nonlinear Models for Intraday Stock Returns

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JOURNAL OF FORECASTING 2012年第2期31卷 172-188页

作者： Matias, Jose M. Reboredo, Juan C. Univ Santiago de Compostela Dept Econ Santiago De Compostela 15782 Spain Univ Vigo Dept Stat ETSEM Vigo 36310 Spain

We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60?minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： forecasting time series intraday stock returns stock price forecasting high-frequency data smooth transition regression Markov switching regression neural networks nonparametric kernel regression support vector machine regression

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nonparametric factor analysis of residual time series

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TEST 2001年第1期10卷 161-182页

作者： Rodríguez-Poo, JM Linton, O Univ Cantabria Dept Econ E-39005 Santander Spain Univ London London Sch Econ & Polit Sci Dept Econ London WC2A 2AE England

We introduce a nonparametric smoothing procedure for nonparametric factor analysis of multivariate time series. Our main objective is to develop an adaptive method for estimating a time-varying covariance matrix. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel of the U.S. economy. We find substantial evidence of time varying second moments and breaks in the contemporaneous correlation structure during the mid 1970's to the early 1980's.

关键词： factor analysis nonparametric kernel regression time series

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Estimation of mean response via the effective balancing score

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BIOMETRIKA 2014年第3期101卷 613-624页

作者： Hu, Zonghui Follmann, Dean A. Wang, Naisyin NIAID Biostat Res Branch NIH Bethesda MD 20892 USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA

We introduce the effective balancing score for estimation of the mean response under a missing-at-random mechanism. Unlike conventional balancing scores, the proposed score is constructed via dimension reduction free. of model specification. Three types of such scores are introduced, distinguished by whether they carry the covariate information about the missingness, the response, or both. The effective balancing score leads to consistent estimation with little or no loss in efficiency. Compared to existing estimators, it reduces the burden of model specification and is more robust. It is a near-automatic procedure which is most appealing when high-dimensional covariates are involved. We investigate its asymptotic and numerical properties, and illustrate its application with an HIV disease study.

关键词： Balancing score Dimension reduction Missingness at random nonparametric kernel regression Prognostic score Propensity score.

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Sufficient dimension reduction and instrument search for data with nonignorable nonresponse

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BERNOULLI 2021年第2期27卷 930-945页

作者： Zhao, Puying Wang, Lei Shaw, Jun Yunnan Univ Dept Stat Kunming Yunnan Peoples R China Nankai Univ Sch Stat & Data Sci Tianjin Peoples R China Univ Wisconsin Dept Stat Madison WI 53706 USA

Consider a response variable subject to nonignorable nonresponse and a fully observed covariate vector. The purpose of our study is threefold. First, we study how to extend nonparametric sufficient dimension reduction to data with nonignorable nonresponse. Second, we utilize sufficient dimension reduction to search an instrument, a linear function of covariates that is related to the response variable but can be excluded from the propensity of nonignorable nonresponse, for the purpose of identifying unknown parameters in a semiparametric propensity and a nonparametric distribution of response variable and covariates. Third, we establish asymptotic results for parameter estimators based on sufficient dimension reduction and instrument search, and investigate the effect on the limiting distribution of parameter estimators due to instrument search. We evaluate the performance of proposed estimators in a Monte Carlo study and illustrate our method in an application to AIDS Clinical Trials Group Protocol 175 data.

关键词： Covariate dimension reduction estimation identifiability instrument nonparametric kernel regression semiparametric propensity

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nonparametric causal inference from observational time series through marginal integration

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ECONOMETRICS AND STATISTICS 2017年 2卷 81-105页

作者： Li, Shu Ernest, Jan Buehlmann, Peter Swiss Fed Inst Technol Seminar Stat Ramistr 101 CH-8092 Zurich Switzerland

Causal inference from observational data is an ambitious but highly relevant task, with diverse applications ranging from natural to social sciences. Within the scope of non-parametric time series, causal inference defined through interventions is largely unexplored, although time order simplifies the problem substantially. A marginal integration scheme is considered for inferring causal effects from observational time series data, MINT-T (marginal integration in time series), which is an adaptation for time series of a previously proposed method for the case of independent data. This approach for stationary stochastic processes is fully nonparametric and, assuming no instantaneous effects consistently recovers the total causal effect of a single intervention with optimal one-dimensional nonparametric convergence rate n(-2/5) assuming regularity conditions and twice differentiability of a certain corresponding regression function. Therefore, MINT-T remains largely unaffected by the curse of dimensionality as long as smoothness conditions hold in higher dimensions and it is feasible for a large class of stationary time series, including nonlinear and multivariate processes. For the case with instantaneous effects, we provide a procedure which guards against false positive causal statements. (c) 2016 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.

关键词： Application to economics Backdoor adjustment nonparametric kernel regression Structural equation models Total causal effects Instantaneous effects

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Aid and investment in LDCs: A robust approach

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JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT 2010年第2期19卷 319-349页

作者： Gyimah-Brempong, Kwabena Racine, Jeffrey S. Univ S Florida Dept Econ Tampa FL 33620 USA McMaster Univ Dept Econ Hamilton ON L8S 4M4 Canada

This paper uses panel data and the Local Linear kernel Estimator (LLKE), to investigate the effects of aid on physical capital investment in developing countries. Specifically, we investigate the robustness of the relationship between aid and physical capital investment in Less Developed countries (LDCs) using two different measures of aid and five measures of the policy environment. We find that external aid has a positive and significant impact on physical capital investment given the support of the sample data we use. This effect is robust to the measurement of aid as well as the policy environment. However, the character of the positive relationship between aid and investment varies with the combination of the aid measure and the policy environment. We find that conditional on inflows, the better the policy environment, the higher the investment rate, all things being equal. The results have implications for aid research and aid policy.

关键词： aid investment developing countries nonparametric kernel regression policy impacts

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An econometric identification of ineffective common-cold advertising

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INTERNATIONAL JOURNAL OF PHARMACEUTICAL AND HEALTHCARE MARKETING 2023年第4期17卷 476-494页

作者： Vinod, Hrishikesh Jetta, Kurt Rengifo, Minaya Eric Fordham Univ Dept Econ New York NY 10458 USA TABS Grp Shelton CT USA

PurposeThis study aims to highlight potential savings in advertising budgets. Design/methodology/approachThis study uses modern computer-based tools including stochastic dominance to check if advertising expenses are increasing sales by using modern causality assessment tools which allow for nonlinearities and use sophisticated assessment of causal impact of ads on sales. FindingsThis study identifies specific media spots where ad budget savings are possible. The marketing managers can take the next step to make small-scale local experiments to reassess this study's findings. Research limitations/implicationsThis study is a statistical observational assessment not based on controlled experiments. Practical implicationsThe authors have tools to identify ineffective advertising which can produce huge savings for the organization. The over-the-counter cold remedies have become important due to the pandemic. The tools have wider applicability in marketing research. Social implicationsLess wasteful expenses always benefit the society. Originality/valueTo the best of the authors' knowledge, this may be the first such attempt to use sophisticated causal identification tools. Remedies for the common cold sold by seven major US retailers help identify specific retailers and specific media with negative returns on investment.

关键词： nonparametric kernel regression Stochastic dominance Marketing Media outlets Common cold remedy nonparametric methods

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