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nonlinear Predictability of Stock Returns? Parametric Versus Nonparametric Inference in Predictive regressions

JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2022年第1期40卷 382-397页

作者： Demetrescu, Matei Hillmann, Benjamin Christian Albrechts Univ Kiel Inst Stat & Econometr Olshausenstr 40-60 D-24118 Kiel Germany

Nonparametric test procedures in predictive regressions havelimiting null distributions under both low and high regressor persistence, but low local power compared to misspecified linear predictive regressions. We argue that IV inference is better suited (in terms of local power) for analyzing additive predictive models with uncertain predictor persistence. Then, a two-step procedure is proposed for out-of-sample predictions. For the current estimation window, one first tests for predictability;in case of a rejection, one predicts using a nonlinear regression model, otherwise the historic average of the stock returns is used. This two-step approach performs better than competitors (though not by a large margin) in a pseudo-out-of-sample prediction exercise for the S&P 500.

关键词： Chi-square distribution Endogeneity nonlinear regression function Predictive regression Time-varying variance Unknown persistence

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Investigation on linearisation of data-driven transport research: two representative case studies

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第7期14卷 675-683页

作者： Zou, Yun Kuang, Yan Zhi, Yue Qu, Xiaobo Sch Civil & Environm Engn Univ Technol Sydney Sydney NSW 4007 Australia Griffith Univ Griffith Sch Engn Gold Coast Qld 4222 Australia Binzhou Med Univ Sch Rehabil Med Yantai 264003 Peoples R China Chalmers Univ Technol Dept Architecture & Civil Engn S-41296 Gothenburg Sweden

Transportation engineering, as a practical engineering discipline, relies heavily on the accurate calibration of importation parameters from field data. In the real world, most transport relations possess inherent non-linearity. Two prevailing methods for handling non-linear regression are the non-linear least-squares method (LSM) with an iterative solution, and linearisation for the non-linear regression function. The second method applies a linear regression method to solve the non-linear regression problem but requires a data transformation of the observations from variant coordinates, and the objective function is suspected to be changed accordingly. This work describes the authors' investigation into the problem of non-linear regression through two illustrative examples, the calibration of three non-linear (either exponential or logarithmic) single-regime models for fundamental diagram and the regression of non-linear (power) bunker-consumption model, by applying the weighted LSM (WLSM) and the ordinary LSM to calibrate. It is found that linearising the regression model leads to deviations, and the data transformation can create even more concern with the WLSM because the weights can be redistributed after the data transformation. A further investigation into the linear regression and the non-linear regression gives more suggestions on the choice of regression method.

关键词： transportation least squares approximations mechanical engineering computing regression analysis data-driven transport research transportation engineering nonlinearity nonlinear regression function linear regression method nonlinear regression problem data transformation nonlinear bunker-consumption model regression model

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Investigation on linearisation of data-driven transport research: two representative case studies

Investigation on linearisation of data-driven transport rese...

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15th World Conference on Transport Research (WCTR)

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Two-Stage Short-Term Load Forecasting for Power Transformers Under Different Substation Operating Conditions

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IEEE ACCESS 2019年 7卷 161424-161436页

作者： Liu, Hang Wang, Youyuan Wei, Chao Li, Jiansheng Lin, Yuandi Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Jiangsu Elect Power Co Res Inst Nanjing 211103 Jiangsu Peoples R China

The load of transformers shows higher volatility and uncertainty than do the system-level and substation-level loads. This paper proposes a two-stage short-term load forecasting (STLF) model for power transformers. 1) Three state-of-the-art technologies are applied to predict the aggregated substation-level load by taking the historical load, weather, and calendar data as inputs. In this stage, no specific STLF model needs to be developed, which allows the forecasters to select the most accurate prediction results for transformer-level load forecasting. 2) The load distribution factor (LDF) is defined as the ratio of the transformer load to the substation load. The relationship between LDF and substation load is captured by nonlinear regression functions under different substation operating conditions, and the load of each parallel transformer is predicted using these nonlinear regression functions. Each nonlinear function can be accurately established even if the historical load data are scarce under some irregular operating conditions. Three application examples show the effectiveness and rationality of the proposed method. The third example demonstrates that STLF of transformers is necessary because it provides important information for optimizing substation operating schemes and equipment maintenance plans.

关键词： Load modeling Substations Predictive models Load forecasting Meteorology Power transformers Data models Load distribution factor nonlinear regression function short-term load forecasting substation operating condition transformer

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Nonnested Testing for Competing Autoregressive Dynamic Models Estimated by Instrumental Variables

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2012年第20期41卷 3799-3812页

作者： Rebelo, Efigenio Do Valle, Patricia Oom Nunes, Rui Univ Algarve Fac Econ Favo Portugal

In this work we investigate nonnested tests for two competing univariate dynamic linear models with autoregressive disturbances, where the motivation for instrumental variable estimation is mainly due to the recognized presence of current endogenous variables in the regression function, either in one or both models. As the previous transformation of both models yields regression functions which are nonlinear in the parameters, the attractive Gauss-Newton regression (GNR) approach, firstly advocated by Davidson and Mackinnon (1981), will be used to obtain the results.

关键词： Gauss-Newton regression nonlinear instrumental variables nonlinear regression function Nonnested tests Serially correlated disturbances

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A NONPARAMETRIC TEST FOR THE regression function - ASYMPTOTIC THEORY

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 1995年第1期44卷 1-17页

作者： DIEBOLT, J UNIV PARIS 06 CNRSURA 1321F-75252 PARIS 05FRANCE

This paper is devoted to the study of the asymptotic behavior of a family of nonparametric tests for the regression function in a nonlinear contest, when the observations are i.i.d. These nonparametric tests are all constructed as functionals of a basic process. We first determine the rate of convergence to zero of the distance between this basic process and Wiener type approximations. This involves establishing uniform rates of convergence for a certain sequence of processes to their limit. These processes are hybrids of the empirical and partial-sum processes, and are of some interest themselves. Then, we examine the asymptotic behavior of the power of two particular tests within the family under consideration. Both tests have been selected as the most natural ones in some sense. Contiguous alternatives are also briefly examined for these tests.

关键词： CONTIGUOUS ALTERNATIVES EMPIRICAL PROCESS GOODNESS-OF-FIT nonlinear regression function PARTIAL-SUM PROCESS POWER STRONG APPROXIMATION

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