Special Issue "Nonparametric Methods in Econometrics"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (31 May 2016)

Special Issue Editor

Guest Editor
Dr. Isabel Casas

Department of Business and Economics, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark
Website | E-Mail
Interests: econometrics: semiparametric and nonparametric estimation; time series econometrics: volatility and interest rates estimation; simulation and modelling

Special Issue Information

Dear Colleagues,

This Special Issue aims at gathering the latest advances in nonparametric techniques within a variety of applications in economics and finance, in particular, but not exclusively, it will highlight the research in methodologies for time-varying parameter models. Acolytes of the motto “let data speak” have increased in the last decades as faster computer power permits a feasible treatment of large datasets with nonparametric techniques. More recently, researchers in nonparametrics are working on varying coefficient models. In the particular case of time series, economists have searched intensely for a way to include time variation in the coefficients and volatility. The reason for this is that economic processes evolve over time and their effects must be identified locally rather than globally. Despite the fact that these estimators adapt easily to situations of change, the lack of computer applications with this functionality makes them somewhat “unpopular” as it is difficult for the nonspecialized end-user to code them. This Special Issue also calls for computing code, when available, to be posted as part of the research contribution.

Dr. Isabel Casas
Guest Editor

Manuscript Submission Information

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Keywords

  • nonparametric estimation
  • varying coefficients
  • time-varying parameters
  • nonstationarity
  • kernel smoothing
  • smoothing spline
  • forecasting
  • rate of convergence

Published Papers (3 papers)

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Research

Open AccessArticle Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Econometrics 2016, 4(2), 24; https://doi.org/10.3390/econometrics4020024
Received: 8 December 2015 / Revised: 5 April 2016 / Accepted: 6 April 2016 / Published: 22 April 2016
Cited by 3 | PDF Full-text (1135 KB) | HTML Full-text | XML Full-text
Abstract
This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is [...] Read more.
This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP) growth rates among the organisation for economic co-operation and development (OECD) and non-OECD countries. Full article
(This article belongs to the Special Issue Nonparametric Methods in Econometrics)
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Open AccessArticle Functional-Coefficient Spatial Durbin Models with Nonparametric Spatial Weights: An Application to Economic Growth
Received: 6 November 2015 / Revised: 11 January 2016 / Accepted: 19 January 2016 / Published: 3 February 2016
Cited by 1 | PDF Full-text (464 KB) | HTML Full-text | XML Full-text
Abstract
This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS) estimation method. To further improve estimation accuracy, we [...] Read more.
This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS) estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown functional coefficients by a local linear regression approach. Some Monte Carlo simulation results are reported to assess the finite sample performance of our proposed estimators. We then apply the proposed model to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries’ openness to trade, respectively. Full article
(This article belongs to the Special Issue Nonparametric Methods in Econometrics)
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Figure 1

Open AccessArticle Forecast Combination under Heavy-Tailed Errors
Econometrics 2015, 3(4), 797-824; https://doi.org/10.3390/econometrics3040797
Received: 22 August 2015 / Revised: 9 November 2015 / Accepted: 10 November 2015 / Published: 23 November 2015
PDF Full-text (371 KB) | HTML Full-text | XML Full-text
Abstract
Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has [...] Read more.
Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has been done to obtain reliable forecast combinations for such situations. The familiar forecast combination methods, such as simple average, least squares regression or those based on the variance-covariance of the forecasts, may perform very poorly due to the fact that outliers tend to occur, and they make these methods have unstable weights, leading to un-robust forecasts. To address this problem, in this paper, we propose two nonparametric forecast combination methods. One is specially proposed for the situations in which the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student’s t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to a shortage of data and/or an evolving data-generating process. Adaptive risk bounds of both methods are developed. They show that the resulting combined forecasts yield near optimal mean forecast errors relative to the candidate forecasts. Simulations and a real example demonstrate their superior performance in that they indeed tend to have significantly smaller prediction errors than the previous combination methods in the presence of forecast outliers. Full article
(This article belongs to the Special Issue Nonparametric Methods in Econometrics)
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