Special Issue "Nonparametric Econometric Methods and Application"

Special Issue Editor

Guest Editor
Prof. Dr. Thanasis Stengos

University Research Chair, Department of Economics and Finance, University of Guelph, Guelph, Canada
Website | E-Mail
Interests: empirical growth; nonparametric econometric methods

Special Issue Information

Dear Colleagues,

An area of very active research in econometrics over the last 30 years has been that of non- and semi-parametric methods. These methods have provided ways to complement more-traditional parametric approaches in terms of robust alternatives, as well as preliminary data analysis. The field has expanded with important advances both in time series and cross sectional frameworks and more recently in panel data settings, allowing for data driven flexibility that has proved invaluable to applied researchers. The methodology has been enhanced by software developments that have made these methods easy to apply, somethings that has opened up a variety of potentially important and relevant applications in all areas of economics, microeconomics, macroeconomics and economic growth, finance, labor, etc. The present Special Issue aims at collecting a number of new contributions both at the theoretical level, as well as in terms of applications.  We hope that this collection of papers will add to this important literature, both at the theoretical and empirical level including areas, such as local smoothing techniques, splines, series estimators, and wavelets.

Prof. Dr. Thanasis Stengos
Guest Editor

Manuscript Submission Information

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Keywords

  • Nonparametric methods
  • Semiparametric methods
  • Local smoothers
  • Splines
  • Wavelets

Published Papers (8 papers)

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Research

Open AccessFeature PaperArticle Forecasting of Realised Volatility with the Random Forests Algorithm
J. Risk Financial Manag. 2018, 11(4), 61; https://doi.org/10.3390/jrfm11040061
Received: 8 September 2018 / Revised: 8 October 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
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Abstract
The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply
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The paper addresses the forecasting of realised volatility for financial time series using the heterogeneous autoregressive model (HAR) and machine learning techniques. We consider an extended version of the existing HAR model with included purified implied volatility. For this extended model, we apply the random forests algorithm for the forecasting of the direction and the magnitude of the realised volatility. In experiments with historical high frequency data, we demonstrate improvements of forecast accuracy for the proposed model. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Open AccessArticle Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis
J. Risk Financial Manag. 2018, 11(3), 52; https://doi.org/10.3390/jrfm11030052
Received: 27 July 2018 / Revised: 31 August 2018 / Accepted: 1 September 2018 / Published: 5 September 2018
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Abstract
In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation
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In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation of the observables’ joint distribution. Our nonparametric estimator allows for deviation from conditional Gaussianity through non-zero, higher ordered, moments, like asymmetric, fat-tailed behavior, along with smooth, nonlinear, risk–return relationships. We use the parsimonious and relatively uninformative Bayesian Dirichlet process prior to overcoming the problem of having too many unknowns and not enough observations. Applying our Bayesian nonparametric model to more than a century’s worth of monthly US stock market returns and realized variances, we find strong, robust evidence of volatility feedback. Once volatility feedback is accounted for, we find an unambiguous positive, nonlinear, relationship between expected excess returns and expected log-realized variance. In addition to the conditional mean, volatility feedback impacts the entire joint distribution. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Open AccessArticle Monte Carlo Comparison for Nonparametric Threshold Estimators
J. Risk Financial Manag. 2018, 11(3), 49; https://doi.org/10.3390/jrfm11030049
Received: 17 July 2018 / Revised: 13 August 2018 / Accepted: 15 August 2018 / Published: 17 August 2018
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Abstract
This paper compares the finite sample performance of three non-parametric threshold estimators via the Monte Carlo method. Our results indicate that the finite sample performance of the three estimators is not robust to the position of the threshold level along the distribution of
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This paper compares the finite sample performance of three non-parametric threshold estimators via the Monte Carlo method. Our results indicate that the finite sample performance of the three estimators is not robust to the position of the threshold level along the distribution of the threshold variable, especially when a structural change occurs at the tail part of the distribution. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Open AccessArticle On the Performance of Wavelet Based Unit Root Tests
J. Risk Financial Manag. 2018, 11(3), 47; https://doi.org/10.3390/jrfm11030047
Received: 21 June 2018 / Revised: 1 August 2018 / Accepted: 8 August 2018 / Published: 13 August 2018
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Abstract
In this paper, we apply the wavelet methods in the popular Augmented Dickey-Fuller and M types of unit root tests. Moreover, we provide an extensive comparison of the wavelet based unit root tests which also includes the recent contributions in the literature. Moreover,
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In this paper, we apply the wavelet methods in the popular Augmented Dickey-Fuller and M types of unit root tests. Moreover, we provide an extensive comparison of the wavelet based unit root tests which also includes the recent contributions in the literature. Moreover, we derive the asymptotic properties of the wavelet based unit root tests under generalized least squares detrending mechanism. We demonstrate that the wavelet based M tests exhibit better size performance even in problematic cases such as the presence of negative moving average innovations. However, the power performances of the wavelet based unit root tests are quite similar to each other. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Open AccessFeature PaperArticle Financial Development and Countries’ Production Efficiency: A Nonparametric Analysis
J. Risk Financial Manag. 2018, 11(3), 46; https://doi.org/10.3390/jrfm11030046
Received: 16 July 2018 / Revised: 4 August 2018 / Accepted: 6 August 2018 / Published: 7 August 2018
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Abstract
This paper examines the effect of financial development on countries’ production efficiency levels. By applying a probabilistic framework it develops robust (Order-m) time-dependent conditional nonparametric frontier estimators in order to measure 87 countries’ production efficiency levels over the period 1970–2014. In order to
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This paper examines the effect of financial development on countries’ production efficiency levels. By applying a probabilistic framework it develops robust (Order-m) time-dependent conditional nonparametric frontier estimators in order to measure 87 countries’ production efficiency levels over the period 1970–2014. In order to examine the effect of time and domestic credit on countries’ production efficiency levels, a second-stage nonparametric econometric analysis is performed. Specifically, generalized additive models with tensor products and cubic spline penalties are applied in order to investigate the potential nonlinear behavior of financial development on countries’ production efficiency levels. The results reveal that the effect of financial development on production efficiency is nonlinear. Specifically, the effect is positive up to a certain credit level after which it becomes negative. Finally, the evidence suggests that the effect is influenced by a country’s financial system, institutional, and development characteristics. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Open AccessFeature PaperArticle Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model
J. Risk Financial Manag. 2018, 11(3), 44; https://doi.org/10.3390/jrfm11030044
Received: 11 July 2018 / Revised: 31 July 2018 / Accepted: 1 August 2018 / Published: 3 August 2018
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Abstract
This paper develops a nonparametric method to estimate a conditional quantile function for a panel data model with an additive individual fixed effects. The proposed method is easy to implement, it does not require numerical optimization and automatically ensures quantile monotonicity by construction.
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This paper develops a nonparametric method to estimate a conditional quantile function for a panel data model with an additive individual fixed effects. The proposed method is easy to implement, it does not require numerical optimization and automatically ensures quantile monotonicity by construction. Monte Carlo simulations show that the proposed estimator performs well in finite samples. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Open AccessFeature PaperArticle Greenhouse Emissions and Productivity Growth
J. Risk Financial Manag. 2018, 11(3), 38; https://doi.org/10.3390/jrfm11030038
Received: 4 June 2018 / Revised: 25 June 2018 / Accepted: 3 July 2018 / Published: 9 July 2018
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Abstract
In this paper, we examine the effect of emissions, as measured by carbon dioxide (CO2), on economic growth among a set of OECD countries during the period 1981–1998. We examine the relationship between total factor productivity (TFP) growth and emissions using
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In this paper, we examine the effect of emissions, as measured by carbon dioxide (CO2), on economic growth among a set of OECD countries during the period 1981–1998. We examine the relationship between total factor productivity (TFP) growth and emissions using a semiparametric smooth coefficient model that allow us to directly estimate the output elasticity of emissions. The results indicate that there exists a monotonically-increasing relationship between emissions and TFP growth. The output elasticity of CO2 emissions is small with an average sample value of 0.07. In addition, we find an average contribution of CO2 emissions to productivity growth of about 0.063 percent for the period 1981–1998. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Open AccessArticle Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study
J. Risk Financial Manag. 2018, 11(2), 29; https://doi.org/10.3390/jrfm11020029
Received: 5 April 2018 / Revised: 30 May 2018 / Accepted: 4 June 2018 / Published: 8 June 2018
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Abstract
This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different
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This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different segments of the market return distribution. We find that: (a) the two series exhibit strong, negative, extreme tail dependence; (b) the negative dependence is stronger in extreme bearish markets than in extreme bullish markets; (c) the dependence gradually weakens as the market return moves toward the center of its distribution, or in quiet markets. The unique dependence structure supports the VIX as a barometer of markets’ mood in general. Moreover, applying the proposed method to the S&P 500 returns and the implied variance (VIX2), we find that the nonparametric leverage effect is much stronger than the nonparametric volatility feedback effect, although, in general, both effects are weaker than the dependence relation between the market returns and the log-increments of the VIX. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Evaluation of economic and social development in EU28 member states by DEA efficiency.
Abstract: Data Envelopment Analysis (DEA) methodology has been used for comparison of the dynamic efficiency of the European countries over the last decade. Moreover, efficiency analysis determines where the resources are distributed in an efficient way and/or have been used efficiently/inefficiently pursuant to factors of competitiveness extracted from Factor Analysis. DEA measures numerical grades of efficiency scores of economical processes within evaluated countries, and therefore it becomes a suitable tool for setting an efficient/inefficient position of each country. DEA is applied to all 28 EU countries to evaluate technical and technological efficiency within the selected factors of competitiveness based on Country Competitiveness Index in the 2000-2015 period. The main aim of the paper is to measure the efficiency changes over the reference period and to analyse the level of productivity in individual countries based on the Malmquist Productivity Index (MPI). Empirical results confirm significant economic development disparities among European countries and selected periods 2000-2007, 2008-2011 and 2012-2015. The conclusion offers a comprehensive comparison and discussion of results obtained by MPI that indicate in which EU country should be policy-making authorities in order to stimulate national development and provide more quality of life to the EU citizens.
Keywords: Competitiveness, DEA, Efficiency, European Union, Factors, Malmquist Productivity Index.
JEL Classification: C61, C67, E61, O11, O52, Y10

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