Econometrics

447 KiB

Open AccessArticle

Evaluating Eigenvector Spatial Filter Corrections for Omitted Georeferenced Variables

by Daniel A. Griffith and Yongwan Chun

Econometrics 2016, 4(2), 29; https://doi.org/10.3390/econometrics4020029 - 21 Jun 2016

Cited by 17 | Viewed by 6639

The Ramsey regression equation specification error test (RESET) furnishes a diagnostic for omitted variables in a linear regression model specification (i.e., the null hypothesis is no omitted variables). Integer powers of fitted values from a regression analysis are introduced as additional [...] Read more.

The Ramsey regression equation specification error test (RESET) furnishes a diagnostic for omitted variables in a linear regression model specification (i.e., the null hypothesis is no omitted variables). Integer powers of fitted values from a regression analysis are introduced as additional covariates in a second regression analysis. The former regression model can be considered restricted, whereas the latter model can be considered unrestricted; this first model is nested within this second model. A RESET significance test is conducted with an F-test using the error sums of squares and the degrees of freedom for the two models. For georeferenced data, eigenvectors can be extracted from a modified spatial weights matrix, and included in a linear regression model specification to account for the presence of nonzero spatial autocorrelation. The intuition underlying this methodology is that these synthetic variates function as surrogates for omitted variables. Accordingly, a restricted regression model without eigenvectors should indicate an omitted variables problem, whereas an unrestricted regression model with eigenvectors should result in a failure to reject the RESET null hypothesis. This paper furnishes eleven empirical examples, covering a wide range of spatial attribute data types, that illustrate the effectiveness of eigenvector spatial filtering in addressing the omitted variables problem for georeferenced data as measured by the RESET. Full article

(This article belongs to the Special Issue Recent Developments of Specification Testing)

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419 KiB

Open AccessArticle

Testing Symmetry of Unknown Densities via Smoothing with the Generalized Gamma Kernels

by Masayuki Hirukawa and Mari Sakudo

Econometrics 2016, 4(2), 28; https://doi.org/10.3390/econometrics4020028 - 17 Jun 2016

Cited by 2 | Viewed by 5489

Abstract

This paper improves a kernel-smoothed test of symmetry through combining it with a new class of asymmetric kernels called the generalized gamma kernels. It is demonstrated that the improved test statistic has a normal limit under the null of symmetry and is consistent [...] Read more.

This paper improves a kernel-smoothed test of symmetry through combining it with a new class of asymmetric kernels called the generalized gamma kernels. It is demonstrated that the improved test statistic has a normal limit under the null of symmetry and is consistent under the alternative. A test-oriented smoothing parameter selection method is also proposed to implement the test. Monte Carlo simulations indicate superior finite-sample performance of the test statistic. It is worth emphasizing that the performance is grounded on the first-order normal limit and a small number of observations, despite a nonparametric convergence rate and a sample-splitting procedure of the test. Full article

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779 KiB

Open AccessArticle

Removing Specification Errors from the Usual Formulation of Binary Choice Models

by P.A.V.B. Swamy, I-Lok Chang, Jatinder S. Mehta, William H. Greene, Stephen G. Hall and George S. Tavlas

Econometrics 2016, 4(2), 26; https://doi.org/10.3390/econometrics4020026 - 03 Jun 2016

Cited by 2 | Viewed by 5691

Abstract

We develop a procedure for removing four major specification errors from the usual formulation of binary choice models. The model that results from this procedure is different from the conventional probit and logit models. This difference arises as a direct consequence of our [...] Read more.

We develop a procedure for removing four major specification errors from the usual formulation of binary choice models. The model that results from this procedure is different from the conventional probit and logit models. This difference arises as a direct consequence of our relaxation of the usual assumption that omitted regressors constituting the error term of a latent linear regression model do not introduce omitted regressor biases into the coefficients of the included regressors. Full article

(This article belongs to the Special Issue Discrete Choice Modeling)

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405 KiB

Open AccessArticle

Continuous and Jump Betas: Implications for Portfolio Diversification

by Vitali Alexeev, Mardi Dungey and Wenying Yao

Econometrics 2016, 4(2), 27; https://doi.org/10.3390/econometrics4020027 - 01 Jun 2016

Cited by 4 | Viewed by 6593

Abstract

Using high-frequency data, we decompose the time-varying beta for stocks into beta for continuous systematic risk and beta for discontinuous systematic risk. Estimated discontinuous betas for S&P500 constituents between 2003 and 2011 generally exceed the corresponding continuous betas. We demonstrate how continuous and [...] Read more.

Using high-frequency data, we decompose the time-varying beta for stocks into beta for continuous systematic risk and beta for discontinuous systematic risk. Estimated discontinuous betas for S&P500 constituents between 2003 and 2011 generally exceed the corresponding continuous betas. We demonstrate how continuous and discontinuous betas decrease with portfolio diversification. Using an equiweighted broad market index, we assess the speed of convergence of continuous and discontinuous betas in portfolios of stocks as the number of holdings increase. We show that discontinuous risk dissipates faster with fewer stocks in a portfolio compared to its continuous counterpart. Full article

(This article belongs to the Special Issue Financial High-Frequency Data)

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730 KiB

Open AccessArticle

Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability

by Marc S. Paolella

Econometrics 2016, 4(2), 25; https://doi.org/10.3390/econometrics4020025 - 05 May 2016

Cited by 19 | Viewed by 8268

Abstract

A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration is proposed. Several powerful tests for the (asymmetric) stable Paretian distribution with tail index

1 < α < 2

are used for assessing the appropriateness of the stable [...] Read more.

A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration is proposed. Several powerful tests for the (asymmetric) stable Paretian distribution with tail index

1 < α < 2

are used for assessing the appropriateness of the stable assumption as the innovations process in stable-GARCH-type models for daily stock returns. Overall, there is strong evidence against the stable as the correct innovations assumption for all stocks and time periods, though for many stocks and windows of data, the stable hypothesis is not rejected. Full article

(This article belongs to the Special Issue Recent Developments of Financial Econometrics)

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1135 KiB

Open AccessArticle

Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors

by Xibin Zhang, Maxwell L. King and Han Lin Shang

Econometrics 2016, 4(2), 24; https://doi.org/10.3390/econometrics4020024 - 22 Apr 2016

Cited by 9 | Viewed by 7200

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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290 KiB

Open AccessArticle

Building a Structural Model: Parameterization and Structurality

by Michel Mouchart and Renzo Orsi

Econometrics 2016, 4(2), 23; https://doi.org/10.3390/econometrics4020023 - 12 Apr 2016

Cited by 2 | Viewed by 5803

Abstract

A specific concept of structural model is used as a background for discussing the structurality of its parameterization. Conditions for a structural model to be also causal are examined. Difficulties and pitfalls arising from the parameterization are analyzed. In particular, pitfalls when considering [...] Read more.

A specific concept of structural model is used as a background for discussing the structurality of its parameterization. Conditions for a structural model to be also causal are examined. Difficulties and pitfalls arising from the parameterization are analyzed. In particular, pitfalls when considering alternative parameterizations of a same model are shown to have lead to ungrounded conclusions in the literature. Discussions of observationally equivalent models related to different economic mechanisms are used to make clear the connection between an economically meaningful parameterization and an economically meaningful decomposition of a complex model. The design of economic policy is used for drawing some practical implications of the proposed analysis. Full article

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753 KiB

Open AccessArticle

Distribution of Budget Shares for Food: An Application of Quantile Regression to Food Security ¹

by Charles B. Moss, James F. Oehmke, Alexandre Lyambabaje and Andrew Schmitz

Econometrics 2016, 4(2), 22; https://doi.org/10.3390/econometrics4020022 - 08 Apr 2016

Cited by 8 | Viewed by 7390

Abstract

This study examines, using quantile regression, the linkage between food security and efforts to enhance smallholder coffee producer incomes in Rwanda. Even though in Rwanda smallholder coffee producer incomes have increased, inhabitants these areas still experience stunting and wasting. This study examines whether [...] Read more.

This study examines, using quantile regression, the linkage between food security and efforts to enhance smallholder coffee producer incomes in Rwanda. Even though in Rwanda smallholder coffee producer incomes have increased, inhabitants these areas still experience stunting and wasting. This study examines whether the distribution of the income elasticity for food is the same for coffee and noncoffee growing provinces. We find that that the share of expenditures on food is statistically different in coffee growing and noncoffee growing provinces. Thus, the increase in expenditure on food is smaller for coffee growing provinces than noncoffee growing provinces. Full article

(This article belongs to the Special Issue Quantile Methods)

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264 KiB

Open AccessArticle

Unit Root Tests: The Role of the Univariate Models Implied by Multivariate Time Series

by Nunzio Cappuccio and Diego Lubian

Econometrics 2016, 4(2), 21; https://doi.org/10.3390/econometrics4020021 - 07 Apr 2016

Cited by 1 | Viewed by 5631

Abstract

In cointegration analysis, it is customary to test the hypothesis of unit roots separately for each single time series. In this note, we point out that this procedure may imply large size distortion of the unit root tests if the DGP is a [...] Read more.

In cointegration analysis, it is customary to test the hypothesis of unit roots separately for each single time series. In this note, we point out that this procedure may imply large size distortion of the unit root tests if the DGP is a VAR. It is well-known that univariate models implied by a VAR data generating process necessarily have a finite order MA component. This feature may explain why an MA component has often been found in univariate ARIMA models for economic time series. Thereby, it has important implications for unit root tests in univariate settings given the well-known size distortion of popular unit root test in the presence of a large negative coefficient in the MA component. In a small simulation experiment, considering several popular unit root tests and the ADF sieve bootstrap unit tests, we find that, besides the well known size distortion effect, there can be substantial differences in size distortion according to which univariate time series is tested for the presence of a unit root. Full article

336 KiB

Open AccessArticle

Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence

by Ba Chu and Stephen Satchell

Econometrics 2016, 4(2), 20; https://doi.org/10.3390/econometrics4020020 - 29 Mar 2016

Cited by 1 | Viewed by 5637

Abstract

This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by maximizing [...] Read more.

This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by maximizing Shannon entropy to yield a proper copula such that known dependence structures of data (e.g., measures of association) are matched to their empirical counterparts. In fact the problem of maximizing the entropy of copulas is the dual to the problem of minimizing the Kullback-Leibler cross entropy (KLCE) of joint probability densities when the marginal probability densities are fixed. Our simulation study shows that the proposed MEC estimator can potentially outperform many other copula estimators in finite samples. Full article

(This article belongs to the Special Issue Recent Developments of Financial Econometrics)

633 KiB

Open AccessArticle

A Method for Measuring Treatment Effects on the Treated without Randomization

by P.A.V.B. Swamy, Stephen G. Hall, George S. Tavlas, I-Lok Chang, Heather D. Gibson, William H. Greene and Jatinder S. Mehta

Econometrics 2016, 4(2), 19; https://doi.org/10.3390/econometrics4020019 - 25 Mar 2016

Cited by 4 | Viewed by 6134

Abstract

This paper contributes to the literature on the estimation of causal effects by providing an analytical formula for individual specific treatment effects and an empirical methodology that allows us to estimate these effects. We derive the formula from a general model with minimal [...] Read more.

This paper contributes to the literature on the estimation of causal effects by providing an analytical formula for individual specific treatment effects and an empirical methodology that allows us to estimate these effects. We derive the formula from a general model with minimal restrictions, unknown functional form and true unobserved variables such that it is a credible model of the underlying real world relationship. Subsequently, we manipulate the model in order to put it in an estimable form. In contrast to other empirical methodologies, which derive average treatment effects, we derive an analytical formula that provides estimates of the treatment effects on each treated individual. We also provide an empirical example that illustrates our methodology. Full article

(This article belongs to the Special Issue Recent Developments of Financial Econometrics)

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Econometrics, Volume 4, Issue 2 (June 2016) – 11 articles

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