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Econometrics, Volume 5, Issue 2 (June 2017)

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Editorial

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Open AccessEditorial Unit Roots and Structural Breaks
Econometrics 2017, 5(2), 22; doi:10.3390/econometrics5020022
Received: 26 May 2017 / Revised: 26 May 2017 / Accepted: 27 May 2017 / Published: 30 May 2017
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(This article belongs to the Special Issue Unit Roots and Structural Breaks)

Research

Jump to: Editorial

Open AccessArticle Copula–Based vMEM Specifications versus Alternatives: The Case of Trading Activity
Econometrics 2017, 5(2), 16; doi:10.3390/econometrics5020016
Received: 4 March 2016 / Revised: 3 March 2017 / Accepted: 5 April 2017 / Published: 12 April 2017
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Abstract
We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past values and conditional
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We discuss several multivariate extensions of the Multiplicative Error Model to take into account dynamic interdependence and contemporaneously correlated innovations (vector MEM or vMEM). We suggest copula functions to link Gamma marginals of the innovations, in a specification where past values and conditional expectations of the variables can be simultaneously estimated. Results with realized volatility, volumes and number of trades of the JNJ stock show that significantly superior realized volatility forecasts are delivered with a fully interdependent vMEM relative to a single equation. Alternatives involving log–Normal or semiparametric formulations produce substantially equivalent results. Full article
(This article belongs to the Special Issue Financial High-Frequency Data)
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Open AccessArticle Selecting the Lag Length for the MGLS Unit Root Tests with Structural Change: A Warning Note for Practitioners Based on Simulations
Econometrics 2017, 5(2), 17; doi:10.3390/econometrics5020017
Received: 31 August 2016 / Revised: 31 March 2017 / Accepted: 4 April 2017 / Published: 16 April 2017
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Abstract
This is a simulation-based warning note for practitioners who use the MGLS unit root tests in the context of structural change using different selection lag length criteria. With T=100, we find severe oversize problems when using some
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This is a simulation-based warning note for practitioners who use the M G L S unit root tests in the context of structural change using different selection lag length criteria. With T = 100 , we find severe oversize problems when using some criteria, while other criteria produce an undersizing behavior. In view of this dilemma, we do not recommend using these tests. While such behavior tends to disappear when T = 250 , it is important to note that most empirical applications use smaller sample sizes such as T = 100 or T = 150 . The A D F G L S test does not present an oversizing or undersizing problem. The only disadvantage of the A D F G L S test arises in the presence of M A ( 1 ) negative correlation, in which case the M G L S tests are preferable, but in all other cases they are very undersized. When there is a break in the series, selecting the breakpoint using the Supremum method greatly improves the results relative to the Infimum method. Full article
(This article belongs to the Special Issue Unit Roots and Structural Breaks)
Open AccessArticle The Univariate Collapsing Method for Portfolio Optimization
Econometrics 2017, 5(2), 18; doi:10.3390/econometrics5020018
Received: 9 October 2016 / Revised: 4 February 2017 / Accepted: 17 February 2017 / Published: 5 May 2017
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Abstract
The univariate collapsing method (UCM) for portfolio optimization is based on obtaining the predictive mean and a risk measure such as variance or expected shortfall of the univariate pseudo-return series generated from a given set of portfolio weights and multivariate set of assets
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The univariate collapsing method (UCM) for portfolio optimization is based on obtaining the predictive mean and a risk measure such as variance or expected shortfall of the univariate pseudo-return series generated from a given set of portfolio weights and multivariate set of assets under interest and, via simulation or optimization, repeating this process until the desired portfolio weight vector is obtained. The UCM is well-known conceptually, straightforward to implement, and possesses several advantages over use of multivariate models, but, among other things, has been criticized for being too slow. As such, it does not play prominently in asset allocation and receives little attention in the academic literature. This paper proposes use of fast model estimation methods combined with new heuristics for sampling, based on easily-determined characteristics of the data, to accelerate and optimize the simulation search. An extensive empirical analysis confirms the viability of the method. Full article
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Open AccessArticle Maximum Likelihood Estimation of the I(2) Model under Linear Restrictions
Econometrics 2017, 5(2), 19; doi:10.3390/econometrics5020019
Received: 27 February 2017 / Revised: 2 May 2017 / Accepted: 8 May 2017 / Published: 15 May 2017
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Abstract
Estimation of the I(2) cointegrated vector autoregressive (CVAR) model is considered. Without further restrictions, estimation of the I(1) model is by reduced-rank regression (Anderson (1951)). Maximum likelihood estimation of I(2) models, on the other hand, always requires iteration. This paper presents a new
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Estimation of the I(2) cointegrated vector autoregressive (CVAR) model is considered. Without further restrictions, estimation of the I(1) model is by reduced-rank regression (Anderson (1951)). Maximum likelihood estimation of I(2) models, on the other hand, always requires iteration. This paper presents a new triangular representation of the I(2) model. This is the basis for a new estimation procedure of the unrestricted I(2) model, as well as the I(2) model with linear restrictions imposed. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle Copula-Based Factor Models for Multivariate Asset Returns
Econometrics 2017, 5(2), 20; doi:10.3390/econometrics5020020
Received: 29 September 2016 / Revised: 3 May 2017 / Accepted: 3 May 2017 / Published: 17 May 2017
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Abstract
Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to
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Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of them are based on vine copulas, and they differ in the choice of the regular vine structure. In this article, we consider a copula autoregressive (COPAR) approach to model the dependence of unobserved multivariate factors resulting from two dynamic factor models. However, the proposed methodology is general and applicable to several factor models as well as to other copula models for stationary multivariate time series. An empirical study illustrates the forecasting superiority of our approach for constructing an optimal portfolio of U.S. industrial stocks in the mean-variance framework. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting
Econometrics 2017, 5(2), 21; doi:10.3390/econometrics5020021
Received: 25 November 2016 / Revised: 26 April 2017 / Accepted: 8 May 2017 / Published: 23 May 2017
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Abstract
Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent
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Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent factor. We use adaptive rejection Metropolis sampling (ARMS) within Gibbs sampling for posterior simulation: Gibbs sampling enables application to Bayesian problems, while ARMS is an adaptive strategy that replaces traditional Metropolis-Hastings updates, which typically require careful tuning. Our simulation study shows favorable performance of our proposed approach both in terms of sampling efficiency and accuracy. We provide an extensive application example using historical data on European financial stocks that forecasts portfolio Value at Risk (VaR) and Expected Shortfall (ES). Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle Dependence between Stock Returns of Italian Banks and the Sovereign Risk
Econometrics 2017, 5(2), 23; doi:10.3390/econometrics5020023
Received: 16 March 2017 / Revised: 1 June 2017 / Accepted: 5 June 2017 / Published: 8 June 2017
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Abstract
We analyze the interdependence between the government yield spread and stock returns of the banking sector in Italy during the years 2003–2015. In a first step, we find that the Spearman’s rank correlation between the yield spread and the Italian banking system changed
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We analyze the interdependence between the government yield spread and stock returns of the banking sector in Italy during the years 2003–2015. In a first step, we find that the Spearman’s rank correlation between the yield spread and the Italian banking system changed significantly after September 2008. According to this finding, we split the time window in two sub-periods. While we show that the dependence between the banking industry and changes in the yield spread increased significantly in the second time interval, we find no contagion effects from changes in the yield spread to returns of the banking system. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle A Spatial Econometric Analysis of the Calls to the Portuguese National Health Line
Econometrics 2017, 5(2), 24; doi:10.3390/econometrics5020024
Received: 31 January 2017 / Revised: 30 May 2017 / Accepted: 31 May 2017 / Published: 16 June 2017
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Abstract
The Portuguese National Health Line, LS24, is an initiative of the Portuguese Health Ministry which seeks to improve accessibility to health care and to rationalize the use of existing resources by directing users to the most appropriate institutions of the national public health
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The Portuguese National Health Line, LS24, is an initiative of the Portuguese Health Ministry which seeks to improve accessibility to health care and to rationalize the use of existing resources by directing users to the most appropriate institutions of the national public health services. This study aims to describe and evaluate the use of LS24. Since for LS24 data, the location attribute is an important source of information to describe its use, this study analyses the number of calls received, at a municipal level, under two different spatial econometric approaches. This analysis is important for future development of decision support indicators in a hospital context, based on the economic impact of the use of this health line. Considering the discrete nature of data, the number of calls to LS24 in each municipality is better modelled by a Poisson model, with some possible covariates: demographic, socio-economic information, characteristics of the Portuguese health system and development indicators. In order to explain model spatial variability, the data autocorrelation can be explained in a Bayesian setting through different hierarchical log-Poisson regression models. A different approach uses an autoregressive methodology, also for count data. A log-Poisson model with a spatial lag autocorrelation component is further considered, better framed under a Bayesian paradigm. With this empirical study we find strong evidence for a spatial structure in the data and obtain similar conclusions with both perspectives of the analysis. This supports the view that the addition of a spatial structure to the model improves estimation, even in the case where some relevant covariates have been included. Full article
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Open AccessArticle Improved Inference on Cointegrating Vectors in the Presence of a near Unit Root Using Adjusted Quantiles
Econometrics 2017, 5(2), 25; doi:10.3390/econometrics5020025
Received: 20 April 2017 / Revised: 25 May 2017 / Accepted: 7 June 2017 / Published: 14 June 2017
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Abstract
It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the
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It is well known that inference on the cointegrating relations in a vector autoregression (CVAR) is difficult in the presence of a near unit root. The test for a given cointegration vector can have rejection probabilities under the null, which vary from the nominal size to more than 90%. This paper formulates a CVAR model allowing for multiple near unit roots and analyses the asymptotic properties of the Gaussian maximum likelihood estimator. Then two critical value adjustments suggested by McCloskey (2017) for the test on the cointegrating relations are implemented for the model with a single near unit root, and it is found by simulation that they eliminate the serious size distortions, with a reasonable power for moderate values of the near unit root parameter. The findings are illustrated with an analysis of a number of different bivariate DGPs. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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Open AccessArticle The Realized Hierarchical Archimedean Copula in Risk Modelling
Econometrics 2017, 5(2), 26; doi:10.3390/econometrics5020026
Received: 31 December 2016 / Revised: 2 June 2017 / Accepted: 6 June 2017 / Published: 15 June 2017
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Abstract
This paper introduces the concept of the realized hierarchical Archimedean copula (rHAC). The proposed approach inherits the ability of the copula to capture the dependencies among financial time series, and combines it with additional information contained in high-frequency data. The considered model does
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This paper introduces the concept of the realized hierarchical Archimedean copula (rHAC). The proposed approach inherits the ability of the copula to capture the dependencies among financial time series, and combines it with additional information contained in high-frequency data. The considered model does not suffer from the curse of dimensionality, and is able to accurately predict high-dimensional distributions. This flexibility is obtained by using a hierarchical structure in the copula. The time variability of the model is provided by daily forecasts of the realized correlation matrix, which is used to estimate the structure and the parameters of the rHAC. Extensive simulation studies show the validity of the estimator based on this realized correlation matrix, and its performance, in comparison to the benchmark models. The application of the estimator to one-day-ahead Value at Risk (VaR) prediction using high-frequency data exhibits good forecasting properties for a multivariate portfolio. Full article
(This article belongs to the Special Issue Recent Developments in Copula Models)
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Open AccessArticle Sustainable Financial Obligations and Crisis Cycles
Econometrics 2017, 5(2), 27; doi:10.3390/econometrics5020027
Received: 28 February 2017 / Revised: 23 May 2017 / Accepted: 13 June 2017 / Published: 22 June 2017
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Abstract
The ability to distinguish between sustainable and excessive debt developments is crucial for securing economic stability. By studying US private sector credit loss dynamics, we show that this distinction can be made based on a measure of the incipient aggregate liquidity constraint, the
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The ability to distinguish between sustainable and excessive debt developments is crucial for securing economic stability. By studying US private sector credit loss dynamics, we show that this distinction can be made based on a measure of the incipient aggregate liquidity constraint, the financial obligations ratio. Specifically, as this variable rises, the interaction between credit losses and the business cycle increases, albeit with different intensity depending on whether the problems originate in the household or the business sector. This occurs 1–2 years before each recession in the sample. Our results have implications for macroprudential policy and countercyclical capital-buffers. Full article
(This article belongs to the Special Issue Recent Developments in Cointegration)
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