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Econometrics, Volume 7, Issue 2 (June 2019)

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Open AccessArticle
Interval-Based Hypothesis Testing and Its Applications to Economics and Finance
Econometrics 2019, 7(2), 21; https://doi.org/10.3390/econometrics7020021
Received: 26 March 2019 / Revised: 6 May 2019 / Accepted: 7 May 2019 / Published: 15 May 2019
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Abstract
This paper presents a brief review of interval-based hypothesis testing, widely used in bio-statistics, medical science, and psychology, namely, tests for minimum-effect, equivalence, and non-inferiority. We present the methods in the contexts of a one-sample t-test and a test for linear restrictions [...] Read more.
This paper presents a brief review of interval-based hypothesis testing, widely used in bio-statistics, medical science, and psychology, namely, tests for minimum-effect, equivalence, and non-inferiority. We present the methods in the contexts of a one-sample t-test and a test for linear restrictions in a regression. We present applications in testing for market efficiency, validity of asset-pricing models, and persistence of economic time series. We argue that, from the point of view of economics and finance, interval-based hypothesis testing provides more sensible inferential outcomes than those based on point-null hypothesis. We propose that interval-based tests be routinely employed in empirical research in business, as an alternative to point null hypothesis testing, especially in the new era of big data. Full article
(This article belongs to the Special Issue Towards a New Paradigm for Statistical Evidence)
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Open AccessArticle
Background Indicators
Econometrics 2019, 7(2), 20; https://doi.org/10.3390/econometrics7020020
Received: 18 December 2018 / Revised: 19 April 2019 / Accepted: 19 April 2019 / Published: 14 May 2019
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Abstract
It is customary to assume that an indicator of a latent variable is driven by the latent variable and some random noise. In contrast, a background indicator is also systematically influenced by variables outside the structural model of interest. Background indicators deserve attention [...] Read more.
It is customary to assume that an indicator of a latent variable is driven by the latent variable and some random noise. In contrast, a background indicator is also systematically influenced by variables outside the structural model of interest. Background indicators deserve attention because in empirical work they are difficult to distinguish from ordinary effect indicators. This paper assesses instrumental variable (IV) estimation of the effect of a latent variable in a linear model when a background indicator replaces the latent variable. It turns out that IV estimates are inconsistent in many important cases. In some cases, the estimates capture causal effects of the indicator rather than causal effects of the latent variable. A simulation experiment that considers the impact of economic uncertainty on aggregate consumption illustrates some of the results. Full article
Open AccessArticle
Covariance Prediction in Large Portfolio Allocation
Econometrics 2019, 7(2), 19; https://doi.org/10.3390/econometrics7020019
Received: 12 November 2018 / Revised: 23 April 2019 / Accepted: 2 May 2019 / Published: 9 May 2019
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Abstract
Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. [...] Read more.
Many financial decisions, such as portfolio allocation, risk management, option pricing and hedge strategies, are based on forecasts of the conditional variances, covariances and correlations of financial returns. The paper shows an empirical comparison of several methods to predict one-step-ahead conditional covariance matrices. These matrices are used as inputs to obtain out-of-sample minimum variance portfolios based on stocks belonging to the S&P500 index from 2000 to 2017 and sub-periods. The analysis is done through several metrics, including standard deviation, turnover, net average return, information ratio and Sortino’s ratio. We find that no method is the best in all scenarios and the performance depends on the criterion, the period of analysis and the rebalancing strategy. Full article
Open AccessArticle
Important Issues in Statistical Testing and Recommended Improvements in Accounting Research
Econometrics 2019, 7(2), 18; https://doi.org/10.3390/econometrics7020018
Received: 17 December 2018 / Revised: 23 April 2019 / Accepted: 26 April 2019 / Published: 8 May 2019
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Abstract
A great deal of the accounting research published in recent years has involved statistical tests. Our paper proposes improvements to both the quality and execution of such research. We address the following limitations in current research that appear to us to be ignored [...] Read more.
A great deal of the accounting research published in recent years has involved statistical tests. Our paper proposes improvements to both the quality and execution of such research. We address the following limitations in current research that appear to us to be ignored or used inappropriately: (1) unaddressed situational effects resulting from model limitations and what has been referred to as “data carpentry,” (2) limitations and alternatives to winsorizing, (3) necessary improvements to relying on a study’s calculated “p-values” instead of on the economic or behavioral importance of the results, and (4) the information loss incurred by under-valuing what can and cannot be learned from replications. Full article
(This article belongs to the Special Issue Towards a New Paradigm for Statistical Evidence)
Open AccessArticle
Measures of Dispersion and Serial Dependence in Categorical Time Series
Econometrics 2019, 7(2), 17; https://doi.org/10.3390/econometrics7020017
Received: 21 December 2018 / Revised: 1 April 2019 / Accepted: 17 April 2019 / Published: 22 April 2019
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Abstract
The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. [...] Read more.
The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed serial dependence in categorical time series, we consider three types of κ -measures. By analyzing bias properties, it is shown that always one of the κ -measures is related to one of the above-mentioned dispersion measures. For doing statistical inference based on the sample versions of these dispersion and dependence measures, knowledge on their distribution is required. Therefore, we study the asymptotic distributions and bias corrections of the considered dispersion and dependence measures, and we investigate the finite-sample performance of the resulting asymptotic approximations with simulations. The application of the measures is illustrated with real-data examples from politics, economics and biology. Full article
(This article belongs to the Special Issue Discrete-Valued Time Series: Modelling, Estimation and Forecasting)
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