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Econometrics, Volume 7, Issue 2 (June 2019) – 12 articles

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Open AccessArticle
A Semi-Parametric Approach to the Oaxaca–Blinder Decomposition with Continuous Group Variable and Self-Selection
Econometrics 2019, 7(2), 28; https://doi.org/10.3390/econometrics7020028 - 21 Jun 2019
Viewed by 3387
Abstract
This paper presents an extension to the Oaxaca–Blinder decomposition with continuous groups using a semiparametric approach known as varying coefficients model. To account for potential self-selection into the continuum of groups, the use of inverse mills ratios is expanded upon following the literature [...] Read more.
This paper presents an extension to the Oaxaca–Blinder decomposition with continuous groups using a semiparametric approach known as varying coefficients model. To account for potential self-selection into the continuum of groups, the use of inverse mills ratios is expanded upon following the literature on endogenous selection. The flexibility of this methodology may allow detecting heterogeneity when analyzing endogenous dose treatments effects, as well as correcting for endogeneity when analyzing the heterogeneous partial effects across the continuous group variable. For illustration, the methodology is used to revisit the impact of body weight on wages, using body mass index (BMI) as the continuum of groups, finding evidence that body weight has a negative, but decreasing impact on wages for both white men and women. Full article
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Open AccessArticle
Looking Backward and Looking Forward
Econometrics 2019, 7(2), 27; https://doi.org/10.3390/econometrics7020027 - 14 Jun 2019
Viewed by 2797
Abstract
Filtering has had a profound impact as a device of perceiving information and deriving agent expectations in dynamic economic models. For an abstract economic system, this paper shows that the foundation of applying the filtering method corresponds to the existence of a conditional [...] Read more.
Filtering has had a profound impact as a device of perceiving information and deriving agent expectations in dynamic economic models. For an abstract economic system, this paper shows that the foundation of applying the filtering method corresponds to the existence of a conditional expectation as an equilibrium process. Agent-based rational behavior of looking backward and looking forward is generalized to a conditional expectation process where the economic system is approximated by a class of models, which can be represented and estimated without information loss. The proposed framework elucidates the range of applications of a general filtering device and is not limited to a particular model class such as rational expectations. Full article
(This article belongs to the Special Issue Filtering)
Open AccessArticle
A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals
Econometrics 2019, 7(2), 26; https://doi.org/10.3390/econometrics7020026 - 04 Jun 2019
Cited by 7 | Viewed by 3704
Abstract
There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none [...] Read more.
There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone. Full article
(This article belongs to the Special Issue Towards a New Paradigm for Statistical Evidence)
Open AccessArticle
Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric
Econometrics 2019, 7(2), 25; https://doi.org/10.3390/econometrics7020025 - 31 May 2019
Viewed by 2957
Abstract
It is well known that efficient estimation of average treatment effects can be obtained by the method of inverse propensity score weighting, using the estimated propensity score, even when the true one is known. When the true propensity score is unknown but parametric, [...] Read more.
It is well known that efficient estimation of average treatment effects can be obtained by the method of inverse propensity score weighting, using the estimated propensity score, even when the true one is known. When the true propensity score is unknown but parametric, it is conjectured from the literature that we still need nonparametric propensity score estimation to achieve the efficiency. We formalize this argument and further identify the source of the efficiency loss arising from parametric estimation of the propensity score. We also provide an intuition of why this overfitting is necessary. Our finding suggests that, even when we know that the true propensity score belongs to a parametric class, we still need to estimate the propensity score by a nonparametric method in applications. Full article
Open AccessFeature PaperShort Note
On Using the t-Ratio as a Diagnostic
Econometrics 2019, 7(2), 24; https://doi.org/10.3390/econometrics7020024 - 29 May 2019
Viewed by 2895
Abstract
The t-ratio has not one but two uses in econometrics, which should be carefully distinguished. It is used as a test and also as a diagnostic. I emphasize that the commonly-used estimators are in fact pretest estimators, and argue in favor of [...] Read more.
The t-ratio has not one but two uses in econometrics, which should be carefully distinguished. It is used as a test and also as a diagnostic. I emphasize that the commonly-used estimators are in fact pretest estimators, and argue in favor of an improved (continuous) version of pretesting, called model averaging. Full article
(This article belongs to the Special Issue Towards a New Paradigm for Statistical Evidence)
Open AccessArticle
Threshold Regression with Endogeneity for Short Panels
Econometrics 2019, 7(2), 23; https://doi.org/10.3390/econometrics7020023 - 22 May 2019
Viewed by 3047
Abstract
This paper considers the estimation of dynamic threshold regression models with fixed effects using short panel data. We examine a two-step method, where the threshold parameter is estimated nonparametrically at the N-rate and the remaining parameters are estimated by GMM at the [...] Read more.
This paper considers the estimation of dynamic threshold regression models with fixed effects using short panel data. We examine a two-step method, where the threshold parameter is estimated nonparametrically at the N-rate and the remaining parameters are estimated by GMM at the N -rate. We provide simulation results that illustrate advantages of the new method in comparison with pure GMM estimation. The simulations also highlight the importance of the choice of instruments in GMM estimation. Full article
Open AccessArticle
Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model
Econometrics 2019, 7(2), 22; https://doi.org/10.3390/econometrics7020022 - 21 May 2019
Cited by 2 | Viewed by 3040
Abstract
In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, [...] Read more.
In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power. Full article
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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 - 15 May 2019
Cited by 1 | Viewed by 3439
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 - 14 May 2019
Viewed by 2972
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
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Open AccessArticle
Covariance Prediction in Large Portfolio Allocation
Econometrics 2019, 7(2), 19; https://doi.org/10.3390/econometrics7020019 - 09 May 2019
Cited by 4 | Viewed by 3267
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 - 08 May 2019
Cited by 2 | Viewed by 3226
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 - 22 Apr 2019
Cited by 1 | Viewed by 3367
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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