Econometrics

1236 KiB

Open AccessArticle

A Joint Specification Test for Response Probabilities in Unordered Multinomial Choice Models

by Masamune Iwasawa

Econometrics 2015, 3(3), 667-697; https://doi.org/10.3390/econometrics3030667 - 16 Sep 2015

Cited by 1 | Viewed by 4811

Estimation results obtained by parametric models may be seriously misleading when the model is misspecified or poorly approximates the true model. This study proposes a test that jointly tests the specifications of multiple response probabilities in unordered multinomial choice models. The test statistic [...] Read more.

Estimation results obtained by parametric models may be seriously misleading when the model is misspecified or poorly approximates the true model. This study proposes a test that jointly tests the specifications of multiple response probabilities in unordered multinomial choice models. The test statistic is asymptotically chi-square distributed, consistent against a fixed alternative and able to detect a local alternative approaching to the null at a rate slower than the parametric rate. We show that rejection regions can be calculated by a simple parametric bootstrap procedure, when the sample size is small. The size and power of the tests are investigated by Monte Carlo experiments. Full article

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

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229 KiB

Open AccessArticle

On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study

by Antonio F. Galvao and Gabriel Montes-Rojas

Econometrics 2015, 3(3), 654-666; https://doi.org/10.3390/econometrics3030654 - 10 Sep 2015

Cited by 23 | Viewed by 5514

Abstract

This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling [...] Read more.

This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temporal resampling is performed from the time series. Finally, a more general resampling scheme, which considers sampling from both the cross-sectional and temporal dimensions, is introduced. The bootstrap algorithms are computationally attractive and easy to use in practice. We evaluate the performance of the bootstrap confidence interval by means of Monte Carlo simulations. The results show that the bootstrap methods have good finite sample performance for both location and location-scale models. Full article

(This article belongs to the Special Issue Quantile Methods)

394 KiB

Open AccessArticle

A New Family of Consistent and Asymptotically-Normal Estimators for the Extremal Index

by Jose Olmo

Econometrics 2015, 3(3), 633-653; https://doi.org/10.3390/econometrics3030633 - 28 Aug 2015

Cited by 1 | Viewed by 5031

Abstract

The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. to stationary sequences. One important property of this parameter is that its inverse determines the degree of clustering in the extremes. This article introduces a novel interpretation [...] Read more.

The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. to stationary sequences. One important property of this parameter is that its inverse determines the degree of clustering in the extremes. This article introduces a novel interpretation of the extremal index as a limiting probability characterized by two Poisson processes and a simple family of estimators derived from this new characterization. Unlike most estimators for θ in the literature, this estimator is consistent, asymptotically normal and very stable across partitions of the sample. Further, we show in an extensive simulation study that this estimator outperforms in finite samples the logs, blocks and runs estimation methods. Finally, we apply this new estimator to test for clustering of extremes in monthly time series of unemployment growth and inflation rates and conclude that runs of large unemployment rates are more prolonged than periods of high inflation. Full article

(This article belongs to the Special Issue Quantile Methods)

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623 KiB

Open AccessArticle

Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting

by Stanislav Anatolyev and Stanislav Khrapov

Econometrics 2015, 3(3), 610-632; https://doi.org/10.3390/econometrics3030610 - 10 Aug 2015

Cited by 1 | Viewed by 6005

Abstract

Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the return distribution affect the [...] Read more.

Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the return distribution affect the quality of estimation of the volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions are more precisely estimated when no variance targeting is employed. Bias properties are exacerbated for a heavier-tailed distribution of standardized returns, while the distributional asymmetry has little or moderate impact, these phenomena tending to be more pronounced under variance targeting. Some effects further intensify if one uses ML based on a leptokurtic distribution in place of normal QML. The sample size has also a more favorable effect on estimation precision when no variance targeting is used. Thus, if computational costs are not prohibitive, variance targeting should probably be avoided. Full article

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

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303 KiB

Open AccessArticle

A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts

by Hossein Hassani and Emmanuel Sirimal Silva

Econometrics 2015, 3(3), 590-609; https://doi.org/10.3390/econometrics3030590 - 04 Aug 2015

Cited by 109 | Viewed by 10583

Abstract

This paper introduces a complement statistical test for distinguishing between the predictive accuracy of two sets of forecasts. We propose a non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA [...] Read more.

This paper introduces a complement statistical test for distinguishing between the predictive accuracy of two sets of forecasts. We propose a non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. We perform a simulation study for the size and power of the proposed test and report the results for different noise distributions, sample sizes and forecasting horizons. The simulation results indicate that the KSPA test is correctly sized, and robust in the face of varying forecasting horizons and sample sizes along with significant accuracy gains reported especially in the case of small sample sizes. Real world applications are also considered to illustrate the applicability of the proposed KSPA test in practice. Full article

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250 KiB

Open AccessArticle

A Spectral Model of Turnover Reduction

by Zura Kakushadze

Econometrics 2015, 3(3), 577-589; https://doi.org/10.3390/econometrics3030577 - 29 Jul 2015

Cited by 2 | Viewed by 4359

Abstract

We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction [...] Read more.

We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corresponding eigenvector of the alpha correlation matrix. Full article

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297 KiB

Open AccessShort Note

A Note on the Asymptotic Normality of the Kernel Deconvolution Density Estimator with Logarithmic Chi-Square Noise

by Yang Zu

Econometrics 2015, 3(3), 561-576; https://doi.org/10.3390/econometrics3030561 - 21 Jul 2015

Cited by 3 | Viewed by 4892

Abstract

This paper studies the asymptotic normality for the kernel deconvolution estimator when the noise distribution is logarithmic chi-square; both identical and independently distributed observations and strong mixing observations are considered. The dependent case of the result is applied to obtain the pointwise asymptotic [...] Read more.

This paper studies the asymptotic normality for the kernel deconvolution estimator when the noise distribution is logarithmic chi-square; both identical and independently distributed observations and strong mixing observations are considered. The dependent case of the result is applied to obtain the pointwise asymptotic distribution of the deconvolution volatility density estimator in discrete-time stochastic volatility models. Full article

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607 KiB

Open AccessArticle

New Graphical Methods and Test Statistics for Testing Composite Normality

by Marc S. Paolella

Econometrics 2015, 3(3), 532-560; https://doi.org/10.3390/econometrics3030532 - 15 Jul 2015

Cited by 4 | Viewed by 5407

Abstract

Several graphical methods for testing univariate composite normality from an i.i.d. sample are presented. They are endowed with correct simultaneous error bounds and yield size-correct tests. As all are based on the empirical CDF, they are also consistent for all alternatives. For one [...] Read more.

Several graphical methods for testing univariate composite normality from an i.i.d. sample are presented. They are endowed with correct simultaneous error bounds and yield size-correct tests. As all are based on the empirical CDF, they are also consistent for all alternatives. For one test, called the modified stabilized probability test, or MSP, a highly simplified computational method is derived, which delivers the test statistic and also a highly accurate p-value approximation, essentially instantaneously. The MSP test is demonstrated to have higher power against asymmetric alternatives than the well-known and powerful Jarque-Bera test. A further size-correct test, based on combining two test statistics, is shown to have yet higher power. The methodology employed is fully general and can be applied to any i.i.d. univariate continuous distribution setting. Full article

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238 KiB

Open AccessArticle

Efficient Estimation in Heteroscedastic Varying Coefficient Models

by Chuanhua Wei and Lijie Wan

Econometrics 2015, 3(3), 525-531; https://doi.org/10.3390/econometrics3030525 - 15 Jul 2015

Cited by 1 | Viewed by 4710

Abstract

This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct some simulation to illustrate the [...] Read more.

This paper considers statistical inference for the heteroscedastic varying coefficient model. We propose an efficient estimator for coefficient functions that is more efficient than the conventional local-linear estimator. We establish asymptotic normality for the proposed estimator and conduct some simulation to illustrate the performance of the proposed method. Full article

500 KiB

Open AccessArticle

Consistency in Estimation and Model Selection of Dynamic Panel Data Models with Fixed Effects

by Guangjie Li

Econometrics 2015, 3(3), 494-524; https://doi.org/10.3390/econometrics3030494 - 10 Jul 2015

Cited by 2 | Viewed by 5428

Abstract

We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002) does not necessarily lead to consistent estimation of common parameters when some [...] Read more.

We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002) does not necessarily lead to consistent estimation of common parameters when some true exogenous regressors are excluded. We propose a data dependent way to specify the prior of the autoregressive coefficient and argue for comparing different model specifications before parameter estimation. Model selection properties of Bayes factors and Bayesian information criterion (BIC) are investigated. When model uncertainty is substantial, we recommend the use of Bayesian Model Averaging to obtain point estimators with lower root mean squared errors (RMSE). We also study the implications of different levels of inclusion probabilities by simulations. Full article

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299 KiB

Open AccessArticle

A New Approach to Model Verification, Falsification and Selection

by Andrew J. Buck and George M. Lady

Econometrics 2015, 3(3), 466-493; https://doi.org/10.3390/econometrics3030466 - 29 Jun 2015

Cited by 2 | Viewed by 5464

Abstract

This paper shows that a qualitative analysis, i.e., an assessment of the consistency of a hypothesized sign pattern for structural arrays with the sign pattern of the estimated reduced form, can always provide decisive insight into a model’s validity both in general [...] Read more.

This paper shows that a qualitative analysis, i.e., an assessment of the consistency of a hypothesized sign pattern for structural arrays with the sign pattern of the estimated reduced form, can always provide decisive insight into a model’s validity both in general and compared to other models. Qualitative analysis can show that it is impossible for some models to have generated the data used to estimate the reduced form, even though standard specification tests might show the model to be adequate. A partially specified structural hypothesis can be falsified by estimating as few as one reduced form equation. Zero restrictions in the structure can themselves be falsified. It is further shown how the information content of the hypothesized structural sign patterns can be measured using a commonly applied concept of statistical entropy. The lower the hypothesized structural sign pattern’s entropy, the more a priori information it proposes about the sign pattern of the estimated reduced form. As an hypothesized structural sign pattern has a lower entropy, it is more subject to type 1 error and less subject to type 2 error. Three cases illustrate the approach taken here. Full article

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Econometrics, Volume 3, Issue 3 (September 2015) – 11 articles , Pages 466-697

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