Econometrics

7380 KiB

Open AccessArticle

Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality

by Thibault Vatter, Hau-Tieng Wu, Valérie Chavez-Demoulin and Bin Yu

Econometrics 2015, 3(4), 864-887; https://doi.org/10.3390/econometrics3040864 - 18 Dec 2015

Cited by 4 | Viewed by 7519

We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically time-varying and [...] Read more.

We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically time-varying and evolve in real time. We provide the associated estimators and use simulations to show that they behave adequately in the presence of jumps and heteroskedastic and heavy-tailed noise. A study of exchange rate returns sampled from 2010 to 2013 suggests that failing to factor in the seasonality’s dynamic properties may lead to misestimation of the intraday spot volatility. Full article

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

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580 KiB

Open AccessArticle

Bootstrap Tests for Overidentification in Linear Regression Models

by Russell Davidson and James G. MacKinnon

Econometrics 2015, 3(4), 825-863; https://doi.org/10.3390/econometrics3040825 - 9 Dec 2015

Cited by 5 | Viewed by 6937

Abstract

We study the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent [...] Read more.

We study the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent random variables and two nuisance parameters. The distributions of the statistics are shown to have an ill-defined limit as the parameter that determines the strength of the instruments tends to zero and as the correlation between the disturbances of the structural and reduced-form equations tends to plus or minus one. This makes it impossible to perform reliable inference near the point at which the limit is ill-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are not too weak. We also study their power properties. Full article

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

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371 KiB

Open AccessArticle

Forecast Combination under Heavy-Tailed Errors

by Gang Cheng, Sicong Wang and Yuhong Yang

Econometrics 2015, 3(4), 797-824; https://doi.org/10.3390/econometrics3040797 - 23 Nov 2015

Cited by 1 | Viewed by 5275

Abstract

Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has [...] Read more.

Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our knowledge, little has been done to obtain reliable forecast combinations for such situations. The familiar forecast combination methods, such as simple average, least squares regression or those based on the variance-covariance of the forecasts, may perform very poorly due to the fact that outliers tend to occur, and they make these methods have unstable weights, leading to un-robust forecasts. To address this problem, in this paper, we propose two nonparametric forecast combination methods. One is specially proposed for the situations in which the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student’s t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to a shortage of data and/or an evolving data-generating process. Adaptive risk bounds of both methods are developed. They show that the resulting combined forecasts yield near optimal mean forecast errors relative to the candidate forecasts. Simulations and a real example demonstrate their superior performance in that they indeed tend to have significantly smaller prediction errors than the previous combination methods in the presence of forecast outliers. Full article

(This article belongs to the Special Issue Nonparametric Methods in Econometrics)

466 KiB

Open AccessArticle

Testing in a Random Effects Panel Data Model with Spatially Correlated Error Components and Spatially Lagged Dependent Variables

by Ming He and Kuan-Pin Lin

Econometrics 2015, 3(4), 761-796; https://doi.org/10.3390/econometrics3040761 - 9 Nov 2015

Cited by 3 | Viewed by 6103

Abstract

We propose a random effects panel data model with both spatially correlated error components and spatially lagged dependent variables. We focus on diagnostic testing procedures and derive Lagrange multiplier (LM) test statistics for a variety of hypotheses within this model. We first construct [...] Read more.

We propose a random effects panel data model with both spatially correlated error components and spatially lagged dependent variables. We focus on diagnostic testing procedures and derive Lagrange multiplier (LM) test statistics for a variety of hypotheses within this model. We first construct the joint LM test for both the individual random effects and the two spatial effects (spatial error correlation and spatial lag dependence). We then provide LM tests for the individual random effects and for the two spatial effects separately. In addition, in order to guard against local model misspecification, we derive locally adjusted (robust) LM tests based on the Bera and Yoon principle (Bera and Yoon, 1993). We conduct a small Monte Carlo simulation to show the good finite sample performances of these LM test statistics and revisit the cigarette demand example in Baltagi and Levin (1992) to illustrate our testing procedures. Full article

(This article belongs to the Special Issue Spatial Econometrics)

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1327 KiB

Open AccessArticle

Forecasting Interest Rates Using Geostatistical Techniques

by Giuseppe Arbia and Michele Di Marcantonio

Econometrics 2015, 3(4), 733-760; https://doi.org/10.3390/econometrics3040733 - 9 Nov 2015

Cited by 3 | Viewed by 7690

Abstract

Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we [...] Read more.

Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we apply the proposed method to forecast Euro Zero Rates (2003–2014) using the Ordinary Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield curves is proposed. The results show that the model is characterized by good levels of predictions’ accuracy and it is competitive with the other forecasting models considered. Full article

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

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472 KiB

Open AccessArticle

Counterfactual Distributions in Bivariate Models—A Conditional Quantile Approach

by Javier Alejo and Nicolás Badaracco

Econometrics 2015, 3(4), 719-732; https://doi.org/10.3390/econometrics3040719 - 9 Nov 2015

Viewed by 5245

Abstract

This paper proposes a methodology to incorporate bivariate models in numerical computations of counterfactual distributions. The proposal is to extend the works of Machado and Mata (2005) and Melly (2005) using the grid method to generate pairs of random variables. This contribution allows [...] Read more.

This paper proposes a methodology to incorporate bivariate models in numerical computations of counterfactual distributions. The proposal is to extend the works of Machado and Mata (2005) and Melly (2005) using the grid method to generate pairs of random variables. This contribution allows incorporating the effect of intra-household decision making in counterfactual decompositions of changes in income distribution. An application using data from five latin american countries shows that this approach substantially improves the goodness of fit to the empirical distribution. However, the exercise of decomposition is less conclusive about the performance of the method, which essentially depends on the sample size and the accuracy of the regression model. Full article

(This article belongs to the Special Issue Quantile Methods)

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231 KiB

Open AccessArticle

Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality

by Giuseppe Arbia, Giuseppe Espa and Diego Giuliani

Econometrics 2015, 3(4), 709-718; https://doi.org/10.3390/econometrics3040709 - 29 Oct 2015

Cited by 10 | Viewed by 5334

Abstract

In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in [...] Read more.

In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in a regression framework. However, in order to preserve confidentiality, spatial micro-data are often geo-masked, thus reducing their quality and dramatically distorting the inferential conclusions. In particular in this case, a measurement error is introduced in the independent variable which negatively affects the properties of the estimators. This paper studies these negative effects, discusses their consequences, and suggests possible interpretations and directions to data producers, end users, and practitioners. Full article

(This article belongs to the Special Issue Spatial Econometrics)

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293 KiB

Open AccessArticle

Is Benford’s Law a Universal Behavioral Theory?

by Sofia B. Villas-Boas, Qiuzi Fu and George Judge

Econometrics 2015, 3(4), 698-708; https://doi.org/10.3390/econometrics3040698 - 22 Oct 2015

Cited by 4 | Viewed by 8405

Abstract

In this paper, we consider the question and present evidence as to whether or not Benford’s exponential first significant digit (FSD) law reflects a fundamental principle behind the complex and nondeterministic nature of large-scale physical and behavioral systems. As a behavioral example, we [...] Read more.

In this paper, we consider the question and present evidence as to whether or not Benford’s exponential first significant digit (FSD) law reflects a fundamental principle behind the complex and nondeterministic nature of large-scale physical and behavioral systems. As a behavioral example, we focus on the FSD distribution of Australian micro income data and use information theoretic entropy methods to investigate the degree that corresponding empirical income distributions are consistent with Benford’s law. Full article

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Econometrics, Volume 3, Issue 4 (December 2015) – 8 articles , Pages 698-887

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