Econometrics

18 pages, 761 KB

Open AccessArticle

Statistical Inference on the Canadian Middle Class

by Russell Davidson

Econometrics 2018, 6(1), 14; https://doi.org/10.3390/econometrics6010014 - 13 Mar 2018

Cited by 4 | Viewed by 7801

Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for [...] Read more.

Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for inference on these shares, by means of deriving expressions for their asymptotic variances of sample estimates, and the covariance of the estimates. Asymptotic inference can be undertaken based on asymptotic normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an illustration, samples of individual earnings drawn from Canadian census data are used to test various hypotheses about the middle-class shares, and confidence intervals for them are computed. It is found that, for the earlier censuses, sample sizes are large enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap fails on account of a strange phenomenon whereby many presumably different incomes in the data are rounded to one and the same value. Another difference between the centuries is the appearance of heavy right-hand tails in the income distributions of both men and women. Full article

(This article belongs to the Special Issue Econometrics and Income Inequality)

► Show Figures

Figure 1

21 pages, 352 KB

Open AccessArticle

An Overview of Modified Semiparametric Memory Estimation Methods

by Marie Busch and Philipp Sibbertsen

Econometrics 2018, 6(1), 13; https://doi.org/10.3390/econometrics6010013 - 12 Mar 2018

Cited by 3 | Viewed by 7798

Abstract

Several modified estimation methods of the memory parameter have been introduced in the past years. They aim to decrease the upward bias of the memory parameter in cases of low frequency contaminations or an additive noise component, especially in situations with a short-memory [...] Read more.

Several modified estimation methods of the memory parameter have been introduced in the past years. They aim to decrease the upward bias of the memory parameter in cases of low frequency contaminations or an additive noise component, especially in situations with a short-memory process being contaminated. In this paper, we provide an overview and compare the performance of nine semiparametric estimation methods. Among them are two standard methods, four modified approaches to account for low frequency contaminations and three procedures developed for perturbed fractional processes. We conduct an extensive Monte Carlo study for a variety of parameter constellations and several DGPs. Furthermore, an empirical application of the log-absolute return series of the S&P 500 shows that the estimation results combined with a long-memory test indicate a spurious long-memory process. Full article

► Show Figures

Figure 1

17 pages, 1121 KB

Open AccessArticle

Response-Based Sampling for Binary Choice Models With Sample Selection

by Maria Felice Arezzo and Giuseppina Guagnano

Econometrics 2018, 6(1), 12; https://doi.org/10.3390/econometrics6010012 - 7 Mar 2018

Cited by 10 | Viewed by 8380

Abstract

Sample selection models attempt to correct for non-randomly selected data in a two-model hierarchy where, on the first level, a binary selection equation determines whether a particular observation will be available for the second level (outcome equation). If the non-random selection mechanism induced [...] Read more.

Sample selection models attempt to correct for non-randomly selected data in a two-model hierarchy where, on the first level, a binary selection equation determines whether a particular observation will be available for the second level (outcome equation). If the non-random selection mechanism induced by the selection equation is ignored, the coefficient estimates in the outcome equation may be severely biased. When the selection mechanism leads to many censored observations, few data are available for the estimation of the outcome equation parameters, giving rise to computational difficulties. In this context, the main reference is Greene (2008) who extends the results obtained by Manski and Lerman (1977), and develops an estimator which requires the knowledge of the true proportion of occurrences in the outcome equation. We develop a method that exploits the advantages of response-based sampling schemes in the context of binary response models with a sample selection, relaxing this assumption. Estimation is based on a weighted version of Heckman’s likelihood, where the weights take into account the sampling design. In a simulation study, we found that, for the outcome equation, the results obtained with our estimator are comparable to Greene’s in terms of mean square error. Moreover, in a real data application, it is preferable in terms of the percentage of correct predictions. Full article

► Show Figures

Figure 1

28 pages, 371 KB

Open AccessArticle

Jackknife Bias Reduction in the Presence of a Near-Unit Root

by Marcus J. Chambers and Maria Kyriacou

Econometrics 2018, 6(1), 11; https://doi.org/10.3390/econometrics6010011 - 5 Mar 2018

Cited by 2 | Viewed by 7929

Abstract

This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment [...] Read more.

This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment generating function (MGF) of two components of these distributions is obtained and its properties explored. The MGF can be used to derive the weights for an optimal jackknife estimator that removes fully the first-order finite sample bias from the estimator. The resulting jackknife estimator is shown to perform well in finite samples and, with a suitable choice of the number of sub-samples, is shown to reduce the overall finite sample root mean squared error, as well as bias. However, the optimal jackknife weights rely on knowledge of the near-unit root parameter and a quantity that is related to the long-run variance of the disturbance process, which are typically unknown in practice, and so, this dependence is characterised fully and a discussion provided of the issues that arise in practice in the most general settings. Full article

(This article belongs to the Special Issue Celebrated Econometricians: Peter Phillips)

16 pages, 1527 KB

Open AccessArticle

Top Incomes, Heavy Tails, and Rank-Size Regressions

by Christian Schluter

Econometrics 2018, 6(1), 10; https://doi.org/10.3390/econometrics6010010 - 2 Mar 2018

Cited by 7 | Viewed by 8162

Abstract

In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading [...] Read more.

In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading parametric income models (so the upper income tail is less heavy than estimated), which leads to test size distortions and undermines inference. For practical work, we propose a sensitivity analysis based on regression diagnostics in order to assess the likely impact of the distortion. The methods are illustrated using data on top incomes in the UK. Full article

(This article belongs to the Special Issue Econometrics and Income Inequality)

► Show Figures

Figure 1

15 pages, 287 KB

Open AccessArticle

A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade

by Rodolfo Metulini, Roberto Patuelli and Daniel A. Griffith

Econometrics 2018, 6(1), 9; https://doi.org/10.3390/econometrics6010009 - 22 Feb 2018

Cited by 28 | Viewed by 10979

Abstract

Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each [...] Read more.

Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each other due to spatial and network autocorrelation, these methods may lead to biased parameter estimates. To overcome this problem, eigenvector spatial filtering (ESF) variants of the Poisson/negative binomial specifications have been proposed in the literature on gravity modelling of trade. However, no specific treatment has been developed for cases in which many zero flows are present. This paper contributes to the literature in two ways. First, by employing a stepwise selection criterion for spatial filters that is based on robust (sandwich) p-values and does not require likelihood-based indicators. In this respect, we develop an ad hoc backward stepwise function in R. Second, using this function, we select a reduced set of spatial filters that properly accounts for importer-side and exporter-side specific spatial effects, as well as network effects, both at the count and the logit processes of zero-inflated methods. Applying this estimation strategy to a cross-section of bilateral trade flows between a set of 64 countries for the year 2000, we find that our specification outperforms the benchmark models in terms of model fitting, both considering the AIC and in predicting zero (and small) flows. Full article

(This article belongs to the Special Issue Recent developments in Spatial Econometrics, associated with the 10th Annual Conference of the Spatial Econometrics Association, Rome 13-15 June 2016)

24 pages, 341 KB

Open AccessArticle

Lasso Maximum Likelihood Estimation of Parametric Models with Singular Information Matrices

by Fei Jin and Lung-fei Lee

Econometrics 2018, 6(1), 8; https://doi.org/10.3390/econometrics6010008 - 22 Feb 2018

Cited by 4 | Viewed by 8701

Abstract

An information matrix of a parametric model being singular at a certain true value of a parameter vector is irregular. The maximum likelihood estimator in the irregular case usually has a rate of convergence slower than the

\sqrt{n}

-rate in a regular case. [...] Read more.

An information matrix of a parametric model being singular at a certain true value of a parameter vector is irregular. The maximum likelihood estimator in the irregular case usually has a rate of convergence slower than the

\sqrt{n}

-rate in a regular case. We propose to estimate such models by the adaptive lasso maximum likelihood and propose an information criterion to select the involved tuning parameter. We show that the penalized maximum likelihood estimator has the oracle properties. The method can implement model selection and estimation simultaneously and the estimator always has the usual

\sqrt{n}

-rate of convergence. Full article

27 pages, 581 KB

Open AccessArticle

A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns

by Ralf Becker, Adam Clements and Robert O'Neill

Econometrics 2018, 6(1), 7; https://doi.org/10.3390/econometrics6010007 - 17 Feb 2018

Cited by 1 | Viewed by 8752

Abstract

This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of the matrix. [...] Read more.

This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of the matrix. The model makes use of similarity forecasting techniques and it is demonstrated that several popular techniques can be thought as a subset of this approach. A forecasting experiment demonstrates the potential for the technique to improve the statistical accuracy of forecasts of variance-covariance matrices. Full article

(This article belongs to the Special Issue Volatility Modeling)

► Show Figures

Figure 1

20 pages, 360 KB

Open AccessArticle

Estimating Unobservable Inflation Expectations in the New Keynesian Phillips Curve

by Francesca Rondina

Econometrics 2018, 6(1), 6; https://doi.org/10.3390/econometrics6010006 - 5 Feb 2018

Cited by 4 | Viewed by 9234

Abstract

This paper uses an econometric model and Bayesian estimation to reverse engineer the path of inflation expectations implied by the New Keynesian Phillips Curve and the data. The estimated expectations roughly track the patterns of a number of common measures of expected inflation [...] Read more.

This paper uses an econometric model and Bayesian estimation to reverse engineer the path of inflation expectations implied by the New Keynesian Phillips Curve and the data. The estimated expectations roughly track the patterns of a number of common measures of expected inflation available from surveys or computed from financial data. In particular, they exhibit the strongest correlation with the inflation forecasts of the respondents in the University of Michigan Survey of Consumers. The estimated model also shows evidence of the anchoring of long run inflation expectations to a value that is in the range of the target inflation rate. Full article

(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)

► Show Figures

Figure 1

19 pages, 7486 KB

Open AccessArticle

Assessing News Contagion in Finance

by Paola Cerchiello and Giancarlo Nicola

Econometrics 2018, 6(1), 5; https://doi.org/10.3390/econometrics6010005 - 3 Feb 2018

Cited by 27 | Viewed by 13004

Abstract

The analysis of news in the financial context has gained a prominent interest in the last years. This is because of the possible predictive power of such content especially in terms of associated sentiment/mood. In this paper, we focus on a specific aspect [...] Read more.

The analysis of news in the financial context has gained a prominent interest in the last years. This is because of the possible predictive power of such content especially in terms of associated sentiment/mood. In this paper, we focus on a specific aspect of financial news analysis: how the covered topics modify according to space and time dimensions. To this purpose, we employ a modified version of topic model LDA, the so-called Structural Topic Model (STM), that takes into account covariates as well. Our aim is to study the possible evolution of topics extracted from two well known news archive—Reuters and Bloomberg—and to investigate a causal effect in the diffusion of the news by means of a Granger causality test. Our results show that both the temporal dynamics and the spatial differentiation matter in the news contagion. Full article

(This article belongs to the Special Issue Big Data in Economics and Finance)

► Show Figures

Figure 1

20 pages, 367 KB

Open AccessArticle

From the Classical Gini Index of Income Inequality to a New Zenga-Type Relative Measure of Risk: A Modeller’s Perspective

by Francesca Greselin and Ričardas Zitikis

Econometrics 2018, 6(1), 4; https://doi.org/10.3390/econometrics6010004 - 25 Jan 2018

Cited by 18 | Viewed by 29685

Abstract

The underlying idea behind the construction of indices of economic inequality is based on measuring deviations of various portions of low incomes from certain references or benchmarks, which could be point measures like the population mean or median, or curves like the hypotenuse [...] Read more.

The underlying idea behind the construction of indices of economic inequality is based on measuring deviations of various portions of low incomes from certain references or benchmarks, which could be point measures like the population mean or median, or curves like the hypotenuse of the right triangle into which every Lorenz curve falls. In this paper, we argue that, by appropriately choosing population-based references (called societal references) and distributions of personal positions (called gambles, which are random), we can meaningfully unify classical and contemporary indices of economic inequality, and various measures of risk. To illustrate the herein proposed approach, we put forward and explore a risk measure that takes into account the relativity of large risks with respect to small ones. Full article

(This article belongs to the Special Issue Econometrics and Income Inequality)

► Show Figures

Figure 1

15 pages, 1010 KB

Open AccessArticle

Spurious Seasonality Detection: A Non-Parametric Test Proposal

by Aurelio F. Bariviera, Angelo Plastino and George Judge

Econometrics 2018, 6(1), 3; https://doi.org/10.3390/econometrics6010003 - 19 Jan 2018

Cited by 3 | Viewed by 7857

Abstract

This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact [...] Read more.

This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies. Full article

► Show Figures

Figure 1

2 pages, 145 KB

Open AccessEditorial

Acknowledgement to Reviewers of Econometrics in 2017

by Econometrics Editorial Office

Econometrics 2018, 6(1), 2; https://doi.org/10.3390/econometrics6010002 - 10 Jan 2018

Viewed by 6214

Abstract

Peer review is an essential part in the publication process, ensuring that Econometrics maintains high quality standards for its published papers. In 2017, a total of 47 papers were published in the journal.[...] Full article

5 pages, 139 KB

Open AccessEditorial

Recent Developments in Cointegration

by Katarina Juselius

Econometrics 2018, 6(1), 1; https://doi.org/10.3390/econometrics6010001 - 31 Dec 2017

Cited by 5 | Viewed by 7779

Abstract

n/a Full article

(This article belongs to the Special Issue Recent Developments in Cointegration)

Journal Menu

Journal Browser

Econometrics, Volume 6, Issue 1 (March 2018) – 14 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI