Econometrics

403 KiB

Open AccessArticle

The Biggest Myth in Spatial Econometrics

by James P. LeSage and R. Kelley Pace

Econometrics 2014, 2(4), 217-249; https://doi.org/10.3390/econometrics2040217 - 23 Dec 2014

Cited by 279 | Viewed by 14636

There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based [...] Read more.

There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based on the true partial derivatives for a well-specified spatial regression model. We conclude that this myth may have arisen from past applied work that incorrectly interpreted the model coefficients as if they were partial derivatives, or from use of misspecified models. Full article

(This article belongs to the Special Issue Spatial Econometrics)

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243 KiB

Open AccessArticle

Testing for A Set of Linear Restrictions in VARMA Models Using Autoregressive Metric: An Application to Granger Causality Test

by Francesca Di Iorio and Umberto Triacca

Econometrics 2014, 2(4), 203-216; https://doi.org/10.3390/econometrics2040203 - 22 Dec 2014

Viewed by 3860

Abstract

In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M₀ and M₁ [...] Read more.

In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M₀ and M₁, introduced by Piccolo in 1990. In particular, we show that this set of linear restrictions is equivalent to a null distance d(M0,M1 ) between two given ARMA models. This result provides the logical basis for using d(M₀,M₁) = 0 as a null hypothesis in our test. Some Monte Carlo evidence about the finite sample behavior of our testing procedure is provided and two empirical examples are presented. Full article

747 KiB

Open AccessArticle

Success at the Summer Olympics: How Much Do Economic Factors Explain?

by Pravin K. Trivedi and David M. Zimmer

Econometrics 2014, 2(4), 169-202; https://doi.org/10.3390/econometrics2040169 - 05 Dec 2014

Cited by 20 | Viewed by 7475

Abstract

Many econometric analyses have attempted to model medal winnings as dependent on per capita GDP and population size. This approach ignores the size and composition of the team of athletes, especially the role of female participation and the role of sports culture, and [...] Read more.

Many econometric analyses have attempted to model medal winnings as dependent on per capita GDP and population size. This approach ignores the size and composition of the team of athletes, especially the role of female participation and the role of sports culture, and also provides an inadequate explanation of the variability between the outcomes of countries with similar features. This paper proposes a model that offers two substantive advancements, both of which shed light on previously hidden aspects of Olympic success. First, we propose a selection model that treats the process of fielding any winner and the subsequent level of total winnings as two separate, but related, processes. Second, our model takes a more structural angle, in that we view GDP and population size as inputs into the “production” of athletes. After that production process, those athletes then compete to win medals. We use country-level panel data for the seven Summer Olympiads from 1988 to 2012. The size and composition of the country’s Olympic team are shown to be highly significant factors, as is also the past performance, which generates a persistence effect. Full article

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821 KiB

Open AccessArticle

A GMM-Based Test for Normal Disturbances of the Heckman Sample Selection Model

by Michael Pfaffermayr

Econometrics 2014, 2(4), 151-168; https://doi.org/10.3390/econometrics2040151 - 23 Oct 2014

Cited by 1 | Viewed by 4474

Abstract

The Heckman sample selection model relies on the assumption of normal and homoskedastic disturbances. However, before considering more general, alternative semiparametric models that do not need the normality assumption, it seems useful to test this assumption. Following Meijer and Wansbeek (2007), the present [...] Read more.

The Heckman sample selection model relies on the assumption of normal and homoskedastic disturbances. However, before considering more general, alternative semiparametric models that do not need the normality assumption, it seems useful to test this assumption. Following Meijer and Wansbeek (2007), the present contribution derives a GMM-based pseudo-score LM test on whether the third and fourth moments of the disturbances of the outcome equation of the Heckman model conform to those implied by the truncated normal distribution. The test is easy to calculate and in Monte Carlo simulations it shows good performance for sample sizes of 1000 or larger. Full article

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Econometrics, Volume 2, Issue 4 (December 2014) – 4 articles , Pages 151-249

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