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A Homogeneous Approach to Testing for Granger Non-Causality in Heterogeneous Panels

Juodis, Arturas; Karavias, Yiannis; Sarafidis, Vasilis - 2020

This paper develops a new method for testing for Granger non-causality in panel data models with large cross …. In order to account for the well-known "Nickell bias", the approach makes use of the well-known Split Panel Jackknife … that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data …

Persistent link: https://www.econbiz.de/10015218430

A Panel Cointegration study of the long-run relationship between Savings and Investments in the OECD economies, 1970-2007

Di Iorio, Francesca; Fachin, Stefano - 2010

bootstrap test for panel cointegration robust to short- and long-run dependence across units. Thid test provides evidence of a …

Persistent link: https://www.econbiz.de/10015223673

A Statistical Test of City Growth: Location, Increasing Returns and Random Growth

González-Val, Rafael; Olmo, Jose - 2010

This article analyzes the main existing theories on income and population city growth: the existence of increasing returns to scale, the importance of locational fundamentals, and random growth. To do this we develop a nonlinearity test that is implemented to a dataset on urban, climatological...

Persistent link: https://www.econbiz.de/10015224524

Why inferential statistics are inappropriate for development studies and how the same data can be better used

Ballinger, Clint - 2011

The purpose of this paper is twofold: 1) to highlight the widely ignored but fundamental problem of ‘superpopulations’ for the use of inferential statistics in development studies. We do not to dwell on this problem however as it has been sufficiently discussed in older papers by...

Persistent link: https://www.econbiz.de/10015226259

Detecting big structural breaks in large factor models

Chen, Liang; Dolado, Juan Jose; Gonzalo, Jesus - 2011

Constant factor loadings is a standard assumption in the analysis of large dimensional factor models. Yet, this assumption may be restrictive unless parameter shifts are mild. In this paper we develop a new testing procedure to detect big breaks in factor loadings at either known or unknown...

Persistent link: https://www.econbiz.de/10015227234

Growth in a cross-section of cities: location, increasing returns or random growth?

González-Val, Rafael; Olmo, Jose - 2011

This article analyzes empirically the main existing theories on income and population city growth: increasing returns to scale, locational fundamentals and random growth. To do this we implement a threshold nonlinearity test that extends standard linear growth regression models to a dataset on...

Persistent link: https://www.econbiz.de/10015227902

Testing Panel Cointegration with Unobservable Dynamic Common Factors

Bai, Jushan; Carrion-i-Silvestre, Josep Lluis - 2009

The paper proposes statistics to test the null hypothesis of no cointegration in panel data when common factors drive …

Persistent link: https://www.econbiz.de/10015229647

Analyzing repeated-game economics experiments: robust standard errors for panel data with serial correlation

Vossler, Christian A. - 2009

particular, I propose the use of heteroskedasticity-autocorrelation consistent (HAC) covariance estimators for panel data, which … correlation likely present in econometric models. Through Monte Carlo experiments I explore the properties of three panel HAC …

Persistent link: https://www.econbiz.de/10015232100

Cherry Picking with Synthetic Controls

Ferman, Bruno; Pinto, Cristine; Possebom, Vitor - 2017

We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification-searching opportunities. We first provide theoretical results showing that specification-searching opportunities would be asymptotically irrelevant...

Persistent link: https://www.econbiz.de/10015257259

Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity

Ferman, Bruno; Pinto, Cristine - 2015

We show that existing inference methods used in Differences-in-Differences might not perform well with few treated groups and heteroskedastic errors. This is restrictive because variation in the number of observations per group inherently leads to heteroskedasticity in the group x time aggregate...

Persistent link: https://www.econbiz.de/10015257819