Exponent of Cross‐Sectional Dependence: Estimation and Inference

Bailey, Norman T. J.; Kapetanios, George; Pesaran, M. Hashem

doi:10.1002/jae.2476

Cited by 207 publications

(256 citation statements)

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“…The degree of dominance (or pervasiveness) of a sector is measured by the exponent that controls the rate at which the outdegree of the sector in question rises with the total number of sectors. This measure turns out to be the same as the exponent of cross-sectional dependence introduced in Bailey et al (2016), for the analysis of crosssection dependence in panel data models with large cross-section and time dimensions. We also show that the largest , denoted by (1) , is the inverse of , the shape parameter of the Pareto distribution, the form assumed by Acemoglu et al (2012) and others in the literature.…”

Section: Introductionmentioning

confidence: 94%

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Econometric Analysis of Production Networks with Dominant Units

Pesaran

Yang

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. (2012) and considers a production network with unobserved common technological factor and establishes general conditions under which the network structure contributes to aggregate fluctuations. It introduces the notions of strongly and weakly dominant units, and shows that at most a finite number of units in the network can be strongly dominant, while the number of weakly dominant units can rise with N (the cross section dimension). This paper further establishes the equivalence between the highest degree of dominance in a network and the inverse of the shape parameter of the power law. A new extremum estimator for the degree of pervasiveness of individual units in the network is proposed, and is shown to be robust to the choice of the underlying distribution. Using Monte Carlo techniques, the proposed estimator is shown to have satisfactory small sample properties. Empirical applications to US input-output tables suggest the presence of production sectors with a high degree of pervasiveness, but their effects are not sufficiently pervasive to be considered as strongly dominant. Terms of use: Documents inJEL-Codes: C120, C130, C230, C670, E320.

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Section: Introductionmentioning

confidence: 94%

“…The factor loading, i , measures the importance of technological change on sector i. Following Bailey et al (2016), we denote the cross-section exponent of the factor loadings by , de…ned by the value of that ensures…”

Section: Production and Price Networkmentioning

confidence: 99%

Econometric Analysis of Production Networks with Dominant Units

Pesaran

Yang

2016

SSRN Journal

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“…To test for weak and strong cross-section dependence, we estimate the cross-sectional dependence (CD) test statistic of Pesaran (2015) and the exponent of cross sectional dependence (α) proposed in Bailey et al (2016). The CD statistic is normally distributed with zero-mean and unit-variance under the null of zero average pair-wise correlations.…”

Section: Cross-country Correlations Of Volatility and Growth Innovationsmentioning

confidence: 99%

“…So, the critical value is around 2. When the null is rejected, Bailey et al (2016) Pesaran (2015).α is the estimate of the exponent of cross-sectional dependence as in Bailey et al (2016), together with its 90-percent confidence interval ('Lower 5%' and 'Upper95%').…”

Section: Cross-country Correlations Of Volatility and Growth Innovationsmentioning

confidence: 99%

Uncertainty and Economic Activity: A Multi-Country Perspective

2018

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Measures of economic uncertainty are countercyclical, but economic theory does not provide definite guidance on the direction of causation between uncertainty and the business cycle. This paper proposes a new multi-country approach to the analysis of the interaction between uncertainty and economic activity, without a priori restricting the direction of causality. We develop a multi-country version of the Lucas tree model with time-varying volatility and show that in addition to common technology shocks that affect output growth, higher-order moments of technology shocks are also required to explain the cross country variations of realized volatility. Using this theoretical insight, two common factors, a 'real' and a 'financial' one, are identified in the empirical analysis assuming different patterns of cross-country correlations of country-specific innovations to real GDP growth and realized stock market volatility. We then quantify the absolute and the relative importance of the common factor shocks as well as country-specific volatility and GDP growth shocks. The paper highlights three main empirical findings. First, it is shown that most of the unconditional correlation between volatility and growth can be accounted for by the real common factor, which is proportional to world growth in our empirical model and linked to the risk-free rate. Second, the share of volatility forecast error variance explained by the real common factor and by country-specific growth shocks amounts to less than 5 percent. Third, shocks to the common financial factor explain about 10 percent of the growth forecast error variance, but when such shocks occur, their negative impact on growth is large and persistent. In contrast, country-specific volatility shocks account for less than 1-2 percent of the growth forecast error variance. Terms of use: Documents inJEL-Codes: E440, F440, G150.

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“…6 From an econometric perspective, the spatial econometric tools can be grouped into two categories With the increased availability of data observations for both time and cross-sections, however, the focus of the spatial econometric studies has shifted to the case with N and T both large (N; T ! 1 jointly).…”

Section: Gvar Model Of Regional Well-beingmentioning

confidence: 99%

Geographic Inequality of Economic Well-being among U.S. Cities: Evidence from Micro Panel Data

Choi

Chudík

2017

gwp

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We analyze the geographic inequality of economic well-being among U.S. cities by utilizing a novel measure of quantity based product-level economic well-being, i.e., the number of goods and services that can be purchased by consumers with an average city wage. We find a considerable cross-city dispersion in the economic well-being and the geographic dispersion has been on the steady rise since the mid-1990s for most goods and services under study. Strong geographic correlations exist in the local economic well-being and our empirical analysis based on a Global VAR (GVAR) model suggests that national shocks are an important source behind it. On average, about 30-35% of the variance of local well-being is explained by common national shocks, but the impact of common national shocks varies considerably across products, albeit to a lesser extent across cities. Nationwide unemployment shock, for example, has a stronger effect in the products whose prices are adjusted more frequently and in the cities that have a larger fraction of high-skill workers. Taken together, our results indicate that the geographic inequality of economic well-being observed in the U.S. has proceeded over time mainly through the products with more flexible price adjustments and in the cities with higher concentration of skilled workers.

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Exponent of Cross‐Sectional Dependence: Estimation and Inference

Cited by 207 publications

References 35 publications

Econometric Analysis of Production Networks with Dominant Units

Econometric Analysis of Production Networks with Dominant Units

Uncertainty and Economic Activity: A Multi-Country Perspective

Geographic Inequality of Economic Well-being among U.S. Cities: Evidence from Micro Panel Data

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