Some econometric issues in measuring the monetary transmission mechanism, with an application to developing countries

Boyd, Derick; Smith, Ron

doi:10.1017/cbo9780511492488.005

Cited by 18 publications

(12 citation statements)

References 173 publications

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“…With the specific data at our disposal (unbalanced panel, average T ¼ 23), a closer analysis of whether we can identify discernible patterns must be interpreted with caution, and we view our results below as merely indicative. Previous empirical analysis averaging individual country regressions has frequently observed that although country estimates are widely dispersed and, at times, economically implausible, averages represent very plausible estimates (Boyd and Smith 2002;Baltagi et al 2003). Pedroni (2007: 440) calls for caution when interpreting the estimates for any individual country because the "long-run signals contained in [limited] years of data may be relatively weak," whereas the cross-section averages will amplify the signal patterns sufficiently.…”

Section: Technology Heterogeneitymentioning

confidence: 99%

Structural Change and Cross-Country Growth Empirics

Eberhardt¹,

Teal²

2012

The World Bank Economic Review

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One of the most striking features of economic growth is the process of structural change whereby the share of agriculture in GDP decreases as countries develop. The crosscountry growth literature typically estimates an aggregate homogeneous production function or convergence regression model that abstracts from the process of structural change. In this paper, we investigate the extent to which assumptions about aggregation and homogeneity matter for inferences regarding the nature of technology differences across countries. Using a unique World Bank dataset, we estimate production functions for agriculture and manufacturing in a panel of 40 developing and developed countries for the period from 1963 to 1992. We empirically model dimensions of heterogeneity across countries, allowing for different choices of technology within both sectors. We argue that heterogeneity is important within sectors across countries implying that an analysis of aggregate data will not produce useful measures of the nature of the technology or productivity. We show that many of the puzzling elements in aggregate crosscountry empirics can be explained by inappropriate aggregation across heterogeneous sectors. JEL codes: O47, O11, C23 The early literature on developing countries distinguished between the processes of economic development and economic growth. Economic development was considered a process of structural transformation by which, in Arthur

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Section: Technology Heterogeneitymentioning

confidence: 99%

Structural Change and Cross-Country Growth Empirics

Eberhardt¹,

Teal²

2012

The World Bank Economic Review

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“…Baltagi and Gri¢ n (1997), among many others, note this and the fact that pooled or averaged estimators (such as the Swamy RCM or the mean group estimator of Pesaran and Smith (1995)) tend to be much more reasonable. Similarly, using a panel of N = 57 countries with T = 31 annual observations Boyd and Smith (2002) estimate purchasing power parity equations where one would expect the the elasticity of the exchange rate to price di¤erentials to be close to unity. They …nd a range from -0.40 to 2.47 in static levels regression and from -2.21 to 7.93 for long-run estimates in a …rst order dynamic model.…”

Section: Introductionmentioning

confidence: 99%

Random coefficients models of arms imports

Smith

Taşıran

2010

Economic Modelling

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Prepared for Special Issue of Economic Modelling in honor of P.A.V.B. Swamy. This paper uses a large panel of data with up to 19 time-series observations for almost 150 countries to estimate models of arms imports. Qualitative evidence suggest a non-linear relationship. As income and military expenditure grow, the propensity to import …rst rises and then falls as a domestic arms industry develops. We face the di¢ -culty that there is virtually no data on domestic arms procurement or production capability. We try to avoid this di¢ culty by adopting a random coe¢ cient approach in order to identify any systematic in ‡uences on import propensity, through the impact of military expenditure, size of the armed forces or income on unobserved domestic production capability. While a clear non-linear pattern is apparent in the cross-section relationship, once one allows for parameter heterogeneity such a pattern is not apparent in the time-series.J.E.L. Classi…cation: C23, C24, D74

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“…Second , we can compute the implied labour coefficient in each country and create a robust mean (0.24), which yields the 95% confidence interval [0.13, 0.36]. Note that country‐specific parameter coefficients should not be viewed in isolation (Pedroni, 2007, p. 440), frequently yielding economically implausible magnitudes (Boyd and Smith, 2002), as is illustrated by the extreme values of the distribution in the present case, –2.30 and 3.05. In Eberhardt and Teal (forthcoming), we provide a formal explanation for the occurrence of these implausible country estimates alongside the much more sensible average values.…”

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confidence: 99%

No Mangoes in the Tundra: Spatial Heterogeneity in Agricultural Productivity Analysis*

Eberhardt¹,

Teal²

2012

Oxf Bull Econ Stat

154

162

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In line with the wider macro productivity literature existing studies of agricultural production largely neglect technology heterogeneity, variable time‐series properties and the potential for heterogeneous but correlated total factor productivity (TFP) across countries. Our empirical approach accommodates these difficulties and seeks to model the nature of the cross‐section dependence in a sample of 128 countries (1961–2002). Our results suggest that agro‐climatic environment drives similarity in TFP evolution across countries with heterogeneous production technology. This provides a possible explanation for the failure of technology transfer from advanced countries of the temperate ‘North’ to arid and/or equatorial developing countries of the ‘South’.

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Some econometric issues in measuring the monetary transmission mechanism, with an application to developing countries

Cited by 18 publications

References 173 publications

Structural Change and Cross-Country Growth Empirics

Structural Change and Cross-Country Growth Empirics

Random coefficients models of arms imports

No Mangoes in the Tundra: Spatial Heterogeneity in Agricultural Productivity Analysis*

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