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Econometrics for Grumblers: A New Look at the Literature on Cross-Country Growth Empirics

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Author Info
Eberhardt, Markus
Teal, Francis

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Abstract

Since the seminal contribution of Gregory Mankiw, David Romer and David Weil (1992), the growth empirics literature has used increasingly sophisticated methods to select relevant growth determinants in estimating cross-section growth regressions. The vast majority of empirical approaches however limit cross-country heterogeneity in production technology to the specification of Total Factor Productivity, the 'measure of our ignorance' (Abramowitz, 1956). The central theme of this survey is an investigation of this choice of specification against the background of pertinent data properties when the units of observations are countries or regions and the time-series dimension of the data becomes substantial. We present two general empirical frameworks for cross-country productivity analysis and demonstrate that they encompass the approaches in the growth empirics literature of the past two decades. We then develop our central argument, that cross-country heterogeneity in the impact of observables and unobservables on output is important for reliable empirical analysis. This idea is developed against the background of the pertinent time-series and cross-section properties of macro panel data.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 15813.

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Date of creation: 12 Jun 2009
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Handle: RePEc:pra:mprapa:15813

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Related research
Keywords: Cross-Country Empirical Analysis; Nonstationary Panel Econometrics; Parameter Heterogeneity; Common Factor Model; Cross-section Dependence;

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Find related papers by JEL classification:
O11 - Economic Development, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
O47 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Measurement of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data

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