Jaqueson Kingeski Galimberti


Last Name

Kingeski Galimberti

First Name

Jaqueson

ORCID

0000-0003-1736-1833

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2018 - 201972020 - 20256
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Publications 1 - 10 of 13
  • Measuring Inequality using Geospatial Data
    Item type: Conference Paper
    Kingeski Galimberti, Jaqueson; Pichler, Stefan; Pleninger, Regina (2021)
    The main challenge in studying economic inequality is limited data availability, which is particularly problematic in developing countries. We construct a measure of economic inequality for 234 countries/territories from 1992 to 2013 using satellite data on night lights and gridded population data. Key methodological innovations include the use of varying levels of data aggregation, and a calibration of the lights-prosperity relationship to match traditional inequality measures based on income data. We obtain a measure that is significantly correlated with cross country variation in income inequality. We provide three applications of the data in the fields of health economics and international finance. Our results show that light- and income-based inequality measures lead to similar results in terms of cross-country correlations, but not for the dynamics of inequality within countries. Namely, we find that the light-based inequality measure can capture more enduring features of economic activity that are not directly captured by income.
  • An approximation of the distribution of learning estimates in macroeconomic models
    Item type: Journal Article
    Kingeski Galimberti, Jaqueson (2019)
    Journal of Economic Dynamics & Control
  • An approximation of the distribution of learning estimates in macroeconomic models
    Item type: Other Conference Item
    Kingeski Galimberti, Jaqueson (2018)
  • Forecasting GDP growth from outer space
    Item type: Other Conference Item
    Kingeski Galimberti, Jaqueson (2018)
  • An approximation of the distribution of learning estimates in macroeconomic models
    Item type: Working Paper
    Kingeski Galimberti, Jaqueson (2019)
    KOF Working Papers
    Adaptive learning under constant-gain allows persistent deviations of beliefs from equilibrium so as to more realistically reflect agents’ attempt of tracking the continuous evolution of the economy. A characterization of these beliefs is therefore paramount to a proper understanding of the role of expectations in the determination of macroeconomic outcomes. In this paper we propose a simple approximation of the first two moments (mean and variance) of the asymptotic distribution of learning estimates for a general class of dynamic macroeconomic models under constant-gain learning. Our approximation provides renewed convergence conditions that depend on the learning gain and the model’s structural parameters. We validate the accuracy of our approximation with numerical simulations of a Cobweb model, a standard New-Keynesian model, and a model including a lagged endogenous variable. The relevance of our results is further evidenced by an analysis of learning stability and the effects of alternative specifications of interest rate policy rules on the distribution of agents’ beliefs.
  • Measuring Inequality using Geospatial Data
    Item type: Working Paper
    Kingeski Galimberti, Jaqueson; Pichler, Stefan; Pleninger, Regina (2020)
    Economics Working Paper Series
    The main challenge in studying economic inequality is limited data availability, which is particularly problematic in developing countries. We construct a measure of economic inequality for 234 countries and territories from 1992 to 2013 using satellite data on nighttime light emissions as well as gridded population data. Key methodological innovations include the use of varying levels of data aggregation, and a parsimonious calibration of the lights-prosperity relationship to match traditional inequality measures based on income data. Indeed, we obtain a measure that is significantly correlated with cross-country variation in income inequality. Subsequently, we provide three applications of the data in the fields of health economics and international finance. Our results show that light- and income-based inequality measures lead to similar results in terms of crosscountry correlations, but not for the dynamics of inequality within countries. Namely, we find that the light-based inequality measure can capture more enduring features of economic activity that are not directly captured by income.
  • Forecasting GDP growth from outer space
    Item type: Other Conference Item
    Kingeski Galimberti, Jaqueson (2018)
  • Measuring Inequality using Geo-Spatial Data
    Item type: Presentation
    Pichler, Stefan; Kingeski Galimberti, Jaqueson; Pleninger, Regina (2019)
  • Measuring Inequality using Geospatial Data
    Item type: Working Paper
    Kingeski Galimberti, Jaqueson; Pichler, Stefan; Pleninger, Regina (2021)
    KOF Working Papers
    The main challenge in studying economic inequality is limited data availability, which is particularly problematic in developing countries. We construct a measure of economic inequality for 234 countries/territories from 1992 to 2013 using satellite data on night lights and gridded population data. Key methodological innovations include the use of varying levels of data aggregation, and a calibration of the lights-prosperity relationship to match traditional inequality measures based on income data. We obtain a measure that is significantly correlated with cross-country variation in income inequality. We provide three applications of the data in the fields of health economics and international finance. Our results show that light- and income-based inequality measures lead to similar results in terms of cross-country correlations, but not for the dynamics of inequality within countries. Namely, we find that the light-based inequality measure can capture more enduring features of economic activity that are not directly captured by income.
  • Initial Beliefs Uncertainty
    Item type: Journal Article
    Kingeski Galimberti, Jaqueson (2024)
    The B.E. Journal of Macroeconomics
    This paper evaluates how initial beliefs uncertainty can affect data weighting and the estimation of models with adaptive learning. One key finding is that misspecification of initial beliefs uncertainty, particularly with the common approach of artificially inflating initials uncertainty to accelerate convergence of estimates, generates time-varying profiles of weights given to past observations in what should otherwise follow a fixed profile of decaying weights. The effect of this misspecification, denoted as diffuse initials, is shown to distort the estimation and interpretation of learning in finite samples. Simulations of a forward-looking Phillips curve model indicate that (i) diffuse initials lead to downward biased estimates of expectations relevance in the determination of actual inflation, and (ii) these biases spill over to estimates of inflation responsiveness to output gaps. An empirical application with U.S. data shows the relevance of these effects for the determination of expectational stability over decadal subsamples of data. The use of diffuse initials is also found to lead to downward biased estimates of learning gains, both estimated from an aggregate representative model and estimated to match individual expectations from survey expectations data.
Publications 1 - 10 of 13