A <b>Panel Data Toolbox</b> for <i>MATLAB</i>

Álvarez, Inmaculada; Barbero, Javier; Zofío, José L.

doi:10.18637/jss.v076.i06

Cited by 34 publications

(13 citation statements)

References 47 publications

(51 reference statements)

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“…Due to the potential reverse causality we run fixed effects two-stage least squares for our balanced panel data (see [ 34 ] for details of the two-stage estimation, using the ivpanel function from the Panel Data Toolbox in Matlab ). With the inclusion of an instrument in the regression we account for potential endogeneity of the explanatory variables when estimating the model.…”

Section: Data and Econometric Methodologymentioning

confidence: 99%

Economic convergence in a globalized world: The role of business cycle synchronization

2021

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Increasing economic integration and global synchronization can be key for countries aiming to catch up in GDP per capita terms. Little attention has hitherto been placed in synchronization as determinant of convergence. In this paper we estimate the effect of economic globalization and synchronization on income convergence for a sample of 89 developed and developing countries in the period 1970–2015. We use a dynamic factor model and panel data techniques to undertake the objectives of the paper. We show that synchronized countries (those correlated with the factor) exhibit a higher response on GDP per capita growth with variations on the global business cycle. This implies that synchronization improves growth for that group in global expansionary phases, but also implies risks during global recessions. On the contrary, the effect on growth of an economic globalization index is less relevant for synchronized countries than for asynchronized countries. The latter result implies that asynchronized countries can benefit more increasing their levels of economic globalization.

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Section: Data and Econometric Methodologymentioning

confidence: 99%

Economic convergence in a globalized world: The role of business cycle synchronization

2021

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“…Compared to cross‐sectional regression, its major advantage is the ability to control for unobserved time‐invariant heterogeneity, which substantially reduces the risk of omitted variable bias. We consider fixed effects (FE), random effects, and between effects estimators (Álvarez, Barbero, & Zofío, ). The FE estimator is reported since random effects estimators are preferably used for inferring observed sample effects (Searle, Casella, & McCulloch, ) whereas FE estimators are rather applied to provide projections based on observed sample effects.…”

Section: Methodsmentioning

confidence: 99%

Bioeconomic transition?: Projecting consumption‐based biomass and fossil material flows to 2050

Asada

Krisztin

Fulvio

et al. 2020

J of Industrial Ecology

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Countries are responding to unsustainable resource extraction, rising emissions, and increasing waste streams by implementing national bioeconomy strategies. Assuming that the purpose of a bioeconomy is to replace fossil use by biogenic resource use, we estimate biomass and fossil raw material consumption (RMC) by applying multiregional input-output methodology for middle and high income countries. Next, we use a panel fixed effects model to explain RMC with economically active population, urban population, GDP, land cover, and fossil/biomass domestic material consumption. With this model, we project RMC under five Shared Socioeconomic Pathway scenarios up to 2050. The projections show an increase in per capita biomass RMC between 2010 and 2050, accompanied by-in many cases pronounced-per capita growth of fossil RMC across most of the countries and scenarios. We conclude that, if GDP continues to drive fossil RMC at its current magnitude, upcoming conditions are likely to counteract a potential bioeconomic transition and increase, instead of decrease, fossil RMC. Thus, increasing biomass use will not necessarily lead to reduced fossil resource consumption. When considering the relative scarcity of biomass, land and water, more focus needs to be placed on the relevance of technological bio-based innovations in the reconfiguration of RMC drivers.

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“…Then, to proceed with the estimation, we adapt our data in Stata to be spatial and create our weight matrix W . Once the data is properly set up, we switch to Matlab and use the Toolbox developed by Alvarez et al (2017). When the model contains a spatial lag of the error structure, this toolbox implements the GMM estimations proposed by Kapoor et al (2007), Mutl and Pfaffermayr (2011) or Piras (2013).…”

Section: Implementation Of the Modelmentioning

confidence: 99%

The Impact of the Sharing Economy on Housing Rental Prices: The Case of Airbnb in Barcelona

Agustí¹,

Ásmundsson²,

Bischofberger³

et al. 2020

SSRN Journal

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We offer a novel approach to investigate the extent to which the expansion of the sharing economy is responsible for increases in long-term rents and prices on the housing market. Using a panel of quarterly data on newly signed rental contracts and transaction prices in Barcelona from 2015-Q2 to 2018-Q4, we implement a fixed-effects spatial 2SLS method allowing for endogeneity in the variable which measures the presence of Airbnb. We find that a 100-unit increase in the number of Airbnb listings in a given neighborhood induces a 2.1% increase in rents. We find no indication that Airbnb affects transaction prices.

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A Panel Data Toolbox for MATLAB

Cited by 34 publications

References 47 publications

Economic convergence in a globalized world: The role of business cycle synchronization

Economic convergence in a globalized world: The role of business cycle synchronization

Bioeconomic transition?: Projecting consumption‐based biomass and fossil material flows to 2050

The Impact of the Sharing Economy on Housing Rental Prices: The Case of Airbnb in Barcelona

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