Economist with a PhD in Economics and experience in applied economic research, policy analysis, and cross-country empirical work on labour markets, firm dynamics, productivity, job creation, education, gender, health, and development policy. Works on firms and job creation using large-scale firm-level administrative data, with a focus on employment dynamics, productivity, firm performance, and private sector development. Skilled in Stata, R, applied econometrics, causal inference, microdata analysis, data harmonisation, statistical validation, and translating technical evidence into policy-relevant outputs.
Teaching Associate, Deakin University, Labour and Health Economics (Undergraduate), 2023.
Presentations
2024: Africa Meeting of the Econometric Society.
2023: Australian Health Economics Society Conference; Africa Meeting of the Econometric Society; Western Economic Association International (WEAI) International Conference; Australian Gender Economics Workshop.
2022: The Australasian AID Conference; Australian Development Economics Workshop..
2012: 8th International Conference on Environment, Cultural, Economics and Social Sustainability.
Unobserved Components Model
This function implements the Stock and Watson (2007) unobserved components model with stochastic volatilities. It employs a gamma random variable generator function and can be used to extract the trend of any given univariate time series.
This function takes in the output from the VAR() function (see vars package) and computes the vector autoregression historical decomposition. It is an abridged translation of Cesa-Bianchi's MATLAB code (in the VAR-Toolbox) designed for R.
This function determines the uncertainty (or error) of any mathematical expression containing estimated variables with uncertainties by taking the partial derivatives with respect to each variable multiplied by their uncertainty and added in quadrature.
This code is used to estimate the natural rate of interest using an annually adapted Holston, Laubach and Williams (2017) multi-stage maximum likelihood estimation procedure, where the likelihood function is computed by the Kalman filter.
This is bold and this is strong. This is italic and this is emphasized.
This is superscript text and this is subscript text.
This is underlined and this is code: for (;;) { ... }. Finally, this is a link.
Heading Level 2
Heading Level 3
Heading Level 4
Heading Level 5
Heading Level 6
Blockquote
Fringilla nisl. Donec accumsan interdum nisi, quis tincidunt felis sagittis eget tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan faucibus. Vestibulum ante ipsum primis in faucibus lorem ipsum dolor sit amet nullam adipiscing eu felis.
Preformatted
i = 0;
while (!deck.isInOrder()) {
print 'Iteration ' + i;
deck.shuffle();
i++;
}
print 'It took ' + i + ' iterations to sort the deck.';
