Natasha Kang

PhD Job Market Candidate

My research interests are in time-series Econometrics, Macroeconomics, and International Economics. My job market paper provides a novel framework for modeling long cycles. Long cycles refer to persistent, low-frequency stochastic cyclical processes, which are common in macroeconomic and financial time series.

The paper develops the asymptotic theory necessary for valid inference on the cycle length regardless of the length and persistence of cycles. We show that standard inferential procedures can be misleading in presence of such cycles and provide an alternative methodology. We also apply our methodology to study the cyclical properties of key macroeconomic and financial time series. Our empirical findings show that financial cycles as characterized by credit and house prices tend to be twice as long as business cycles. Moreover, asset market variables such as equity prices, volatility index and credit risk premium do not exhibit any stochastic cycles.


Recurrent boom-and-bust cycles are a salient feature of economic and financial history. Cycles found in the data are stochastic, often highly persistent and spanning substantial fractions of the sample size. We refer to such cycles as “long”. In this paper, we develop a novel approach to modeling cyclical behavior specifically designed to capture long cycles. We show that existing inferential procedures may produce misleading results in presence of long cycles, and propose a new econometric procedure for inference on the cycle length. Our procedure is asymptotically valid regardless of the cycle length. We apply our methodology to a set of macroeconomic and financial variables for the U.S. We find evidence of long stochastic cycles in the standard business cycle variables, as well as in credit and house prices. However, we rule out the presence of stochastic cycles in asset market data. Moreover, according to our result, financial cycles as characterized by credit and house prices tend to be twice as long as business cycles. [GO TO PAPER]


We study the relationship between industrial structure of economies and their international portfolio composition. To this end we document a new fact: US industries that are more capital intensive exhibit lower degree of equity home bias. A multi-sector model is developed to rationalize this pattern. It embeds the standard demand for equity to hedge labor income risk and international relative price movement risk. Our contribution is to add two novel channels arising from: (i) differences in productivity across sectors, which tend to strengthen equity home bias; (ii) differences in capital intensity across sectors, which can have an ambiguous effect on equity home bias. In a calibrated version of the model, we show that the predicted aggregate equity home bias is in line with its value observed in the US data, and importantly, that home bias is smaller in more capital intensive industries, in line with the data.



Teaching Assistant

  • ECON 221 – Introduction to Strategic Thinking – Fall 2013 and Spring 2014
  • ECON 302 – Intermediate Macroeconomic Analysis I – Fall 2015
  • ECON 356 – Introduction to International Finance – Summer 2016, 2018 and 2019
  • ECON 421 – Introduction to Game Theory and Applications – Fall 2019
  • ECON 493 – Advanced Empirical Methods for International Economics – Fall 2016 and 2017
  • ECON 594 – Applied Economics – Summer 2018, 2019 and 2020
  • GPP 502 – International Macroeconomics – Spring 2016, 2017, 2018, 2019, 2020