Timothy Kam

G’day! I’m yet another bloke interested in macroeconomics, monetary economics and computational economics.

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I specialize in the modelling of business cycles in monetary economies. I have previous experience working on quantitative models where more reduced-form frictions (e.g., sticky-prices and payment-instrument assumptions) help account for inflation and exchange rate dynamics, and help rationalize a role for policy intervention. My more recent work has called into question the need for such black-box modelling devices to explain price and exchange rate dynamics. Instead I have shown that an alternative mechanism where deeper market frictions stemming from informational and contractual problems can also rationalize the same monetary and international business cycle empirical regularities (KLOPENSEARCH). A further theoretical relaxation on ad-hoc payment-instrument assumptions in open-economy monetary economics, GKWCURSE, has resulted in an equilibrium theory that breaks the famous curse of Kareken and Wallace (1980, QJE). Further quantitative explorations from this theory is currently underway.

I am currently interested in the redistributive role of monetary and fiscal policies in heterogeneous-agent economies where the liquidity properties of money and other financial assets are non-trivially obtained as equilibrium phenomena. I develop a competitive search environment with endogenous market segmentation to address these questions. As a by-product of this agenda, I have developed a fast solution algorithm borrowing insights from computational geometry to solve a class of heterogeneous-agent (search-theoretic) monetary economies (CSIM).

Related to my interest in markets with search and matching frictions, I am also working on an urban economics model that accounts for empirical observations of wage and housing-cost inequality across major cities, in conjunction with a stark pattern of dispersion in city-level political conservatism across these cities. This theory will be identified from and estimated to geo-politico-economic data. From that empirical basis, further statistical inference and counterfactual policy experiments can be conducted.

I have previously invented an approximate bilinear-programming approach to solving history-dependent policy games with heterogeneous agents (HARDPIG). I have also contributed to statistical inference of central bank policy preferences under the hypothesis that central banks conduct credible monetary policies in dynamic rational expectations economies (SOECB-Markov).

My modelling philosophy is as follows: Empirical inference should be founded upon transparent theory; Theory should have empirical content.