The termstructure of interest rates is an instrument that gives us the necessary information for valueing deterministic financial cash flows, measuring the economic market expectations and testing the effectiveness of monetary policy decissions. However, it is not directly observable and needs...

The term structure of interest rates is an instrument that gives us the necessary information for valuing deterministic financial cash flows, measuring the economic market expectations and testing the effectiveness of monetary policy decisions. However, it is not directly observable and needs to...

We develop a multiscale simulation method for dense granular drainage, based on the recently proposed spot model, where the particle packing flows by local collective displacements in response to diffusing “spots” of interstitial free volume. By comparing with discrete-element method (DEM)...

I propose partial-equilibrium models that describe the dynamics of global wheat and corn markets. These models extend the classic competitive storage framework by incorporating nonstationary variables. They are calibrated using data from Ukraine and key importing and exporting countries. The...

The computational time required to solve and estimate dynamic economic models is one of the main constraints in empirical research. The Endogenous Grid Method (EGM) proposed by Carroll (2006) is known to offer impressive speed gains over more traditional stochastic dynamic programming methods,...

In many auctions, the auctioneer is an agent of the seller. This invites corruption. We propose a model of corruption in which the auctioneer orchestrates bid rigging by inviting a bidder to either lower or raise his bid, whichever is more profitable. We characterize equilibrium bidding in...

Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical...

This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and...

Approximating stochastic processes by finite-state Markov chains is useful for reducing computational complexity when solving dynamic economic models. We provide a new method for accurately discretizing general Markov processes by matching low order moments of the conditional distributions using...

This note examines the accuracy of methods that are commonly used to approximate AR(1)-processes with discrete Markov chains. The quadrature-based method suggested by Tauchen and Hussey (1991) generates excellent approximations with a small number of nodes when the autocorrelation is low or...