Production technologies in stochastic continuous time models

Properties of dynamic stochastic general equilibrium models can be revealed by either using numerical solutions or qualitative analysis. Very precise and intuition-building results are obtained by working with models which provide closed-form solutions. Closed-form solutions are known for a large class of models some of which, however, have some undesirable features such as potentially negative output. This paper offers closed-form solutions for models which are just as tractable but do not suffer from these shortcomings.     

Macroeconomic shocks and fluctuations in African economies

We estimate a monetary DSGE model to examine the role of macroeconomic shocks in generating fluctuations in ten African countries. The model is estimated with the Bayesian technique using twelve macroeconomic variables. The findings indicate that both the internal and external shocks significantly influence output fluctuations in African economies. Over a four quarter horizon, internal shocks are dominant and over eight to sixteen quarter horizons, external shocks are dominant. Among the external shocks, external debt, exchange rate, foreign interest rate and commodity price shocks account for a large part of output variations in African economies. Money supply and productivity shocks are the most important internal shocks contributing to output fluctuations in African countries.     

Invertible and non-invertible information sets in linear rational expectations models

Rational expectations solutions are usually derived by assuming that all state variables relevant to forward-looking behaviour are directly observable, or that they are “…an invertible function of observables” (Mehra and Prescott, 1980). Using a framework that nests linearised DSGE models, we give a number of results useful for the analysis of linear rational expectations models with restricted information sets. We distinguish between instantaneous and asymptotic invertibility, and show that the latter may require significantly less information than the former. We also show that non-invertibility of the information set can have significant implications for the time series properties of economies.     

Tailored randomized block MCMC methods with application to DSGE models

In this paper we develop new Markov chain Monte Carlo schemes for the estimation of Bayesian models. One key feature of our method, which we call the tailored randomized block Metropolis–Hastings (TaRB-MH) method, is the random clustering of the parameters at every iteration into an arbitrary number of blocks. Then each block is sequentially updated through an M–H step. Another feature is that the proposal density for each block is tailored to the location and curvature of the target density based on the output of simulated annealing, following  and  and Chib and Ergashev (in press). We also provide an extended version of our method for sampling multi-modal distributions in which at a pre-specified mode jumping iteration, a single-block proposal is generated from one of the modal regions using a mixture proposal density, and this proposal is then accepted according to an M–H probability of move. At the non-mode jumping iterations, the draws are obtained by applying the TaRB-MH algorithm. We also discuss how the approaches of Chib (1995) and Chib and Jeliazkov (2001) can be adapted to these sampling schemes for estimating the model marginal likelihood. The methods are illustrated in several problems. In the DSGE model of Smets and Wouters (2007), for example, which involves a 36-dimensional posterior distribution, we show that the autocorrelations of the sampled draws from the TaRB-MH algorithm decay to zero within 30–40 lags for most parameters. In contrast, the sampled draws from the random-walk M–H method, the algorithm that has been used to date in the context of DSGE models, exhibit significant autocorrelations even at lags 2500 and beyond. Additionally, the RW-MH does not explore the same high density regions of the posterior distribution as the TaRB-MH algorithm. Another example concerns the model of An and Schorfheide (2007) where the posterior distribution is multi-modal. While the RW-MH algorithm is unable to jump from the low modal region to the high modal region, and vice-versa, we show that the extended TaRB-MH method explores the posterior distribution globally in an efficient manner.     

On randomization, modeling and experimental design: some remarks on a paper by De Vos

De Vos (1991) claims to have discovered a new example from agricultural field experimentation which shows that a simple robust spatial model may lead to inference and systematic experimental design that outperforms the inference from randomized experiments by far. In this reaction it is shown that: (1) the example is not new; (2) the gains in efficiency are exaggerated due to comparison with an inefficient randomization method; (3) the paper is over-optimistic with respect to robustness of model-based methods and throws unjustified doubts on the validity of randomization methods; (4) the choice between randomization methods and model-based methods depends on the relative importance attached to efficiency and validity.     

Optimal consumption under uncertainty,liquidity constraints,and bounded rationality

I study how boundedly rational agents can learn a “good” solution to an infinite horizon optimal consumption problem under uncertainty and liquidity constraints. Using an empirically plausible theory of learning I propose a class of adaptive learning algorithms that agents might use to choose a consumption rule. I show that the algorithm always has a globally asymptotically stable consumption rule, which is optimal. Additionally, I present extensions of the model to finite horizon settings, where agents have finite lives and life-cycle income patterns. This provides a simple and parsimonious model of consumption for large agent based models.