Abstract not found

The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary are likely to fall into a chasm if not careful. These chasms are very deep indeed given the complexity of the mixed logit model. Although the theory is relatively clear, estimation and data issues are far from clear. Indeed there is a great deal of potential mis-inference consequent on trying to extract increased behavioural realism from data that are often not able to comply with the demands of mixed logit models. Possibly for the first time we now have an estimation method that requires extremely high quality data if the analyst wishes to take advantage of the extended behavioural capabilities of such models. This paper focuses on the new opportunities offered by mixed logit models and some issues to be aware of to avoid misuse of such advanced discrete choice methods by the practitioner.

We compare forecasts of recessions using four different specifications of the probit model: a time invariant conditionally independent version; a business cycle specific conditionally independent model; a time invariant probit with autocorrelated errors; and a business cycle specific probit with autocorrelated errors. The more sophisticated versions of the model take into account some of the potential underlying causes of the documented predictive instability of the yield curve. We find strong evidence in favor of themoresophisticatedspecification, which allows for multiple breakpoints across business cycles and autocorrelation. We also develop a new approach to the construction of real time forecasting of recession probabilities.

In this paper we propose a Bayesian econometric procedure for the evaluation and comparison of DSGE models. Unlike in many previous econometric approaches we explicitly take into account the possibility that the DSGE mod-els are misspecified and introduce a reference model to complete the model space. Three loss functions are proposed to assess the discrepancy between DSGE model predictions and an overall posterior distribution of population characteristics that the researcher is trying to match. The evaluation proce-dure is applied to the comparison of a standard cash-in-advance (CIA) and a portfolio adjustment cost (PAC) model. We find that the CIA model has higher posterior probability than the PAC model and achieves a better in-sample time series fit. Both models overpredict the magnitude of the negative correlation between output growth and inflation. However, unlike the PAC model, the CIA model is not able to generate a positive real effect of money growth shocks on aggregate output. Overall, the impulse response dynamics of the PAC model resemble the posterior mean impulse response functions more closely than the responses of the CIA model.

Abstract: Tests of the present-value model of the current account are frequently rejected by the data. Standard explanations rely on the “usual suspects ” of nonseparable preferences, shocks to fiscal policy and the world real interest rate, and imperfect international capital mobility. The authors confirm these rejections on postwar Canadian data, then investigate their source by calibrating and simulating alternative versions of a small open economy, real business cycle model. Monte Carlo experiments reveal that, although each of the suspects matters in some way, a “canonical ” RBC model moves closest to the data when it features exogenous world real interest rate shocks.

In 2006 all ECB publications will feature a motif taken from the €5 banknote. This paper can be downloaded without charge from

In the vehicle routing problem with time windows a number of identical vehicles must be routed to and from a depot to cover a given set of customers, each of whom has a specified time interval indicating when they are available for service. Each customer also has a known demand, and a vehicle may only serve the customers on a route if the total demand does not exceed the capacity of the vehicle. The most effective solution method proposed to date for this problem is due to Kohl, Desrosiers, Madsen, Solomon, and Soumis. Their algorithm uses a cutting-plane approach followed by a branchand -bound search with column generation, where the columns of the LP relaxation represent routes of individual vehicles. We describe a new implementation of their method, using Karger's randomized minimum-cut algorithm to generate cutting planes. The standard benchmark in this area is a set of 87 problem instances generated in 1984 by M. Solomon; making using of parallel processing in both the cutting-pla...

ABSTRACT. SVARs are widely used for policy analysis and to provide stylized facts for dynamic general equilibrium models. Yet there have been no workable rank conditions to ascertain whether an SVAR is globally identified and no efficient al-gorithms for small-sample statistical inference when identifying restrictions are di-rectly imposed on impulse responses. To fill these important gaps in the literature, this paper makes four contributions. First, we establish a general rank condition for both exactly and overidentified models. Second, we show that this condition can be easily checked analytically and applies to a wide class of identifying restrictions, including linear and certain nonlinear restrictions. Third, we establish a much sim-pler rank condition for exactly identified models that amounts to a straightforward counting exercise. Fourth, we develop a number of efficient algorithms for small-sample statistical inference. I.

We estimate several variants of a linearized form of a New Keynesian model using quarterly US data. Using these rival models and the estimated posterior probabilities we then design rules that are robust in two senses: ‘weakly robust ’ rules are guaranteed to be stable and determinate in all the possible variants of the model, whereas ‘strongly robust ’ rules, in addition, use the probabilities to minimize an expected loss function of the central bank subject to this model uncertainty. We find three main results. First, in our two model variants with the highest posterior model probabilities there are substantial stabilization gains from commitment. Second, an optimized inflation targeting rule feeding back on current inflation will result in a unique stable equilibrium and realize at least three-quarters of these potential gains, even if it is used in a variant of the model that is not the one for which it was designed. Third, the performance of optimimized inflation targeting rules perform increasing less well as the forward horizon increases from j = 0 to j = 1,2 quarters. For j=2, only a rule designed for our most indeterminacy-prone model is weakly robust and yields determinacy across all models. A strongly robust rule can be designed that sacrifices performance in the least probable models for better performance in the most probable models. JEL Classification: E52, E37, E58

In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the autoregressive representation or in a separate state equation. Tests based on the former are analogous to Dickey-Fuller tests of unit roots, while the latter are analogous to KPSS tests of trend-stationarity. We use Bayesian methods to survey the properties of the likelihood function in such models and to calculate posterior odds ratios comparing models with and without stochastic trends. We extend these ideas to the problem of testing for integration at seasonal frequencies and showhow our techniques can be used to carry out Bayesian variants of either the HEGY or Canova-Hansen test. Stochastic integration rules, based on Markov Chain Monte Carlo, as well as deterministic integration rules are used. Strengths and weaknesses of each approach are indicated.