The ACD Model: Predictability of the Time Between Concecutive Trades

Forecasting ability of several parameterizations of ACD models are compared to benchmark linear autoregressions for inter-trade durations. The estimation of parametric ACD models requires both the choice of a conditional density for durations and the specification of a functional form for the conditional mean duration. Our results provide guidance for choosing among different parameterizations and for developing better forecasting models to predict one-step-ahead, multi-step-ahead, and the whole density of time durations. For evaluating density forecasts, we propose a new constructive test, which is based on the series of probability integral transforms. The choice of the conditional distribution for inter-trade durations does not seem to affect the out-of sample performances of the ACD at short, as well as longer, horizons. Yet, this choice becomes critical when forecasting the density.

Forecasting substantial data revisions in the presence of model uncertainty

A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable press attention, provoked a public enquiry and prompted a number of reforms to UK statistical reporting procedures. In this article, we compute the probability of 'substantial revisions' that are greater (in absolute value) than the controversial 2003 revision. The predictive densities are derived from Bayesian model averaging over a wide set of forecasting models including linear, structural break and regime-switching models with and without heteroscedasticity. Ignoring the nonlinearities and model uncertainty yields misleading predictives and obscures recent improvements in the quality of preliminary UK macroeconomic measurements

Forecast densities for economic aggregates from disaggregate ensembles

We extend the “bottom up” approach for forecasting economic aggregates with disaggregates to probability forecasting. Our methodology utilises a linear opinion pool to combine the forecast densities from many disaggregate forecasting specifications, using weights based on the continuous ranked probability score. We also adopt a post-processing step prior to forecast combination. These methods are adapted from the meteorology literature. In our application, we use our approach to forecast US Personal Consumption Expenditure inflation from 1990q1 to 2009q4. Our ensemble combining the evidence from 16 disaggregate PCE series outperforms an integrated moving average specification for aggregate inflation in terms of density forecasting.We thank the ARC, Norges Bank, the Reserve Bank of Australia and the Reserve Bank of New Zealand for supporting this research (LP 0991098)

A note on the linear, logit and probit functional form of the labour force participation rate equation

The commonly used specification in regional economic research on labour force participation is the linear probability function. An important alternative recommended in the Handbook of Regional and Urban Economics in the contribution of Isserman et al. (1986) on `Regional Labor Market Analysis' is the logit probability function. Their argument for the logit probability function is as follows. Given that economic theory on labour force participation does not suggest to pick one functional form over another and that the parameters of the logit probability function are estimable by OLS under the usual assumptions about the error term, the benefit of the logit probability function is that any estimated value for L lies within the logical bounds [0,1]. This feature is particularly desirable in a forecasting context when out of sample data might otherwise potentially yield absurd labour force participation rates. In this note two counter-arguments are gathered against using the logit probability function which are lacking in the Handbook of Regional and Urban Economics. Furthermore, it is shown that the logit probability function in this discourse can be replaced by the probit probability function equally well. Keywords: logit, probit, labour force participation rate.

Modest policy interventions

The authors present a framework for computing and evaluating linear projections of macro variables conditional on hypothetical paths of monetary policy. A modest policy intervention is a change in policy that does not significantly shift agents' beliefs about policy regime and does not generate quantitatively important expectations-formation effects of the kind Lucas (1976) emphasizes. The framework is applied to an econometric model of U.S. postwar monetary policy behavior. It finds that a rich class of interventions routinely considered by the Federal Reserve are modest and their impacts can be reliably forecast by an accurately identified linear model. Moreover, modest interventions can matter: They may shift the projected paths and probability distributions of macro variables in economically meaningful ways.Monetary policy ; Forecasting ; Vector autoregression