An alternative to the weighted score card approach to measuring performance: An analysis of English and Welsh probation boards

The UK government published a weighted score card approach in 2003 to analyse the performance of Probation Boards in England and Wales. However, there has not been a thorough analysis of whether non-parametric methods could provide more advanced options for analysing performance than the standard Weighted Score Card approach – a variant of the Balanced Score Card. Our results show there is considerable divergence in the ranks of Probation Boards from that of the WSC when we include the input variable ‘resource expenditure’ within a DEA model, calling into question recent policy initiatives to increase efficiency in the national probation service.     

Spatial concentration and plant-level productivity in France

This paper analyzes empirically the effect of spatial agglomeration of activities on plant-level productivity, using French firm and plant-level data from 1996 to 2004. We exploit short-run variations of variables by making use of GMM estimation. This allows us to control for endogeneity biases that the estimation of agglomeration economies typically encounters. This means that our paper focuses on a subset of agglomeration economies, the short-run ones. Our results show that French plants benefit from localization economies, but we find very little - if any - evidence of urbanization economies. We also show that those localization benefits are relatively well internalized by firms in their location choice: we find very little difference between the geography that would maximize productivity gains in the short-run and the geography actually observed.     

On the equivalence of location choice models: Conditional logit, nested logit and Poisson

It is well understood that the two most popular empirical models of location choice - conditional logit and Poisson - return identical coefficient estimates when the regressors are not individual specific. We show that these two models differ starkly in terms of their implied predictions. The conditional logit model represents a zero-sum world, in which one region’s gain is the other regions’ loss. In contrast, the Poisson model implies a positive-sum economy, in which one region’s gain is no other region’s loss. We also show that all intermediate cases can be represented as a nested logit model with a single outside option. The nested logit turns out to be a linear combination of the conditional logit and Poisson models. Conditional logit and Poisson elasticities mark the polar cases and can therefore serve as boundary values in applied research.     

Sources of the great moderation: A time-series analysis of GDP subsectors

Recent work finds evidence that the volatility of the U.S. economy fell dramatically around the first quarter of 1984. We trace the timing of this so-called “Great Moderation” across many subsectors of the economy in order to better understand its root cause. We find that the interest rate sensitive sectors generally experience a much earlier volatility decline than other large sectors of the economy. The changes in Federal Reserve stabilization policies that occurred during the early 1980s support the view that an improved monetary policy played an important role in stabilizing real economic activity. We find only mild evidence that “good luck” was important and little evidence to support the claim that improved inventory management was important.     

Scarcity, regulation and endogenous technical progress

This paper studies to which extent a firm using a scarce resource input and facing environmental regulation can still manage to have a sustainable growth of output and profits. The firm has a vintage capital technology with two complementary factors, capital and a resource input subject to quota, the latter being increasingly scarce through an exogenously rising price. The firm can scrap obsolete capital and invest in adoptive and/or innovative R&D resource-saving activities. Within this realistic framework, we first characterize long-term growth regimes driven by scarcity (induced-innovation) vs. long-term growth regimes driven by quota regulation (Porter-like innovation). More importantly, we study the interaction between scarcity and quota regulation. In particular, we show that there exists a threshold level for the growth rate of the resource price above which the Porter mechanism is killed while the scarcity-induced growth regime may emerge. Symmetrically, we also find that there must exist a threshold value for the environmental quota under which the growth regime induced by scarcity vanishes while the Porter-like growth regime may survive.     

Particle filters for continuous likelihood evaluation and maximisation

In this paper, a method is introduced for approximating the likelihood for the unknown parameters of a state space model. The approximation converges to the true likelihood as the simulation size goes to infinity. In addition, the approximating likelihood is continuous as a function of the unknown parameters under rather general conditions. The approach advocated is fast and robust, and it avoids many of the pitfalls associated with current techniques based upon importance sampling. We assess the performance of the method by considering a linear state space model, comparing the results with the Kalman filter, which delivers the true likelihood. We also apply the method to a non-Gaussian state space model, the stochastic volatility model, finding that the approach is efficient and effective. Applications to continuous time finance models and latent panel data models are considered. Two different multivariate approaches are proposed. The neoclassical growth model is considered as an application.     

Two-stage non Gaussian QML estimation of GARCH models and testing the efficiency of the Gaussian QMLE

In generalized autoregressive conditional heteroskedastic (GARCH) models, the standard identifiability assumption that the variance of the iid process is equal to 1 can be replaced by an alternative moment assumption. We show that, for estimating the original specification based on the standard identifiability assumption, efficiency gains can be expected from using a quasi-maximum likelihood (QML) estimator based on a non Gaussian density and a reparameterization based on an alternative identifiability assumption. A test allowing to determine whether a reparameterization is needed, that is, whether the more efficient QMLE is obtained with a non Gaussian density, is proposed.     

Realized volatility forecasting and market microstructure noise

We extend the analytical results for reduced form realized volatility based forecasting in ABM (2004) to allow for market microstructure frictions in the observed high-frequency returns. Our results build on the eigenfunction representation of the general stochastic volatility class of models developed byMeddahi (2001). In addition to traditional realized volatility measures and the role of the underlying sampling frequencies, we also explore the forecasting performance of several alternative volatility measures designed to mitigate the impact of the microstructure noise. Our analysis is facilitated by a simple unified quadratic form representation for all these estimators. Our results suggest that the detrimental impact of the noise on forecast accuracy can be substantial. Moreover, the linear forecasts based on a simple-to-implement ‘average’ (or ‘subsampled’) estimator obtained by averaging standard sparsely sampled realized volatility measures generally perform on par with the best alternative robust measures.     

Measuring correlations of integrated but not cointegrated variables: A semiparametric approach

Many macroeconomic and financial variables are integrated of order one (or I(1)) processes and are correlated with each other but not necessarily cointegrated. In this paper, we propose to use a semiparametric varying coefficient approach to model/capture such correlations. We propose two consistent estimators to study the dependence relationship among some integrated but not cointegrated time series variables. Simulations are used to examine the finite sample performances of the proposed estimators.     

Inference with dependent data using cluster covariance estimators

This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing t and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed and the number of observations per group to be large. The resulting limiting distributions of the t and Wald statistics are standard t and F distributions where the number of groups plays the role of sample size. Using a small number of groups is analogous to ‘fixed-b’ asymptotics of [Kiefer and Vogelsang, 2002] and [Kiefer and Vogelsang, 2005] (KV) for heteroskedasticity and autocorrelation consistent inference. We provide simulation evidence that demonstrates that the procedure substantially outperforms conventional inference procedures.