Fabrizio Iacone

Extracting information from asset prices: The methodology of EMU calculators

This paper develops a particular technique for extracting market expectations from asset prices. We use the term structure of interest rates to estimate the probability the market attaches to a country, Italy, joining the European Monetary Union at a given date. The extraction of such a probability is based on the presumption that the term structure contains valuable information regarding the markets’ assessment of a country’s chances of joining EMU. The case of Italy is interesting because in the survey regularly conducted by Reuters the probability that Italy joins EMU in 1999 fluctuated, in the first months of 1997, between 0.07 and 0.15 while during the same period the measures computed by financial houses – which are based on the term structure of interest rates – ranged between 0.5 and 0.8. The paper proposes a new method for computing these probabilities and shows that the discrepancies between survey and market-based measures are not the result of market inefficiencies, but of incorrect use of the term structure to compute probabilities. The technique proposed in the paper can also be used to distinguish between convergence of probabilities and convergence of fundamentals, that is to find out whether an observed reduction in interest rate spreads signals a higher probability of joining EMU at a given time, or simply reflects improved fundamentals. It could also be applied, more generally, to extract information on imminent changes in an exchange rate regime from asset prices.

Local Whittle Estimation of the Memory Parameter in Presence of Deterministic Components

We discuss the estimation of the order of integration of a fractional process that may be contaminated by a time-varying deterministic trend or by a break in the mean. We show that in some cases the estimate may still be consistent and asymptotically normally distributed even when the order of magnitude of the spectral density of the fractional process does not dominate the one of the periodogram of the contaminating term. If trimming is introduced, stronger deterministic components may be neglected. The performance of the estimate in small samples is studied in a Monte Carlo experiment. Copyright Copyright 2009 Blackwell Publishing Ltd

A FIXED‐ b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION

In this paper, we propose a test for a break in the level of a fractionally integrated process when the timing of the putative break is not known. This testing problem has received considerable attention in the literature in the case where the time series is weakly autocorrelated. Less attention has been given to the case where the underlying time series is allowed to be fractionally integrated. Here, valid testing can only be performed if the limiting null distribution of the level break test statistic is well defined for all values of the fractional integration exponent considered. However, conventional sup-Wald type tests diverge when the data are strongly autocorrelated. We show that a sup-Wald statistic, which is standardized using a non-parametric kernel-based long-run variance estimator, does possess a well-defined limit distribution, depending only on the fractional integration parameter, provided the recently developed fixed-b asymptotic framework is applied. We give the appropriate asymptotic critical values for this sup-Wald statistic and show that it has good finite sample size and power properties.

First Stage Estimation of Fractional Cointegration

In a fractionally cointegrated model, we analyze, both theoretically and by means of a Monte Carlo experiment, the performance of the most popular first stage estimation methods, including ordinary and narrow band least squares (Robinson, 1994), difference taper narrow band least squares (Chen and Hurvich, 2003a), instrumental variables (Robinson and Gerolimetto, 2006), and compare it with the behavior of a new proposal, the integrated ordinary least squares. An appropriate version of this latter estimator (and also of the instrumental variables one) achieves in all circumstances the fastest convergence rate (among other first stage methods) and performs well in finite samples. The use of improved first stage methods is most important in cases of low collective memory of regressor and cointegrating error. This is particularly relevant in multivariate settings, where the key parameters which rule the convergence properties of the estimators are the memories of adjacent cointegrating subspaces.

Modelling the dynamics of a public health care system: evidence from time-series data

The English National Health Service (NHS) was established in 1948, and has therefore yielded some long time series data on health system performance. Waiting time for inpatient care have been a persistent policy concern since the creation of the NHS. After developing a simple theoretical framework of the dynamic interaction between key indicators of health system performance, we investigate empirically the relationship between hospital activity, waiting time and population characteristics using aggregate time-series data for the NHS over the period 1952 to 2003. Structural Vector Autoregression (S-VAR) suggests that in the long run: higher activity is associated with lower waiting times (elasticity = −0.9); an increase in the elderly population is associated with higher waiting time (elasticity = 1.3). In the short run, higher lagged waiting time leads to higher activity (elasticity = 0.12). We also find that shocks in waiting times are countered by higher activity, so the effect is only temporary.

Comparing Predictive Accuracy in Small Samples Using Fixed-Smoothing Asymptotics

We consider fixed-smoothing asymptotics for the Diebold and Mariano (1995) test of predictive accuracy. We show that this approach delivers predictive accuracy tests that are correctly sized even when only a small number of out of sample observations are available. We apply the fixed-smoothing asymptotics to the Diebold and Mariano (1995) test to evaluate the predictive accuracy of the Survey of Professional Forecasters (SPF) and the ECB Survey of Professional Forecasters (ECB SPF) against a simple random walk. Our results show that the predictive abilities of the SPF and the ECB SPF were partially spurious.

Testing for a change in mean under fractional integration

Abstract We consider testing for the presence of a change in mean, at an unknown point in the sample, in data that are possibly fractionally integrated, and of unknown order. This testing problem has recently been considered in a number of papers, most notably Shao (2011, “A Simple Test of Changes in Mean in the Possible Presence of Long-Range Dependence.” Journal of Time Series Analysis 32:598–606) and Iacone, Leybourne, and Taylor (2013b, “A Fixed-b Test for a Break in Level at an Unknown Time under Fractional Integration.” Journal of Time Series Analysis 35:40–54) who employ Wald-type statistics based on OLS estimation and rely on a self-normalization to overcome the fact that the standard Wald statistic does not have a well-defined limiting distribution across different values of the memory parameter. Here, we consider an alternative approach that uses the standard Wald statistic but is based on quasi-GLS estimation to control for the effect of the memory parameter. We show that this approach leads to significant improvements in asymptotic local power.