Specifying the Forecast Generating Process for Exchange Rate Survey Forecasts

This paper contributes to the literature on the modeling of survey forecasts using learning variables. We use individual industry data on yen-dollar exchange rate predictions at the two week, three month, and six month horizons supplied by the Japan Center for International Finance. Compared to earlier studies, our focus is not on testing a single type of learning model, whether univariate or mixed, but on searching over many types of learning models to determine if any are congruent. In addition to including the standard expectational variables (adaptive, extrapolative, and regressive), we also include a set of interactive variables which allow for lagged dependence of one industry’s forecast on the others. Our search produces a remarkably small number of congruent specifications-even when we allow for 1) a flexible lag specification, 2) endogenous break points and 3) an expansion of the initial list of regressors to include lagged dependent variables and use a General-to-Specific modeling strategy. We conclude that, regardless of forecasters’ ability to produce rational forecasts, they are not only “different,” but different in ways that cannot be adequately represented by learning models.Learning Models, Exchange Rate, Survey Forecasts

The Rationality and Heterogeneity of Survey Forecasts of the Yen-Dollar Exchange Rate: A Reexamination

This paper examines the rationality and diversity of industry-level forecasts of the yen-dollar exchange rate collected by the Japan Center for International Finance. In several ways we update and extend the seminal work by Ito (1990). We compare three specifications for testing rationality: the ”conventional” bivariate regression, the univariate regression of a forecast error on a constant and other information set variables, and an error correction model (ECM). We find that the bivariate specification, while producing consistent estimates, suffers from two defects: first, the conventional restrictions are suffcient but not necessary for unbiasedness; second, the test has low power. However, before we can apply the univariate specification, we must conduct pretests for the stationarity of the forecast error. We find a unit root in the six-month horizon forecast error for all groups, thereby rejecting unbiasedness and weak effciency at the pretest stage. For the other two horizons, we find much evidence in favor of unbiasedness but not weak effciency. Our ECM rejects unbiasedness for all forecasters at all horizons. We conjecture that these results, too, occur because the restrictions test suffciency, not necessity. In our systems estimation and micro- homogeneity testing, we use an innovative GMM technique (Bonham and Cohen (2001)) that allows for forecaster cross-correlation due to the existence of common shocks and/or herd e ects. Tests of micro-homogeneity uniformly reject the hypothesis that forecasters across the four industries exhibit similar rationality characteristics.Rational Expectations, Heterogeneity, Exchange Rate, Survey Forecast

Evidence regarding clinical use of microvolt T-wave alternans [Accuracy of microvolt T-wave alternans testing]

Background: Microvolt T-wave alternans (MTWA) testing in many studies has proven to be a highly accurate predictor of ventricular tachyarrhythmic events (VTEs) in patients with risk factors for sudden cardiac death (SCD) but without a prior history of sustained VTEs (primary prevention patients). In some recent studies involving primary prevention patients with prophylactically implanted cardioverter-defibrillators (ICDs), MTWA has not performed as well. Objective: This study examined the hypothesis that MTWA is an accurate predictor of VTEs in primary prevention patients without implanted ICDs, but not of appropriate ICD therapy in such patients with implanted ICDs. Methods: This study identified prospective clinical trials evaluating MTWA measured using the spectral analytic method in primary prevention populations and analyzed studies in which: (1) few patients had implanted ICDs and as a result none or a small fraction (≤15%) of the reported end point VTEs were appropriate ICD therapies (low ICD group), or (2) many of the patients had implanted ICDs and the majority of the reported end point VTEs were appropriate ICD therapies (high ICD group). Results: In the low ICD group comprising 3,682 patients, the hazard ratio associated with a nonnegative versus negative MTWA test was 13.6 (95% confidence interval [CI] 8.5 to 30.4) and the annual event rate among the MTWA-negative patients was 0.3% (95% CI: 0.1% to 0.5%). In contrast, in the high ICD group comprising 2,234 patients, the hazard ratio was only 1.6 (95% CI: 1.2 to 2.1) and the annual event rate among the MTWA-negative patients was elevated to 5.4% (95% CI: 4.1% to 6.7%). In support of these findings, we analyzed published data from the Multicenter Automatic Defibrillator Trial II (MADIT II) and Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT) trials and determined that in those trials only 32% of patients who received appropriate ICD therapy averted an SCD. Conclusion: This study found that MTWA testing using the spectral analytic method provides an accurate means of predicting VTEs in primary prevention patients without implanted ICDs; in particular, the event rate is very low among such patients with a negative MTWA test. In prospective trials of ICD therapy, the number of patients receiving appropriate ICD therapy greatly exceeds the number of patients who avert SCD as a result of ICD therapy. In trials involving patients with implanted ICDs, these excess appropriate ICD therapies seem to distribute randomly between MTWA-negative and MTWA-nonnegative patients, obscuring the predictive accuracy of MTWA for SCD. Appropriate ICD therapy is an unreliable surrogate end point for SCD

To Aggregate, Pool, or Neither: Testing the Rational Expectations Hypothesis Using Survey Data

It is well known that even if all forecasters are rational, estimated coefficients in unbiasedness regressions using consensus forecasts are inconsistent because forecasters have private information. However, if all forecasters face a common realization, pooled estimators are also inconsistent. In contrast, we show that when predictions and realizations are integrated and cointegrated, micro-homogeneity ensures that consensus and pooled estimators are consistent. Therefore, contrary to claims in the literature, in the absence of micro-homogeneity, pooling is not a solution to the aggregation problem. We reject micro-homogeneity for a number of forecasts from the Survey of Professional Forecasters. Therefore, for these variables unbiasedness can only be tested at the individual level.Rational Expectations, Micro-homogeneity, Heterogeneity Bias, Aggregation Bias, Survey Forecasts

Model-Independent Bounds on R(J/ψ)R(J/\psi)

We present a model-independent bound on $R(J/\psi) \! \equiv \! \mathcal{BR} (B_c^+ \rightarrow J/\psi \, \tau^+\nu_\tau)/ \mathcal{BR} (B_c^+ \rightarrow J/\psi \, \mu^+\nu_\mu)$. This bound is constructed by constraining the form factors through a combination of dispersive relations, heavy-quark relations at zero-recoil, and the limited existing determinations from lattice QCD. The resulting 95\% confidence-level bound, $0.20\leq R(J/\psi)\leq0.39$, agrees with the recent LHCb result at $1.3 \, \sigma$, and rules out some previously suggested model form factors.Comment: 19 pages, 4 figures, JHEP format, revised to match published versio