Monetary Policy And Key Unobservables: Evidence From Large Industrial And Selected Inflation-Targeting Countries

Among the variables that play critical roles in the design of monetary policy, several are unobservable. These include such key variables as the neutral real rate of interest, the output gap, and the natural rate of unemployment. While individual central banks have undertaken efforts to estimate these unobservables, the approaches have generally been country specific and have not provided either systematic estimation or comparison across countries. We adopt a common estimation approach, applied to a parsimonious monetary-policy model, to provide consistent estimates of key unobservables for the U.S., the Eurozone, and Japan, and several inflation-targeting countries: Australia, Canada, Chile, New Zealand, Norway, Sweden, and the U.K. Doing so allows us to obtain comparable measures of unobservables across a range of countries. We exploit our estimates to investigate issues of commonalities and convergence across countries in these key but unobservable variables.

Testing non-nested structural equation models

In this paper, we apply Vuong's (1989) likelihood ratio tests of non-nested models to the comparison of non-nested structural equation models. Similar tests have been previously applied in SEM contexts (especially to mixture models), though the non-standard output required to conduct the tests has limited their previous use and study. We review the theory underlying the tests and show how they can be used to construct interval estimates for differences in non-nested information criteria. Through both simulation and application, we then study the tests' performance in non-mixture SEMs and describe their general implementation via free R packages. The tests offer researchers a useful tool for non-nested SEM comparison, with barriers to test implementation now removed.Comment: 24 pages, 6 figure

Symmetrically normalized instrumental-variable estimation using panel data

In this paper we discuss the estimation of panel data models with sequential moment restrictions using symmetrically normalized GMM estimators. These estimators are asymptotically equivalent to standard GMM but are invariant to normalization and tend to have a smaller finite sample bias. They also have a very different behaviour compared to standard GMM when the instruments are poor. We study the properties of SN-GMM estimators in relation to GMM, minimum distance and pseudo maximum likelihood estimators for various versions of the AR(1) model with individual effects by mean of simulations. The emphasis is not in assessing the value of enforcing particular restrictions in the model; rather, we wish to evaluate the effects in small samples of using alternative estimating criteria that produce asymptotically equivalent estimators for fixed T and large N. Finally, as an empírical illustration, we estimate by SN-GMM employment and wage equations using panels of UK and Spanish firms

Clinical validity assessment of a breast cancer risk model combining genetic and clinical information

_Background:_ The extent to which common genetic variation can assist in breast cancer (BCa) risk assessment is unclear. We assessed the addition of risk information from a panel of BCa-associated single nucleotide polymorphisms (SNPs) on risk stratification offered by the Gail Model.

_Methods:_ We selected 7 validated SNPs from the literature and genotyped them among white women in a nested case-control study within the Women’s Health Initiative Clinical Trial. To model SNP risk, previously published odds ratios were combined multiplicatively. To produce a combined clinical/genetic risk, Gail Model risk estimates were multiplied by combined SNP odds ratios. We assessed classification performance using reclassification tables and receiver operating characteristic (ROC) curves. 

_Results:_ The SNP risk score was well calibrated and nearly independent of Gail risk, and the combined predictor was more predictive than either Gail risk or SNP risk alone. In ROC curve analysis, the combined score had an area under the curve (AUC) of 0.594 compared to 0.557 for Gail risk alone. For reclassification with 5-year risk thresholds at 1.5% and 2%, the net reclassification index (NRI) was 0.085 (Z = 4.3, P = 1.0×10^-5^). Focusing on women with Gail 5-year risk of 1.5-2% results in an NRI of 0.195 (Z = 3.8, P = 8.6×10^−5^).

_Conclusions:_ Combining clinical risk factors and validated common genetic risk factors results in improvement in classification of BCa risks in white, postmenopausal women. This may have implications for informing primary prevention and/or screening strategies. Future research should assess the clinical utility of such strategies.
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