Imperfection Information, Optimal Monetary Policy and Informational Consistency

This paper examines the implications of imperfect information (II) for optimal monetary policy with a consistent set of informational assumptions for the modeller and the private sector an assumption we term the informational consistency. We use an estimated simple NK model from Levine et al. (2012), where the assumption of symmetric II significantly improves the fit of the model to US data to assess the welfare costs of II under commitment, discretion and simple Taylor-type rules. Our main results are: first, common to all information sets we find significant welfare gains from commitment only with a zero-lower bound constraint on the interest rate. Second, optimized rules take the form of a price level rule, or something very close across all information cases. Third, the combination of limited information and a lack of commitment can be particularly serious for welfare. At the same time we find that II with lags introduces a ‘tying ones hands’ effect on the policymaker that may improve welfare under discretion. Finally, the impulse response functions under our most extreme imperfect information assumption (output and inflation observed with a two-quarter delay) exhibit hump-shaped behaviour and the fiscal multiplier is significantly enhanced in this case

An empirical analysis of the welfare magnet debate using the NLSY

This paper examines the extent to which differences in welfare generosity across states lead to interstate migration. Using microdata from the National Longitudinal Survey of Youth between 1979 and 1992, we employ a quasi-experimental design that utilizes the categorical eligibility of the welfare system. The "treatment" group consists of all those in the survey who appear eligible to participate in Aid to Families with Dependent Children. The "control" group contains those who are poor but ineligible for other reasons. The pattern of cross-state moves among poor single women with children who are likely to be eligible for benefits (treatment-group members) is compared to the pattern among other poor households. We find little evidence indicating that welfare-induced migration is a widespread phenomenon.

The intergenerational correlation in AFDC participation: Welfare trap or poverty trap?

Several recent studies have shown that daughters whose mothers have participated in the welfare program Aid to Families with Dependent Children (AFDC), are themselves more likely to participate in AFDC when they head their own household. Other studies have shown that the earnings of parents and their children are highly correlated across generations. This suggests that any variable correlated with income such as AFDC participation will also be correlated across generations. This paper uses data from the original and youth cohorts of the National Longitudinal Surveys to investigate the question of whether the link in mother-daughter welfare participation is a causal relationship, or whether it can be explained by the expected intergenerational correlation in earnings. Several reduced-form probit equations are estimated, and attention is directed to the potential endogeneity of key explanatory variables. The empirical findings suggest that much of the observed correlation in AFDC participation across generations can be explained by the intergenerational correlation of income and other family characteristics.

Information, VARs and DSGE Models

How informative is a time series representation of a given vector of observables about the structural shocks and impulse response functions in a DSGE model? In this paper we refer to this econometrician’s problem as “E-invertibility” and consider the corresponding information problem of the agents in the assumed DGP, the DSGE model, which we refer to as “A-invertibility” We consider how the general nature of the agents’ signal extraction problem under imperfect information impacts on the econometrician’s problem of attempting to infer the nature of structural shocks and associated impulse responses from the data. We also examine a weaker condition of recoverability. A general conclusion is that validating a DSGE model by comparing its impulse response functions with those of a data VAR is more problematic when we drop the common assumption in the literature that agents have perfect information as an endowment. We develop measures of approximate fundamentalness for both perfect and imperfect information cases and illustrate our results using analytical and numerical examples

Estimation of Kalman filter model parameters from an ensemble of tests

A methodology for estimating initial mean and covariance parameters in a Kalman filter model from an ensemble of nonidentical tests is presented. In addition, the problem of estimating time constants and process noise levels is addressed. Practical problems such as developing and validating inertial instrument error models from laboratory test data or developing error models of individual phases of a test are generally considered

Detection of bacterial spores with lanthanide-macrocycle binary complexes

The detection of bacterial spores via dipicolinate-triggered lanthanide luminescence has been improved in terms of detection limit, stability, and susceptibility to interferents by use of lanthanide−macrocycle binary complexes. Specifically, we compared the effectiveness of Sm, Eu, Tb, and Dy complexes with the macrocycle 1,4,7,10-tetraazacyclododecane-1,7-diacetate (DO2A) to the corresponding lanthanide aquo ions. The Ln(DO2A)^+ binary complexes bind dipicolinic acid (DPA), a major constituent of bacterial spores, with greater affinity and demonstrate significant improvement in bacterial spore detection. Of the four luminescent lanthanides studied, the terbium complex exhibits the greatest dipicolinate binding affinity (100-fold greater than Tb^(3+) alone, and 10-fold greater than other Ln(DO2A)^+ complexes) and highest quantum yield. Moreover, the inclusion of DO2A extends the pH range over which Tb−DPA coordination is stable, reduces the interference of calcium ions nearly 5-fold, and mitigates phosphate interference 1000-fold compared to free terbium alone. In addition, detection of Bacillus atrophaeus bacterial spores was improved by the use of Tb(DO2A)^+, yielding a 3-fold increase in the signal-to-noise ratio over Tb^(3+). Out of the eight cases investigated, the Tb(DO2A)^+ binary complex is best for the detection of bacterial spores

Geologic application of thermal inertia imaging using HCMM data

Three test sites in the western US were selected to discriminate among surface geologic materials on the basis of their thermal properties as determined from HCMM data. Attempts to determine quantitatively accurate thermal inertia values from HCMM digital data met with only partial success due to the effects of sensor miscalibrations, radiative transfer in the atmosphere, and varying meteorology and elevation across a scene. In most instances, apparent thermal inertia was found to be an excellent qualitative representation of true thermal inertia. Computer processing of digital day and night HCMM data allowed construction of geologically useful images. At some test sites, more information was provided by data than LANDSAT data. Soil moisture effects and differences in spectrally dark materials were more effectively displayed using the thermal data

Internal rationality, learning and imperfect information

We construct, estimate and explore the monetary policy consequences of a New Keynesian (NK) behavioural model with bounded-rationality and heterogeneous agents. We radically depart from most existing models of this genre in our treatment of bounded rationality and learning. Instead of the usual Euler learning approach, we assume that agents are internally rational (IR) given their beliefs of aggregate states and prices. The model is inhabited by fully rational (RE) and IR agents where the latter use simple heuristic rules to forecast aggregate variables exogenous to their micro-environment. We find that IR results in an NK model with more persistence and a smaller policy space for rule parameters that induce stability and determinacy. In the most general form of the model, agents learn from their forecasting errors by observing and comparing them with those under RE making the composition of the two types endogenous. In a Bayesian estimation with fixed proportions of RE and IR agents and a general heuristic forecasting rule we find that a pure IR model fits the data better than the pure RE case. However, the latter with imperfect rather than the standard perfect information assumption outperforms IR (easily) and RE-IR composites (slightly), but second moment comparisons suggest that the RE-IR composite can match data better. Our findings suggest that Kalman-filtering learning with RE can match bounded-rationality in matching persistence seen in the data

Factors associated with intracerebral hemorrhage after thrombolytic therapy for ischemic stroke pooled analysis of placebo data from the Stroke-Acute Ischemic NXY Treatment (SAINT) I and SAINT II trials

<p><b>Background and Purpose:</b> A number of factors have been associated with postthrombolysis intracerebral hemorrhage, but these have varied across studies.</p> <p><b>Methods:</b> We examined patients with acute ischemic stroke treated with intravenous tissue plasminogen activator within 3 hours of symptom onset who were enrolled in the placebo arms of 2 trials (Stroke-Acute Ischemic NXY Treatment [SAINT] I and II Trials) of a putative neuroprotectant. Early CT changes were graded using the Alberta Stroke Program Early CT Score (ASPECTS). Post–tissue plasminogen activator symptomatic intracerebral hemorrhage was defined as a worsening in National Institutes of Health Stroke Scale of ≥4 points within 36 hours with evidence of hemorrhage on follow-up neuroimaging. Good clinical outcome was defined as a modified Rankin scale of 0 to 2 at 90 days.</p> <p><b>Results:</b> Symptomatic intracerebral hemorrhage occurred in 5.6% of 965 patients treated with tissue plasminogen activator. In multivariable analysis, symptomatic intracerebral hemorrhage was increased with baseline antiplatelet use (single antiplatelet: OR, 2.04, 95% CI, 1.07 to 3.87, P=0.03; double antiplatelet: OR, 9.29, 3.28 to 26.32, P<0.001), higher National Institutes of Health Stroke Scale score (OR, 1.09 per point, 1.03 to 1.15, P=0.002), and CT changes defined by ASPECTS (ASPECTS 8 to 9: OR, 2.26, 0.63 to 8.10, P=0.21; ASPECTS ≤7: OR, 5.63, 1.66 to 19.10, P=0.006). Higher National Institutes of Health Stroke Scale was associated with decreased odds of good clinical outcome (OR, 0.82 per point, 0.79 to 0.85, P<0.001). There was no relationship between baseline antiplatelet use or CT changes and clinical outcome.</p> <p><b>Conclusions:</b> Along with higher National Institutes of Health Stroke Scale and extensive early CT changes, baseline antiplatelet use (particularly double antiplatelet therapy) was associated with an increased risk of post–tissue plasminogen activator symptomatic intracerebral hemorrhage. Of these factors, only National Institutes of Health Stroke Scale was associated with clinical outcome.</p&gt