An interactional model of questions as therapeutic interventions

We dedicate this article to the memory of Steve de Shazer, who was the University's External Examiner for the doctoral dissertation on which this article is based. His comments were both generous and influential, as they were on many other occasions. We would also like to thank Ron Chenail (as Editor of JMFT ) and Dora Fried Schnitman (as Editor of Sistemas Familiares) for coordinating the simultaneous review and publication of this article in English and Spanish.     

Preliminary-Data Error and Monetary Aggregate Targeting

Preliminary monetary aggregate data are subject to subsequent revision because of errors from seasonal and nonseasonal sources that will only be known later. This article examines the characteristics of these revision errors and analyzes their possible effect on monetary policy. It is found that false signals, in the sense that the preliminary M1 growth rate falls within the tolerance band set monthly by the Federal Open Market Committee while the final revised figure lies outside the tolerance limits, or vice versa, occur about two-fifths of the time. It is shown that this proportion can be halved by the adoption of improved seasonal adjustment procedures.     

Interpolation,outliers mid inverse autocorrelations

The paper addresses the problem of estimating missing observations in an infinite realization of a linear, possibly nonstationary, stochastic processes when the model is known. The general case of any possible distribution of missing observations in the time series is considered, and analytical expressions for the optimal estimators and their associated mean squared errors are obtained. These expressions involve solely the elements of the inverse or dual autocorrelation function of the series.

This optimal estimator -the conditional expectation of the missing observations given the available ones- is equal to the estimator that results from filling the missing values in the series with arbitrary numbers, treating these numbers as additive outliers, and removing with intervention analysis the outlier effects from the invented numbers.     

An Application of Nonlinear Time Series Forecasting

By means of a real application, it is seen how ARIMA forecasts can be improved when nonlinearities are present. The autocorrelation function (ACF) of the squared residuals provides a convenient tool to check the linearity assumption. Once nonlinearity has been detected, parsimonious bilinear processes seem rather adequate to model it. The detection of nonlinearity and the forecast improvement appear to be rather robust with respect to changes in the linear and bilinear specification. Finally, what bilinear models seem to capture are periods of atypical behavior or sequences of outliers.     

A basis approach to goodness-of-fit testing in recurrent event models

A class of tests for the hypothesis that the baseline hazard function in Cox's proportional hazards model and for a general recurrent event model belongs to a parametric family C identical with {lambda(0)(.; xi): xi in Xi} is proposed. Finite properties of the tests are examined via simulations, while asymptotic properties of the tests under a contiguous sequence of local alternatives are studied theoretically. An application of the tests to the general recurrent event model, which is an extended minimal repair model admitting covariates, is demonstrated. In addition, two real data sets are used to illustrate the applicability of the proposed tests.     

Relative efficiency of a censored experiment in terms of fisher information matrix

Fisher information matrix is used to quantify information loss in the randomly right-censored model, A real value approach alternative to the matrix approach of Turrero (1988) is presented for obtaining real valued measures of the relative efficiency of the censored experiment. Properties of the proposed measures are examined. The connection between both approaches and the Bayesian approach to this problem is also studied. Results in the paper are exemplified by considering grouped survival data.     

Goodness-of-Fit of the Distribution of Time-to-First-Occurrence in Recurrent Event Models

Imperfect repair models are a class of stochastic models that deal with recurrent phenomena. This article focuses on the Block, Borges, and Savits (1985) age-dependent minimal repair model (the BBS model) in which a system that fails at time t undergoes one of two types of repair: with probability p(t), a perfect repair is performed, or with probability 1-p(t), a minimal repair is performed. The goodness-of-fit problem of interest concerns the initial distribution of the failure ages. In particular, interest is on testing the null hypothesis that the hazard rate function of the time-to-first-event-occurrence, λ(·), is equal to a prespecified hazard rate function λ₀(·). This paper extends the class of hazard-based smooth goodness-of-fit tests introduced in Peña (1998a) to the case where data accrual is from a BBS model. The goodness-of-fit tests are score tests derived by reformulating Neyman's idea of smooth tests in terms of hazard functions. Omnibus as well as directional tests are developed and simulation results are presented to illustrate the sensitivities of the proposed tests for certain types of alternatives.     

SURPRISE ME IF YOU CAN: THE INFLUENCE OF NEWSPAPER ENDORSEMENTS IN U.S. PRESIDENTIAL ELECTIONS

We here evaluate the heterogeneous effects of newspaper endorsements of U.S. Presidential candidates in the 100 days preceding the 2008 and 2012 elections on the probability that they win the election. Our identification strategy relies on daily variations in the winning probabilities (obtained from the Intrade prediction market) and the fact that newspapers decide their endorsements weeks before their announcement. Endorsements that are classified as surprising and consistent have the largest effect. An endorsement is surprising when the newspaper has not traditionally endorsed the candidate's party. An endorsement is inconsistent when the newspaper leans ideologically to one party but endorses a candidate from another party. (JEL L82, D7)