The choice of time interval in seasonal adjustment: A heuristic approach

A typical problem of the seasonal adjustment procedures arises when the series to be adjusted is subject to structural breaks. In fact, using the full span of the series can result in a biased estimation of the “true” seasonally adjusted series, with unclear evidence showed by the usual diagnostic tests. In these cases the researcher has to decide where to cut-off the observed series to obtain a homogeneous span; this is generally performed by a simple visual inspection of the graph of the series and/or using a-priori information about the occurrence of the break. In this paper we propose a statistical criterion based on a distance measure between filters, evaluating its performance with Monte Carlo experiments. The first results of this work have been presented at the XL scientific meeting of the Italian statistical society, Florence, 26–28 April 2000, benefiting of the discussion arisen there; a preliminary version of this paper circulated as ISAE working paper No. 21/2001 with the title “The Choice of Time Interval in Seasonal Adjustment: Characterization and Tools”. We thank an anonymous referee for precious suggestions. The authors are solely responsible of any remaining error.     

Tests for the equality of multinomial parameters

In this paper we face the problem of testing the equality of two or more parameters of a multinomial distribution. We develop a likelihood ratio test and we consider an asymptotically equivalent Pearson's statistic. Moreover we develop an exact and a randomized test. Relationships between these tests are then discussed. The behaviour of these tests is studied by simulations. Results from two known tests developed for less general situations are compared to ours.     

Improved Estimators of the Population Mean for Missing Data

For two dependent random variables X and Y with distributions of convolution equivalence, sufficient conditions are given under which the distribution of the minimum min (X, Y) is still of convolution equivalence. We further extend the result to the multivariate case.     

THE MYSTERY OF THE PRINTING PRESS: MONETARY POLICY AND SELF‐FULFILLING DEBT CRISES

We study the conditions under which unconventional (balance sheet) monetary policy can rule out self‐fulfilling sovereign default in a model with optimizing but discretionary fiscal and monetary policymakers. When purchasing sovereign debt, the central bank effectively swaps risky government paper for monetary liabilities only exposed to inflation risk, thus yielding a lower interest rate. As central bank purchases reduce the (ex ante) costs of debt, we characterize a critical threshold beyond which, absent fundamental fiscal stress, the government strictly prefers primary surplus adjustment to default. Because default may still occur for fundamental reasons, however, the central bank faces the risk of losses on sovereign debt holdings, which may generate inefficient inflation. We show that these losses do not necessarily undermine the credibility of a backstop, nor the monetary authorities' ability to pursue its inflation objectives. Backstops are credible if either the central bank enjoys fiscal backing or fiscal authorities are sufficiently averse to inflation. (JEL: E58, E63, H63)     

THE INTERNATIONAL DIMENSION OF PRODUCTIVITY AND DEMAND SHOCKS IN THE US ECONOMY

This paper analyzes the cross‐country effects of productivity and demand disturbances in the United States identified with sign restrictions based on standard theory. Productivity gains in US manufacturing increase US consumption and investment vis‐à‐vis foreign countries, resulting in a trade deficit and higher international prices of US goods, despite the rise in their supply. Financial adjustment works via a higher global value of US equities, real dollar appreciation, and an expansion of US gross foreign liabilities as well as assets. Positive demand shocks to US manufacturing also increase investment and cause a real dollar appreciation, but have limited effects on the trade balance and net foreign assets. Our findings emphasize the importance for macroeconomic interdependence of endogenous fluctuations in aggregate demand across countries in response to business cycle shocks.     

New scrambled response models for estimating the mean of a sensitive quantitative character

Moving from the scrambling mechanism recently suggested by Saha [25 Saha, A. 2007. A simple randomized response technique in complex surveys. Metron, LXV: 59–66.  [Google Scholar]], three scrambled randomized response (SRR) models are introduced with the intent to realize a right trade-off between efficiency and privacy protection. The models perturb the true response on the sensitive variable by resorting to the multiplicative and additive approaches in different ways. Some analytical and numerical comparisons of efficiency are performed to set up the conditions under which improvements upon Saha's model can be obtained and to quantify the efficiency gain. The use of auxiliary information is also discussed in a class of estimators for the sensitive mean under a generic randomization scheme. The class includes also the three proposed SRR models. Finally, some graphical comparisons are carried out from the double perspective of the accuracy in the estimates and respondents’ privacy protection.     

A Clustering Method for Categorical Ordinal Data

Often, categorical ordinal data are clustered using a well-defined similarity measure for this kind of data and then using a clustering algorithm not specifically developed for them. The aim of this article is to introduce a new clustering method suitably planned for ordinal data. Objects are grouped using a multinomial model, a cluster tree and a pruning strategy. Two types of pruning are analyzed through simulations. The proposed method allows to overcome two typical problems of cluster analysis: the choice of the number of groups and the scale invariance.     

Hansen and Hurwitz estimator with scrambled response on the second call

In this paper we propose a modified version of the estimator of Hansen and Hurwitz [12] in the case of quantitative sensitive variable and consider a randomization mechanism on the second call that provides privacy protection to the respondents to get truthful information. We use variance of the modified estimator as a tool to measure privacy protection and it is observed that the higher is the variance, the lower is the efficiency but the higher is the privacy protection. To overcome this efficiency loss, we consider a linear regression estimator using known non-sensitive auxiliary information. With consideration of four scrambled models, we try to make a trade-off between efficiency and privacy protection. To show this compromise, analytical and numerical comparisons are obtained.     

Biases in bias elicitation

Abstract

We consider the biases that can arise in bias elicitation when expert assessors make random errors. After presenting a general framework of the phenomenon, we illustrate it for two examples: the case of omitting variables bias and that of the bias arising in adjusting relative risks. Results show that, even when assessors’ elicitations of bias have desirable properties, the nonlinear nature of biases can lead to elicitations of bias that are, themselves, biased. We show the corrections which can be made to remove this bias and discuss the implications for the applied literature which employs these methods.