Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

This article documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The article is the result of a collaborative effort between staff at the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not exclusively focus on point forecast performance. It also examines methodological contributions, including how financial market data could have been incorporated into the forecasting process.     

Do We Trust the Data? On the Validity and Reliability of Cross‐National Environmental Surveys

Objective. This article provides evidence on the validity and reliability of cross-national environmental surveys, an aspect that has not found much attention so far. Methods. Validity can be checked in examining whether cross-national differences in environmental attitudes are in accordance with theoretical hypotheses. For example, a study can claim some validity if concern about inadequate sanitation is strongly negatively correlated with the actual extent of access to sanitation in a country. Results. Several validity checks were undertaken for the Gallup, Gallup, and Dunlap (1993) survey, all of which tend to support its validity. Next, the reliability of cross-national environmental surveys was checked. Both Pearson and Spearman rank correlations were run for similar questions from differing studies. Most correlations were low and statistically insignificant, however, thus putting some doubt on the reliability of cross-national environmental surveys, at least with respect to the questions examined. Conclusions. The findings support the validity of cross-national environmental surveys, but not their reliability. Future surveys should be designed such that validity and reliability checks become easier to undertake.     

From Social Instrument to Migration Management Tool: Assisted Voluntary Return Programmes – The Case of Belgium

The return of migrants to their country of origin and the development of efficient return measures have become more prominent on the political agenda of many Western European countries. Since policymakers prefer ‘voluntary’ return, governmental programmes to support the return of migrants – Assisted Voluntary Return (AVR) programmes – were developed as far back as the 1970s and have played an increasingly important role in migration policy over the last three decades. At the same time, general migration policy and welfare systems have undergone profound change, including in the meanings and connotations attached to social welfare, return support and return policy. This raises questions about the implications of these broader societal and policy changes for the widely implemented AVR programmes. In this article, we discuss the interpretation and evolution of AVR programmes by analyzing how one particular European country, Belgium, has developed its AVR programme over time. We explore the evolution of the programme's content, target group and institutional positioning, which shed light on its changing goals and are closely linked to a broader shift towards a ‘managerial’ approach to migration policy and the welfare state. We argue that return support may become decontextualized when it adopts ‘conditional entitlement’ as a central principle. This leads to strong differentiation, based on personal responsibility, between ‘deserving’ and ‘undeserving’ migrants, the levelling down of the support given to returnees, and a more coercive voluntary return policy in which social support is linked to deportation.     

AN EVALUATION OF NON-ITERATIVE METHODS FOR ESTIMATING THE LINEAR-BY-LINEAR PARAMETER OF ORDINAL LOG-LINEAR MODELS

Parameter estimation for association and log-linear models is an important aspect of the analysis of cross-classified categorical data. Classically, iterative procedures, including Newton's method and iterative scaling, have typically been used to calculate the maximum likelihood estimates of these parameters. An important special case occurs when the categorical variables are ordinal and this has received a considerable amount of attention for more than 20 years. This is because models for such cases involve the estimation of a parameter that quantifies the linear-by-linear association and is directly linked with the natural logarithm of the common odds ratio. The past five years has seen the development of non-iterative procedures for estimating the linear-by-linear parameter for ordinal log-linear models. Such procedures have been shown to lead to numerically equivalent estimates when compared with iterative, maximum likelihood estimates. Such procedures also enable the researcher to avoid some of the computational difficulties that commonly arise with iterative algorithms. This paper investigates and evaluates the performance of three non-iterative procedures for estimating this parameter by considering 14 contingency tables that have appeared in the statistical and allied literature. The estimation of the standard error of the association parameter is also considered.     

Bayesian Analysis of Stochastic Volatility Models

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain converge in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of our Markov-chain method. In addition, multistep-ahead predictive densities can be constructed that reflect both inherent model variability and parameter uncertainty. We illustrate our method by analyzing both daily and weekly data on stock returns and exchange rates. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches.     

The International Monetary Fund, World Bank, and structural adjustment: A cross-national analysis of forest loss

We test competing hypotheses drawn from neo-liberal economic theory and dependency theory regarding the effects of International Monetary Fund and World Bank structural adjustment on deforestation. In doing so, we analyze cross-national data for a sample of sixty low and middle income nations from 1990 to 2005. We find substantial support for dependency theory that both International Monetary Fund and World Bank structural adjustment lending are associated with higher rates of forest loss. We also find that a number of factors linked to other theoretical perspectives help to explain deforestation. These include non-governmental organizations, gross domestic product per capita, economic growth, primary sector economic activity, democracy, total population growth, non-dependent population growth, rural population growth, urban population growth, tropical climate, and natural forest stocks. We conclude with a discussion of the findings, theoretical implications, methodological implications, policy implications, and possible directions for future research.     

Simple correspondence analysis using adjusted residuals

Correspondence analysis is a versatile statistical technique that allows the user to graphically identify the association that may exist between variables of a contingency table. For two categorical variables, the classical approach involves applying singular value decomposition to the Pearson residuals of the table. These residuals allow for one to use a simple test to determine those cells that deviate from what is expected under independence. However, the assumptions concerning these residuals are not always satisfied and so such results can lead to questionable conclusions.One may consider instead, an adjustment of the Pearson residual, which is known to have properties associated with the standard normal distribution. This paper explores the application of these adjusted residuals to correspondence analysis and determines how they impact upon the configuration of points in the graphical display.     

The analysis of dependence for three ways contingency tables with ordinal variables: A case study of patient satisfaction data

For many questionnaires and surveys in the marketing, business, and health disciplines, items often involve ordinal scales (such as the Likert scale and rating scale) that are associated in sometimes complex ways. Techniques such as classical correspondence analysis provide a simple graphical means of describing the nature of the association. However, the procedure does not allow the researcher to specify how one item may be associated with another, nor does the analysis allow for the ordinal structure of the scales to be reflected. This article presents a graphical approach that can help the researcher to study in depth the complex association of the items and reflect the structure of the items. We will demonstrate the applicability of this approach using data collected from a study that involves identifying major factors that influence the level of patient satisfaction in a Neapolitan hospital.