Whither PQL?

Generalized linear mixed models (GLMM) are generalized linear models with normally distributed random effects in the linear predictor. Penalized quasi-likelihood (PQL), an approximate method of inference in GLMMs, involves repeated fitting of linear mixed models with “working” dependent variables and iterative weights that depend on parameter estimates from the previous cycle of iteration. The generality of PQL, and its implementation in commercially available software, has encouraged the application of GLMMs in many scientific fields. Caution is needed, however, since PQL may sometimes yield badly biased estimates of variance components, especially with binary outcomes. Recent developments in numerical integration, including adaptive Gaussian quadrature, higher order Laplace expansions, stochastic integration and Markov chain Monte Carlo (MCMC) algorithms, provide attractive alternatives to PQL for approximate likelihood inference in GLMMs. Analyses of some well known datasets, and simulations based on these analyses, suggest that PQL still performs remarkably well in comparison with more elaborate procedures in many practical situations. Adaptive Gaussian quadrature is a viable alternative for nested designs where the numerical integration is limited to a small number of dimensions. Higher order Laplace approximations hold the promise of accurate inference more generally. MCMC is likely the method of choice for the most complex problems that involve high dimensional integrals

Whither Corruption? A Quantitative Survey of the Literature on Corruption and Growth

Does corruption grease or sand the wheels of economic growth? This paper uses meta-analysis techniques to systematically evaluate the evidence addressing this question. It uses a data set comprising 460 estimates of the effect of corruption on growth from 41 empirical studies. The main factors explaining the variation in these estimates are whether the model accounts for institutions and trade openness (both are found to deflate the negative effect of corruption), authors' affiliation (academics systematically report less negative impacts), and use of fixed-effects. We also find that publication bias, albeit somewhat acute, does not eliminate the genuine negative effect of corruption on economic growth.corruption, economic growth, meta-regression analysis

Long-run Determinants of Sovereign Yields

We study sovereign bond yields in OECD countries with a dynamic panel by checking for cross-section dependence; assessing panel cointegration; and estimating panel error-correction models. The results show that markets consider budgetary and external imbalances and inflation as relevant determinants of sovereign yields.long-term yields, panel cointegration, bootstrap

Whither Criminology?: On The State of Criminology\u27s Paradigm

What is the condition of criminology’s paradigm? The reply to this question has implications bearing on the profession’s bona fides as a science as well as its sustainability as an independent academic enterprise. The work attempts to capture the elusive term through the use of five themes: theoretical consensus, methodological consensus, boundaries, the departure from sociology, and the current and future status of the field. In approaching this question the work presents an analysis of both qualitative and quantitative data. Semi-structured interviews were conducted with seventeen renowned criminologists. The centerpiece of the latter data set was assembled and analyzed in prior research (Savelsberg et al. 2002). A content analysis of 2,109 peer reviewed articles appearing in the field’s top journals from 1951 to 2008 produced numerous findings. Criminology lacks a hegemonic theoretical orientation but a consensus is evident in the peer-reviewed publication data in terms of its methodology. The field defends its prerogative to draw from any tradition it sees fit to. A review of the content of the field’s research and the debates discussed with the interviewees suggests a somewhat amorphous, yet still discernible, definition of the field’s identity, one that is dedicated to the process of science. This can be seen in terms of the parameters of the seminal theoretical and empirical debates recounted by the interviewees. What is clear is that the field has successfully emancipated itself from the discipline of sociology both professionally and in terms of its content. Concerns were offered in terms of potential threats to the continued growth of the profession resulting from a reduction in funding and its becoming fractured and isolated organizationally but there are reasons for optimism in terms of the expansion of its research horizons into exploring state crime, overcoming the macro/micro divide and incorporating biological, international, cultural/anthropological, and power oriented themes. Discussion of the prospects for how the current work may come to inform a large scale research agenda conclude the work

A New Approach to Detect Spurious Regressions using Wavelets

In this paper, we propose the use of wavelet covariance and correlation to detect spurious regression. Based on Monte Carlo simulation results and experiments with real exchange rate data, it is shown that the wavelet approach is able to detect spurious relationship in a bivariate time series more directly. Using the wavelet approach, it is sufficient to detect a spurious regression between bivariate time series if the wavelet covariance and correlation for the two series are significantly equal to zero. The wavelet approach does not rely on restrictive assumptions which are critical to the Durbin Watson test. Another distinct advantage of the graphical wavelet analysis of wavelet covariance and correlation to detect spurious regression is the simplicity and efficiency of the decision rule compared to the complicated Durbin-Watson decision rules.Wavelet analysis, spurious regression

New Advances and Contributions to Forestry Research

New Advances and Contributions to Forestry Research consists of 14 chapters divided into three sections and is authored by 48 researchers from 16 countries and all five continents. Section Whither the Use of Forest Resources, authored by 16 researchers, describes negative and positive practices in forestry. Forest is a complex habitat for man, animals, insects and micro-organisms and their activities may impact positively or negatively on the forest. This complex relationship is explained in the section Forest and Organisms Interactions, consisting of contributions made by six researchers. Development of tree plantations has been man’s response to forest degradation and deforestation caused by human, animals and natural disasters. Plantations of beech, spruce, Eucalyptus and other species are described in the last section, Amelioration of Dwindling Forest Resources Through Plantation Development, a section consisting of five papers authored by 20 researchers. New Advances and Contributions to Forestry Research will appeal to forest scientists, researchers and allied professionals. It will be of interest to those who care about forest and who subscribe to the adage that the last tree dies with the last man on our planet. I recommend it to you; enjoy reading it, save the forest and save life

Top ranking economics journals impact variability and a ranking update to the year 2002

In this paper I address four questions concerning the quality of scientific economic papers. First, I validate the ex-ante procedure of computing the average impact of economic papers by comparing its results with the expost values. Second, I calibrate an estimator of papers normalised impact. Third, I compute the ranking variability of journals using a bootstrap procedure. Fourth, I test the statistical hypothesis that journals’ ranking did not changed over the time interval between 1980 and 2000. I concluded that this hypothesis is rejected only for the ‘Quarterly Journal of Economics’ and ‘Econometrica’, which saw their citation impact improved.

Reconciling workless measures at the individual and household level: theory and evidence from the United States, Britain, Germany, Spain and Australia

Individual and household based aggregate measures of worklessness can, and do, offer conflicting signals about labour market performance. We outline a means of quantifying the extent of any disparity, (polarisation), in the signals stemming from individual and household-based measures of worklessness and apply this index to data from 5 countries over 25 years. Built around a comparison of the actual household workless rate with that which would occur if employment were randomly distributed over household occupants, we show that in all the countries we examine, there has been a growing disparity between the individual and household based workless measures. The polarisation count can be decomposed to identify which household groups are exposed to workless concentrations and can also be used to test which individual characteristics account for any excess worklessness among these household groups. We show that the incidence and magnitude of polarisation varies widely across countries, but that in all countries polarisation has increased. For each country most of the discrepancies between the individual and household workless counts stem from within-household factors, rather than from changing household composition

The Distortionary Effects Of Temporal Aggregation On Granger Causality

Economists often have to use temporally aggregated data in causality tests. A number of theoretical studies have pointed out that temporal aggregation has distorting effects on causal inference. This paper provides a quantitative assessment of the magnitude of the distortions created by temporal aggregation by plugging in theoretical cross covariances into the limiting values of least squares estimates. Some Monte Carlo results and an application are provided to assess the impact in small samples. It is observed that in general the most distorting causal inferences are likely at low levels of temporal aggregation. At high levels of aggregation, causal information concentrates in contemporaneous correlations. At present, a data-based approach is not available to establish the direction of causality between contemporaneously correlated variables.