Abstract We propose a simple and reproducible methodology to create a single equation forecasting model (SEFM) for low-frequency macroeconomic variables. Our methodology is illustrated by forecasting annual real GDP growth rates for 52 African countries, where the data are obtained from the World Bank and start in 1960. The models include lagged growth rates of other countries, as well as a cointegration relationship to capture potential common stochastic trends. With a few selection steps ,our methodology quickly arrives at a reasonably small forecasting model per country. Compared with benchmark models, the single equation forecasting models seem to perform quite well.

Abstract The age–period–cohort problem has been studied for decades but without resolution.There have been many suggested solutions to make the three effects estimable, but these solutions mostly exploit non-linear specifications. Yet, these approaches may suffer from misspecification or omitted variable bias. This paper is a practical-oriented study with an aim to empirically disentangle age–period–cohort effects by providing external information on the actual depreciation of housing structure rather than taking age as a proxy. It is based on appraisals of the improvement values of properties in New Zealand to estimate the age-depreciation effect. This research method provides a novel means of solving the identification problem of the age, period, and cohort trilemma. Based on about half a million housing transactions from 1990 to 2019 in the Auckland Region of New Zealand, the results show that traditional hedonic prices models using age and time dummy variables can result, ceteris paribus, in unreasonable positive depreciation rates. The use of the improvement values model can help improve the accuracy of home value assessment and reduce estimation biases. This method also has important practical implications for property valuations.

Abstract In this paper, we introduce a flexible and widely applicable nonparametric entropy-based testing procedure that can be used to assess the validity of simple hypotheses about a specific parametric population distribution. The testing methodology relies on the characteristic function of the population probability distribution being tested and is attractive in that, regardless of the null hypothesis being tested, it provides a unified framework for conducting such tests. The testing procedure is also computationally tractable and relatively straightforward to implement. In contrast to some alternative test statistics, the proposed entropy test is free from user-specified kernel and bandwidth choices, idiosyncratic and complex regularity conditions, and/or choices of evaluation grids. Several simulation exercises were performed to document the empirical performance of our proposed test, including a regression example that is illustrative of how, in some contexts, the approach can be applied to composite hypothesis-testing situations via data transformations. Overall, the testing procedure exhibits notable promise, exhibiting appreciable increasing power as sample size increases for a number of alternative distributions when contrasted with hypothesized null distributions. Possible general extensions of the approach to composite hypothesis-testing contexts, and directions for future work are also discussed.

Abstract In linear regression analysis, the estimator of the variance of the estimator of the regression coefficients should take into account the clustered nature of the data, if present, since using the standard textbook formula will in that case lead to a severe downward bias in the standard errors. This idea of a cluster-robust variance estimator (CRVE) generalizes to clusters the classical heteroskedasticity-robust estimator. Its justification is asymptotic in the number of clusters. Although an improvement, a considerable bias could remain when the number of clusters is low, the more so when regressors are correlated within cluster. In order to address these issues, two improved methods were proposed; one method, which we call CR2VE, was based on biased reduced linearization, while the other, CR3VE, can be seen as a jackknife estimator. The latter is unbiased under very strict conditions, in particular equal cluster size. To relax this condition, we introduce in this paper CR3VE-λ, a generalization of CR3VE where the cluster size is allowed to vary freely between clusters. We illustrate the performance of CR3VE-λ through simulations and we show that, especially when cluster sizes vary widely, it can outperform the other commonly used estimators.