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This chapter investigates the performance of different dimension reduction approaches for large vector autoregressions in multi-step ahead forecasts. The authors consider factor augmented VAR models using principal components and partial least squares, random subset regression, random projection, random compression, and estimation via LASSO and Bayesian VAR. The authors compare the accuracy of iterated and direct multi-step point and density forecasts. The comparison is based on macroeconomic and financial variables from the FRED-MD data base. Our findings suggest that random subspace methods and LASSO estimation deliver the most precise forecasts.

Bipartite networks (e.g., software developers linked to open-source projects) are common in settings studied by organization scholars. But the structure underlying bipartite networks tends to be overlooked. Commonly, two modes are reduced to one mode for analysis, causing loss of information. We review techniques for projecting 2-modes onto 1-mode and discuss 2-mode measures of clustering. We also address the potential for 2-mode theory development concerning (a) how change in one mode influences change in the other, (b) the question of two types of agency, and (c) how diversity in one mode is a substitute for diversity in the other mode.

The author develops and extends the asymptotic F- and t-test theory in linear regression models where the regressors could be deterministic trends, unit-root processes, near-unit-root processes, among others. The author considers both the exogenous case where the regressors and the regression error are independent and the endogenous case where they are correlated. In the former case, the author designs a new set of basis functions that are invariant to the parameter estimation uncertainty and uses them to construct a new series long-run variance estimator. The author shows that the F-test version of the Wald statistic and the t-statistic are asymptotically F and t distributed, respectively. In the latter case, the author shows that the asymptotic F and t theory is still possible, but one has to develop it in a pseudo-frequency domain. The F and t approximations are more accurate than the more commonly used chi-squared and normal approximations. The resulting F and t tests are also easy to implement – they can be implemented in exactly the same way as the F and t tests in a classical normal linear regression.

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables. We use a factor model approach on a set of variables available at different frequencies (daily, weekly, monthly, and quarterly) and provide evidence of instability in the primary factors driving the economy. A sequential analysis of the factors allows us to evaluate the model's forecasting performance and extract some instability measures based on the factor model's eigenvalues. Finally, we show how to use COVID-related variables, such as policy, economic, and health indicators, to compute conditional forecasts with factor models, and perform a scenario analysis on the variables of interest to understand economic instability.

In this chapter we investigate the ownership and control of UK firms using contemporary methods from computational graph theory. Specifically, we analyze a ‘small-world’ model of ownership and control. A small-world is a network whose actors are linked by a short chain of acquaintances (short path lengths), but at the same time have a strongly overlapping circle of friends (high clustering). We simulate a set of corporate worlds using an ensemble of random graphs introduced by Chung and Lu (2002a, 2002b). We find that the corporate governance network structures analyzed here are more clustered (‘clubby’) than would be predicted by the random-graph model. Path lengths, though, are generally not shorter than expected. In addition, we investigate the role of financial institutions: potentially important conduits creating connectivity in corporate networks. We find such institutions give rise to systematically different network topologies.

This paper investigates state auditors’ decisions regarding the isolation or projection of sample misstatements to underlying sample populations. Seventy-eight state auditors completed four treatment cases that incorporate the complete 2×2 manipulation of intentional/unintentional and systematic/non-systematic misstatements in different case scenarios, enabling a test of the independent variables both across and within case scenarios.

The results indicate that both across and within case scenarios, auditors tend to project systematic misstatements more often than they project non-systematic misstatements. However, the auditors’ isolation/projection decisions are generally not influenced by whether the sample misstatements are intentional or unintentional.

Purpose – The purpose of this chapter is to project the global emergence of megacities through the 21st century using population scenarios consistent with the Special Report on Emissions Scenarios (SRES) of the Intergovernmental Panel on Climate Change (IPCC).

Methodology – A dynamic urban growth model is developed based on a scale-independent theory of growing networks taking into consideration the geographical and climatic suitability of the location of cities. The model is able to generate a series of megacity projections consistent with an experimental city size distribution based on a national urban population scenario consistent with Zipf's law. The model is applied to population projections for 45,316 cities around the world using three population scenarios from SRES.

Findings – All of the projections indicate that a large number of megacities will be generated in developing regions towards 2100, although the range is wide and depends on the population assumed in the scenarios. Some results indicate an extreme population concentration in megacities; this might be undesirable for national security, quality of life, and sustainable development. Transport policies affect urban growth and national land development through changes in mobility and accessibility across the nation.

Implications – The results presented in this chapter could serve to stimulate discussions on urban and national transport policies and planning, particularly in China.