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This paper considers the finite-sample distribution of the 2SLS estimator and derives bounds on its exact bias in the presence of weak and/or many instruments. We then contrast the behavior of the exact bias expressions and the asymptotic expansions currently popular in the literature, including a consideration of the no-moment problem exhibited by many Nagar-type estimators. After deriving a finite-sample unbiased k-class estimator, we introduce a double-k-class estimator based on Nagar (1962) that dominates k-class estimators (including 2SLS), especially in the cases of weak and/or many instruments. We demonstrate these properties in Monte Carlo simulations showing that our preferred estimators outperform Fuller (1977) estimators in terms of mean bias and MSE.

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

We put forward the idea that for model selection the intrinsic priors are becoming a center of a cluster of a dominant group of methodologies for objective Bayesian Model Selection.

The intrinsic method and its applications have been developed in the last two decades, and has stimulated closely related methods. The intrinsic methodology can be thought of as the long searched approach for objective Bayesian model selection and hypothesis testing.

In this paper we review the foundations of the intrinsic priors, their general properties, and some of their applications.

Microsimulation is a technique that uses the capacity of modern computers to make microunits act and interact in such a way that it is possible to aggregate to the level of interest. A microsimulation model can be seen as a set of rules, which operates on a sample of microunits such as individuals, households, and firms. Each microunit is defined and characterized by a set of properties (variables) and as the model is simulated these properties are updated for each and every microunit. The model might simply be a set of deterministic rules such as the income tax rules of a country operating on a sample of taxpayers, and used to compute the distribution of after-tax income, the aggregate income tax revenue, or other fiscal entities of interest. But the model could also include behavioral assumptions usually formulated as stochastic models. Examples are fertility models, models for household formation and dissolution, labor supply, and mobility.

African Economies are a mix of emerging and developing economies, characterised by regional imbalances and socio-economic differences. Foreign Direct Investment and Trade has been important for the growth prospects of African Economies. In this paper, we attempt to study the impact of FDI and Trade in a COVID-19 scenario on the African Economies. We also study the lockdown restrictions in different regions of Africa. Applying Neural Network Analysis for the sample of 36 African Economies we identify the significant economic variables for GDP. The analysis based on a feedforward structure suggests that Merchandize Exports (MEXP) and Foreign Direct Investment Stock (FDIS) have very strong causal linkages with the GDP for African Economies sample. On the other hand, Merchandise Imports (MIMP), Services Exports (SEMP), Services Imports (SIMP), and Foreign Direct Investment Inflows (FDII) have a strong and significant relationship with GDP for the African Economies. Tariff Measures (TRFF), Anti-Dumping measures (ADP) and Foreign Direct Investment Outflows (FDIO) have no significant relationship.

Do firms build new capabilities by hiring new people? We explore this question in the context of the pharmaceutical industry’s movement towards science-driven drug discovery. We focus particularly on the potential problem of endogeneity in interpreting correlation between hiring and changes in organizational outcomes as evidence of the impact of new hires on the firm, and on the more fundamental conceptual question of the conditions under which hiring might be a source of competitive advantage, given the well known objection that resources that are freely available through the market cannot be a source of differential capabilities. Using data on the movement and publication of “star” scientists, we find that the adoption of science based drug discovery within the firm is closely correlated with the hiring of star scientists. This correlation appears to be reasonably robust to a number of controls for endogeneity. We also show that the hiring of highly talented scientists appears to have a significant impact on the behavior of scientists already working within the firm. We interpret this as consistent with the idea that hiring may change organizational capabilities through the interaction of new talent with the policies, routines and people already in place within the firm.