Structural vs. atheoretic approaches to econometrics

In this paper I attempt to lay out the sources of conflict between the so-called “structural” and “experimentalist” camps in econometrics. Critics of the structural approach often assert that it produces results that rely on too many assumptions to be credible, and that the experimentalist approach provides an alternative that relies on fewer assumptions. Here, I argue that this is a false dichotomy. All econometric work relies heavily on a priori assumptions. The main difference between structural and experimental (or “atheoretic”) approaches is not in the number of assumptions but the extent to which they are made explicit.     

Problematizing socioemotional wealth in family firms: a systems-theoretical reframing

The concept of socioemotional wealth (SEW) seeks to present an independent paradigmatic basis for family-firm research, and in doing so aims to establish a sound basis for the scientific legitimacy of family-firm research. Establishing that legitimacy requires scholars to demonstrate that SEW is based on coherent assumptions on several theoretical levels. This paper uses the problematization methodology to challenge the coherence of the theoretical assumptions underpinning SEW and to advance theory development. The results of this problematization show that SEW is built on a theoretical level close to the object of research (in-house assumptions), but that more deeply-rooted theoretical levels (e.g. paradigmatic assumptions) are not sufficiently elaborated. Moreover, the original conceptualization is based on a positivist-mechanistic view, which hinders SEW reflecting the complex reality of family firms. Based on the results of this problematization, new systems theory is applied to reframe SEW’s theoretical grounding. Thereby the main contribution of the paper is a critical reflection on the theoretical underpinnings of SEW (in particular root-metaphor and paradigmatic assumptions), serving as the basis for advancing a coherent theoretical understanding of this important concept in family business research.     

The primal route to the turnpike and asymptotic stability

Asymptotic turnpike theorems in the case where the future is discounted are traditionally derived under the assumptions that a utility function is twice differentiable and that is Hessian is negative definite. Replacing these assumptions by a weaker one. I give a proof of the theorem simpler than existing ones.     

The role of judgment,assumptions, techniques,and confidence limits in forecasting population

This paper discusses the fundamental role judgment and assumptions play in forecasting population. It is suggested that so-called “projections” operationally are usually either forecasts or extrapolations. Specific projection methodologies and techniques are shown to embody assumptions. A simple typology of such assumptions is presented as a guide to evaluate forecasts. Tests of projection technique accuracy are cited and it is concluded that such tests cannot succeed due to the assumption factor. Finally, time series forecasting techniques are criticized because their terminology with respect to confidence limits about a forecast is misleading.     

Bayesian selection of technology assumptions for the transformation from supply-use to input–output tables

ABSTRACT

In the construction of input–output models from supply-use tables, technology assumptions disambiguate how an industry uses inputs in the production recipe of multiple outputs. This paper uses Bayes' theorem to select technology assumptions, taking into account empirical observations. The paper presents a formulation to explore hybrids between product and industry technology assumptions in product-by-product tables. We then present Markov chain Monte-Carlo techniques to implement the Bayesian method for selecting technology assumptions. We apply the method in a case study using Eurostat supply-use tables of 2004 and 2005, exhibiting a volume of secondary products of less than 13%, and 59 products and industries per country. The results show that the choice of technology is not important, given that there is no strong evidence in favour of any of them.     

Alternative Assumptions to Identify LATE in Fuzzy Regression Discontinuity Designs

There exist two alternative assumptions to identify local average treatment effects (LATE) in fuzzy regression discontinuity (RD) designs: local independence (LI) and local smoothness (LS). Together with the usual LATE assumptions requiring existence of a first‐stage and treatment monotonicity, either of these two assumptions is sufficient to identify RD LATE. I discuss the practical (and testable) implications of these alternative assumptions, and show that weakening LI by LS might be empirically relevant. However, when LI does hold, there are some practical implications one may explore. Numerical and empirical examples are briefly presented.     

ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.     

The contribution of the systems approach to planning

This paper attempts a three-part review and critique of the state of the application of systems theory to planning. The first part is a critique of McLoughlin's thesis expounded in his book Urban and Regional Planning. The critique emphasises the mechanistic bias and passivity assumed in McLoughlin's exposition and suggests directions in which the systems approach may be widened and strengthened to include in particular the political process and to include dynamic profiles of variables in the systems being analysed.The second part considers how far Chadwick in his book A Systems View of Planning has developed the approach, and comes to the conclusion that while the dynamics of the model are more rigorously treated the philosophical and social science assumptions, though hinted at, are not pursued to significantly greater lengths than by McLoughlin.The third part argues that while it is necessary to pursue the assumptions of the model, it is still permissible to continue model-building before the assumptions are finally tested and agreed as accurate, so long as accurate prediction on the basis of hypothetical assumptions is not taken in itself to constitute the proof of the accuracy of the assumptions.     

Symmetry-based inference in an instrumental variable setting

We describe exact inference based on group-invariance assumptions that specify various forms of symmetry in the distribution of a disturbance vector in a general nonlinear model. It is shown that such mild assumptions can be equivalently formulated in terms of exact confidence sets for the parameters of the functional form. When applied to the linear model, this exact inference provides a unified approach to a variety of parametric and distribution-free tests. In particular, we consider exact instrumental variable inference, based on symmetry assumptions. The unboundedness of exact confidence sets is related to the power to reject a hypothesis of underidentification. In a multivariate instrumental variables context, generalizations of Anderson–Rubin confidence sets are considered.     

Common inventory modeling assumptions that fall short: Arborescent networks, Poisson demand, and single-echelon approximations

Traditional multi-echelon inventory theory focuses on arborescent supply chains that use a central warehouse which replenishes remote warehouses. The remote warehouses serve customers in their respective regions. Common assumptions in the academic literature include use of the Poisson demand process and instantaneous unit-by-unit replenishment. In the practitioner literature, single-echelon approximations are advised for setting safety stock to deal with lead time, demand, and supply variations in these settings. Using data from a U.S. supplier of home improvement products, we find that neither the assumptions from the academic literature nor the approximations from the practitioner literature necessarily work well in practice.In a variation of the strictly arborescent supply chain, the central warehouse at our real company not only replenishes other warehouses but also meets demand from customers in the region near the central warehouse. In this paper, we study this dual-role central warehouse structure, which we believe is common in practice. Using high and low volume product demand data from this company, we use Monte Carlo simulations to study the impact of (1) the use of a dual-role centralized warehouse, (2) common demand assumptions made in multi-echelon research, and (3) single-echelon approximations for managing a multi-echelon supply chain. We explore each of these under both centralized and decentralized control logic. We find that the common assumptions of theoretical models impede their usefulness and that heuristics that ignore the actual supply chain structure fail to account for additional opportunities to utilize safety stock more effectively. Researchers should be aware of the gap between standard assumptions in traditional literature and actual practice, and critically evaluate their assumptions to find a reasonable balance between tractability and relevance.     

A generalized focused information criterion for GMM

This paper proposes a criterion for simultaneous generalized method of moments model and moment selection: the generalized focused information criterion (GFIC). Rather than attempting to identify the “true” specification, the GFIC chooses from a set of potentially misspecified moment conditions and parameter restrictions to minimize the mean squared error (MSE) of a user‐specified target parameter. The intent of the GFIC is to formalize a situation common in applied practice. An applied researcher begins with a set of fairly weak “baseline” assumptions, assumed to be correct, and must decide whether to impose any of a number of stronger, more controversial “suspect” assumptions that yield parameter restrictions, additional moment conditions, or both. Provided that the baseline assumptions identify the model, we show how to construct an asymptotically unbiased estimator of the asymptotic MSE to select over these suspect assumptions: the GFIC. We go on to provide results for postselection inference and model averaging that can be applied both to the GFIC and various alternative selection criteria. To illustrate how our criterion can be used in practice, we specialize the GFIC to the problem of selecting over exogeneity assumptions and lag lengths in a dynamic panel model, and show that it performs well in simulations. We conclude by applying the GFIC to a dynamic panel data model for the price elasticity of cigarette demand.     

关于我国政府会计假设与前提的思考

从计划经济时期起至今,我国政府会计并不重视会计假设与前提。这其中的原因与我国政府产权关系、财政类型及宪政水平有着密切的关联。我国政府产权关系的变迁、公共财政制度改革及宪政水平的提高引发了人们对政府会计假设与前提的关注。我国政府会计应引入规范性的假设与前提,并从政府会计制度安排上充分重视会计假设与前提。     

Profile Likelihood and Incomplete Data

According to the law of likelihood, statistical evidence is represented by likelihood functions and its strength measured by likelihood ratios. This point of view has led to a likelihood paradigm for interpreting statistical evidence, which carefully distinguishes evidence about a parameter from error probabilities and personal belief. Like other paradigms of statistics, the likelihood paradigm faces challenges when data are observed incompletely, due to non-response or censoring, for instance. Standard methods to generate likelihood functions in such circumstances generally require assumptions about the mechanism that governs the incomplete observation of data, assumptions that usually rely on external information and cannot be validated with the observed data. Without reliable external information, the use of untestable assumptions driven by convenience could potentially compromise the interpretability of the resulting likelihood as an objective representation of the observed evidence. This paper proposes a profile likelihood approach for representing and interpreting statistical evidence with incomplete data without imposing untestable assumptions. The proposed approach is based on partial identification and is illustrated with several statistical problems involving missing data or censored data. Numerical examples based on real data are presented to demonstrate the feasibility of the approach.     

General conditions for the existence of maximal elements via the uncovered set

This paper disentangles the topological assumptions of classical results (e.g.,Walker, 1977 on the existence of maximal elements from rationality conditions. It is known from the social choice literature that under the standard topological conditions—with no rationality assumptions on preferences—there is an element such that the upper section of strict preference at that element is minimal in terms of set inclusion, i.e., the uncovered set is nonempty. Assuming the finite subordination property, a condition that weakens known acyclicity and convexity assumptions, each such uncovered alternative is in fact maximal. Implications are a generalization of a result of Yannelis and Prabhakar (1983) on semi-convexity, an extension of Fan’s (1961) lemma on KKM correspondences, and the existence of fixed points for subordinate convex correspondences generalizing the work of Browder (1968).     

Ecological Fallacy and Covariates: New Insights based on Multilevel Modelling of Individual Data

The paper provides a new and more explicit formulation of the assumptions needed by the ordinary ecological regression to provide unbiased estimates and clarifies why violations of these assumptions will affect any method of ecological inference. Empirical evidence is obtained by showing that estimates provided by three main ecological inference methods are heavily biased when compared with multilevel logistic regression applied to a unique set of individual data on voting behaviour. The main findings of our paper have two important implications that can be extended to all situations where the assumptions needed to apply ecological inference are violated in the data: (i) only ecological inference methods that allow one to model the effect of covariates have a chance to produce unbiased estimates, and (ii) there are certain data generating mechanisms producing a kind of bias in ecological estimates that cannot be corrected by modelling the effect of covariates.     

On the theory of optimal government behaviour

A small politico-economic model of the U.S. is presented. Optimal policies for a vote-maximising government and an ideology-maximising government are derived. Different assumptions concerning the expectation formation mechanism and the discount rates of voters and government are employed. Though the structure of the cycle depends on the specific assumptions, it turns out that in most cases it is advantageous for the government to produce a ‘political business cycle’. Therefore it is asked how voters can force government to choose the socially optimal policy.     

Revenue and efficiency ranking in large multi-unit and bundle auctions

Should a seller use a multi-unit auction for identical and indivisible units of a good? We show, under specific assumptions on the value distributions of the bidders, that in large markets the multi-unit format generates higher (lower) expected revenue compared to the bundled format when the supply is relatively scarce (abundant). In contrast, a large market is shown to be always more efficient under the multi-unit format than its bundled counterpart. Thus under these assumptions a profit maximizing seller is expected to choose the relatively efficient multi-unit format when supply is scarce.     

Interpolation and optimal linear prediction

This paper is concerned with the interpolation of spatially distributed observations of a quantitative phenomenon, sometimes referred to as kriging. This activity can be understood as a prediction procedure for values of random functions under stationarity assumptions in a polynomial linear regression context. After a heuristic and an exact derivation of the best linear unbiased prediction procedure (and the variance of prediction error) if the covariance function relating covariance between two possible observations to their mutual distance is known, follows the introduction of weaker assumptions admitting the definition of the variance only for increments of a certain order by a pseudoco–variance function. A particular related case is the so–called semivariogram for increments of order one. The prediction procedure turns out to be similar to that in the previous situation. The weaker assumptions allow an unbiased estimation of the unknown pseudocovahance function of polynomial form under restrictions imposed by Fourier transformation. Extension from point–wise observations or predictions to area or volume averages is touched upon.     

The Logit Model in Economics

This paper reviews aspects of the application of logit models in economics. We consider some economic models that lead to a simple or a multinomial logit specification. A detailed account is given of the possible specifications of the multinomial model. We stress the relationship between distributional assumptions and functional form assumptions. Some (mis)specification tests with individual data are discussed. Moreover, we present a new test. We also consider grouping of continuously recorded data, and the biases introduced by an inappropriate grouping.