Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.     

Barreras comerciales y empleo en las cadenas mundiales de suministro

A partir de estimaciones de la OIT del empleo en las cadenas mundiales de suministro, se estudia el impacto de las barreras al comercio de bienes y servicios sobre los empleos nacionales. El análisis empírico confirma las predicciones de un modelo teórico calibrado con datos de la WIOD para 2000 y 2011. Las barreras al comercio de bienes y servicios tienen un impacto transfronterizo sobre los empleos del sector directamente afectado y efectos indirectos sobre otros sectores que aumentan con el tiempo. La creciente interconexión entre países y sectores en las cadenas mundiales de suministro implica que las políticas comerciales pueden tener importantes efectos sobre los mercados de trabajo de otros países.     

The Context of Entrepreneurial Judgment: Organizations,Markets, and Institutions

The economics and management literatures pay increasing attention to the technological, competitive, and institutional environment for entrepreneurship. However, less is known about how context influences the judgment of entrepreneurs. Focusing on the emerging judgment‐based approach to entrepreneurship, we argue that economics can say much about how the organizational, market, and institutional context shapes entrepreneurial judgment. We describe entrepreneurs as individuals who deploy scarce, heterogeneous resources to service customer preferences at a profit. Because of uncertainty, this process is essentially experimental, and context influences the experimental process. Thus, entrepreneurs will seek to design the internal organization of the firm so that it facilitates internal experimentation. Moreover, the market or task environment determines the need for experimentation (e.g., how fast do consumer preferences change, how does technology evolve, which assets are available at which terms, etc.). Finally, the institutional environment influences, for example, the transaction costs of acquiring and divesting assets as firms adjust their boundaries through ongoing commercial experimentation.     

Efficient Contests

In their seminal contribution, Lazear and Rosen (1981) show that wages based upon rank induce the same efficient effort as incentive‐based reward schemes. They also show that this equivalence result is not robust toward heterogeneity in worker ability, as long as ability is private information because it is not possible to structure contests to simultaneously satisfy self‐selection constraints and first‐best incentives. This paper demonstrates that efficiency can be achieved by a simple modification of the prize scheme in a mixed (heterogenous) contest where contestants learn their type after entry. If contestants know their type before entering the contest, rent extraction becomes an issue. Implications for optimal contest design are also explored. Finally, the relationship between effort maximizing contests and profit maximizing contests are discussed.     

Location decisions of knowledge-based entrepreneurs: Why some Catalan KISAs choose to be rural?

Entrepreneurship has currently become an important element of economic development and innovation policy. In this context, the promotion of knowledge-based firms has become the norm, even in rural areas. Thus, the study presented in this paper analyses the variables that influence the choice of location made by rural and urban knowledge intensive service activity firms (KISA, hereafter). The results of the quantitative study allows for important policy making recommendations, but also offers significant contributions for entrepreneurship and regional development researchers, as well as practical insights for entrepreneurs.     

Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed‐frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high‐ and low‐frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low‐frequency variables like gross domestic product (GDP) exploiting information in a large set of higher‐frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real‐time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state‐space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed‐frequency techniques that can exploit timely information from business cycle indicators.     

Inconsistency of the MLE and inference based on weighted LS for LARCH models

This paper considers a class of finite-order autoregressive linear ARCH models. The model captures the leverage effect, allows the volatility to be arbitrarily close to zero and to reach its minimum for non-zero innovations, and is appropriate for long memory modeling when infinite orders are allowed. However, the (quasi-)maximum likelihood estimator is, in general, inconsistent. A self-weighted least-squares estimator is proposed and is shown to be asymptotically normal. A score test for conditional homoscedasticity and diagnostic portmanteau tests are developed. Their performance is illustrated via simulation experiments. It is also investigated whether stock market returns exhibit some of the characteristic features of the linear ARCH model.     

Simple Identification and Specification of Cointegrated Varma Models

We bring together some recent advances in the literature on vector autoregressive moving‐average models, creating a simple specification and estimation strategy for the cointegrated case. We show that in this case with fixed initial values there exists a so‐called final moving‐average representation. We prove that the specification strategy is consistent. The performance of the proposed method is investigated via a Monte Carlo study and a forecasting exercise for US interest rates. We find that our method performs well relative to alternative approaches for cointegrated series and methods which do not allow for moving‐average terms. Copyright © 2014 John Wiley & Sons, Ltd.