Abstract We obtain a canonical representation for block matrices. The representation facilitates simple computation of the determinant, the matrix inverse, and other powers of a block matrix, as well as the matrix logarithm and the matrix exponential. These results are particularly useful for block covariance and block correlation matrices, where evaluation of the Gaussian log-likelihood and estimation are greatly simplified. We illustrate this with an empirical application using a large panel of daily asset returns. Moreover, the representation paves new ways to model and regularize large covariance/correlation matrices, test block structures in matrices, and estimate regressions with many variables.

We provide the first direct empirical support for the importance of signalling in monetary policy by testing two key predictions from a novel structural model. First, all policymaker types should become less tough on inflation over time and secondly, types that weigh output more should have a more pronounced shift. Voting data from the Bank of England's Monetary Policy Committee strongly support both predictions. Counterfactual results indicate signalling has a substantial impact on interest rates over the business cycle, and improves the committee designer's welfare. Implications for committee design include allowing regular member turnover and transparency regarding publishing individual votes.

ABSTRACT We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.

We create an analytical structure that reveals the long-run risk-return relationship for nonlinear continuous-time Markov environments. We do so by studying an eigenvalue problem associated with a positive eigenfunction for a conveniently chosen family of valuation operators. The members of this family are indexed by the elapsed time between payoff and valuation dates, and they are necessarily related via a mathematical structure called a semigroup. We represent the semigroup using a positive process with three components: an exponential term constructed from the eigenvalue, a martingale, and a transient eigenfunction term. The eigenvalue encodes the risk adjustment, the martingale alters the probability measure to capture long-run approximation, and the eigenfunction gives the long-run dependence on the Markov state. We discuss sufficient conditions for the existence and uniqueness of the relevant eigenvalue and eigenfunction. By showing how changes in the stochastic growth components of cash flows induce changes in the corresponding eigenvalues and eigenfunctions, we reveal a long-run risk-return trade-off.

Continuous-time Markov processes can be characterized conveniently by their infinitesimal generators. For such processes there exist forward and reverse-time generators. We show how to use these generators to construct moment conditions implied by stationary Markov processes. Generalized method of moments estimators and tests can be constructed using these moment conditions. The resulting econometric methods are designed to be applied to discrete-time data obtained by sampling continuous-time Markov processes.

The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g., education, prices) are often endogenous, making conventional quantile regression inconsistent and hence inappropriate for recovering the causal effects of these variables on the quantiles of economic outcomes. In order to address this problem, we develop a model of quantile treatment effects (QTE) in the presence of endogeneity and obtain conditions for identification of the QTE without functional form assumptions. The principal feature of the model is the imposition of conditions that restrict the evolution of ranks across treatment states. This feature allows us to overcome the endogeneity problem and recover the true QTE through the use of instrumental variables. The proposed model can also be equivalently viewed as a structural simultaneous equation model with nonadditive errors, where QTE can be interpreted as the structural quantile effects (SQE).

Some projects take time to build or are slow to yield cash flows. This may impact the dynamics of investment and liquidity management, although few studies test their financial implications. We exploit the peculiar advantages of copper mines as a laboratory to identify cash-flow sensitivities. In this context, investment decisions depend on the expectations of the long run price of the commodity, while the spread between the spot price and this long run expectations shifts current cash-flows. For this study we compiled a sample of copper firms between 2002 and 2012. We do not find significant effects of cash flow on current capital expenditures, but we do observe a systematic cash flow sensitivity of cash holdings, meaning that some of these transitory earnings are retained as liquidity. This cash stockpiling is stronger among financially constrained firms. In a context of time-to-build, our findings support financial theories emphasizing the salience of cash as buffer stock for liquidity in preparation for future investment opportunities.

Abstract This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis testing procedure. The procedure proposed in the article allows the number of clusters to depend on the data, which gives researchers a principled method for choosing an appropriate clustering level. The article gives conditions under which the proposed procedure asymptotically attains correct size. A simulation study shows that the proposed procedure attains near nominal size in finite samples in a variety of statistical testing problems with dependent data.

This paper investigates empirically a model of aggregate consumption and leisure decisions in which utility from goods and leisure is nontime-separable. The nonseparability of preferences accommodates intertemporal substitution or complementarity of leisure and thereby affects the comovements in aggregate compensation and hours worked. These cross-relations are examined empirically using postwar monthly U. S. data on quantities, real wages, and the real return on the one-month Treasury bill. The estimated values of the parameters governing preferences differ significantly from the values assumed in several studies of real business models. Several possible explanations of these discrepancies are discussed.

The types of knowledge, skills, and proficiencies that should be imparted to students in graduate economics programs are a matter of long-standing controversy. A 1953 American Economic Association (AEA) report cataloged major shortcomings in graduate economic education and recommended changes to enhance the quality and effectiveness of economics Ph.D. programs (Howard R. Bowen, 1953). By the 1980’s, the perception grew that economics Ph.D. programs devoted excessive attention to theoretical work at the expense of real-world application (Wassily Leontief, 1982). This development triggered David Colander and Arjo Klamer (1987) to conduct an independent study based on interviews with graduate students at elite Ph.D. programs. Their study reinforced the views of many, including leaders in the AEA, the National Science Foundation, and several private foundations, that something was amiss. In 1988, the AEA established the Commission on Graduate Education in Economics (COGEE) to undertake a thorough study of graduate training. The Commission’s major concern, based on extensive surveys and interviews of graduate students, faculty, and employers, was that graduate education in economics had removed itself from real-world economic problems (Anne O. Krueger et al., 1991). The COGEE study is distinguished by its attempt to determine the emphasis given to cultivating a set of economic proficiencies in graduate school and the importance of an array of skills for success in graduate school and later on the job (Hansen, 1991). The proficiencies included in the COGEE study were: providing rigorous training in economic theory, providing training in econometrics and measurement, applying theory to real-world problems, using economic theory in empirical applications, and conducting independent economic research. For this study, we added three proficiencies to the COGEE list: understanding economic institutions and history and understanding the history of economic ideas, to capture concerns about curriculum changes that eliminated or scaled back training in these two fields, and developing teaching skills, to reflect recent emphasis on improving the quality of instruction (William E. Becker, 2003). The skills included in the COGEE study were: critical judgment (analyzing ideas, reviewing literature, formulating pertinent comments), analytics (understanding and solving problems, making and analyzing logical arguments), application (seeing practical implications of abstract ideas, analyzing real-world policies and processes), mathematics (constructing and analyzing proofs, manipulating mathematical abstractions), computation (effectively and quickly finding and manipulating relevant data, estimating economic relationships using statistical software), communication (speaking and writing effectively, quickly understanding spoken and written ideas of others, explaining ideas clearly), and creativity (conceiving interesting questions, finding new means of analysis). We added instruction (being an effective classroom teacher) to the skills list for the same reason mentioned above.