Economics Research Works

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    Uniform Inference in Panel Autoregression
    (MDPI, 2019-11-26) Chao, John C.; Phillips, Peter C. B.
    This paper considers estimation and inference concerning the autoregressive coefficient (𝜌) in a panel autoregression for which the degree of persistence in the time dimension is unknown. Our main objective is to construct confidence intervals for 𝜌 that are asymptotically valid, having asymptotic coverage probability at least that of the nominal level uniformly over the parameter space. The starting point for our confidence procedure is the estimating equation of the Anderson–Hsiao (AH) IV procedure. It is well known that the AH IV estimation suffers from weak instrumentation when 𝜌 is near unity. But it is not so well known that AH IV estimation is still consistent when 𝜌=1. In fact, the AH estimating equation is very well-centered and is an unbiased estimating equation in the sense of Durbin (1960), a feature that is especially useful in confidence interval construction. We show that a properly normalized statistic based on the AH estimating equation, which we call the 𝕄 statistic, is uniformly convergent and can be inverted to obtain asymptotically valid interval estimates. To further improve the informativeness of our confidence procedure in the unit root and near unit root regions and to alleviate the problem that the AH procedure has greater variation in these regions, we use information from unit root pretesting to select among alternative confidence intervals. Two sequential tests are used to assess how close 𝜌 is to unity, and different intervals are applied depending on whether the test results indicate 𝜌 to be near or far away from unity. When 𝜌 is relatively close to unity, our procedure activates intervals whose width shrinks to zero at a faster rate than that of the confidence interval based on the 𝕄 statistic. Only when both of our unit root tests reject the null hypothesis does our procedure turn to the 𝕄 statistic interval, whose width has the optimal 𝑁−1/2𝑇−1/2 rate of shrinkage when the underlying process is stable. Our asymptotic analysis shows this pretest-based confidence procedure to have coverage probability that is at least the nominal level in large samples uniformly over the parameter space. Simulations confirm that the proposed interval estimation methods perform well in finite samples and are easy to implement in practice. A supplement to the paper provides an extensive set of new results on the asymptotic behavior of panel IV estimators in weak instrument settings.
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    Path-Independent Consideration
    (MDPI, 2021-03-02) Lleras, Juan; Masatlioglu, Yusufcan; Nakajima, Daisuke; Ozbay, Erkut
    In the context of choice with limited consideration, where the decision-maker may not pay attention to all available options, the consideration function of a decision maker is path-independent if her choice cannot be manipulated by the presentation of the choice set. This paper characterizes a model of choice with limited consideration with path independence, which is equivalent to a consideration function that satisfies both the attention filter and consideration filter properties from Masatlioglu et al. (2012) and Lleras et al. (2017), respectively. Despite the equivalence of path-independent consideration with the consideration structures from these two papers, we show that, to have a choice with limited consideration that is path-independent, satisfying both axioms on the choice function that characterize choice limited consideration with attention and consideration filters unilaterally (from Masatlioglu et al. (2012) and Lleras et al. (2017)) is necessary but not sufficient.
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    M&A and technological expansion
    (Wiley, 2023-06-13) Jin, Ginger Zhe; Leccese, Mario; Wagman, Liad
    We examine how public firms listed in North American stock exchanges acquire technology companies during 2010–2020. Combining data from Standard and Poor's (S&P), Refinitiv, Compustat, and Center for Research in Security Prices, and utilizing a unique S&P taxonomy that classifies tech mergers and acquisitions (M&As) by tech categories and business verticals, we show that 13.1% of public firms engage in any tech M&A in the S&P data, while only 6.75% of public firms make any (tech or nontech) M&A in Refinitiv. In both data sets, the acquisitions are widespread across sectors of the economy, but tech acquirers in the S&P data are on average younger, more investment efficient, and more likely to engage in international acquisitions than general acquirers in Refinitiv. Within the S&P data, deals in each M&A-active tech category tend to be led by acquirers from a specific sector; the majority of target companies in tech M&As fall outside the acquirer's core area of business; and firms are, in part, driven to acquire tech companies because they face increased competition in their core areas.
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    Using Topic-Modeling in Legal History, with an Application to Pre-Industrial English Case Law on Finance
    (Cambridge University Press, 2022-06-20) Grajzl, Peter; Murrell, Peter
    We argue that topic-modeling, an unsupervised machine-learning technique for analysis of large corpora, can be a powerful tool for legal-historical research. We provide a non-technical introduction to topic-modeling driven by the presentation of an example of how researchers can use the data that topic-modeling produces. The context of the example is pre-industrial English caselaw on finance. We generate new insights on the timing of pertinent legal developments, the linkages of law on finance to other areas of law, and the relative importance of common-law and equity in the emergence of law and legal ideas relevant to finance. We argue that topic-modeling has the potential to bridge traditional legal history and economics, increasing the influence of the former on the latter, which is overdue. The output of topic-modeling includes the data required to generate a quantitative macroscopic overview of the flow of legal history. These data can be used in many ways in subsequent legal-historical research. Epistemologically, topic-modeling offers an escape from the temptations of Whig history and opens up new avenues for inductive analysis characteristic of traditional historical research.
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    (Cambridge University Press, 2022-03-28) Drukker, David M.; Egger, Peter H.; Prucha, Ingmar R.
    This paper develops an estimation methodology for network data generated from a system of simultaneous equations, which allows for network interdependencies via spatial lags in the endogenous and exogenous variables, as well as in the disturbances. By allowing for higher-order spatial lags, our specification provides important flexibility in modeling network interactions. The estimation methodology builds, among others, on the two-step generalized method of moments estimation approach introduced in Kelejian and Prucha (1998, Journal of Real Estate Finance and Economics 17, 99–121; 1999, International Economic Review 40, 509–533; 2004, Journal of Econometrics 118, 27–50). The paper considers limited and full information estimators, and one- and two-step estimators, and establishes their asymptotic properties. In contrast to some of the earlier two-step estimation literature, our asymptotic results facilitate joint tests for the absence of all forms of network spillovers.