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Professionals are constantly searching for competitive solutions to help determine current and future economic tendencies. Econometrics uses statistical methods and real-world data to predict and establish specific trends within business and finance. This analytical method sustains limitless potential, but the necessary research for professionals to understand and implement this approach is lacking. Applied Econometric Analysis: Emerging Research and Opportunities explores the theoretical and practical aspects of detailed econometric theories and applications within economics, political science, public policy, business, and finance. Featuring coverage on a broad range of topics such as cointegration, machine learning, and time series analysis, this book is ideally designed for economists, policymakers, financial analysts, marketers, researchers, academicians, and graduate students seeking research on the various techniques of econometric concepts. Enter your mobile number or email address below and we'll send you a link to download the free Kindle App.

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Strategies That Fit Emerging Markets

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Emerging Hot Spot Analysis (Space Time Pattern Mining)—ArcGIS Pro | Documentation

Fast-growing economies often provide poor soil for profits. The cause? A lack of specialized intermediary firms and regulatory systems on which multinational companies depend. Successful businesses look for those institutional voids and work around them. Many firms simply go with what they know—and fall far short of their goals. These gaps have made it difficult for multinationals to succeed in developing nations; thus, many companies have resisted investing there. That may be a mistake.
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Emerging Hot Spot Analysis (Space Time Pattern Mining)

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Categories include new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating, and historical hot and cold spots. Learn more about how the Emerging Hot Spot Analysis tool works. Bins associated with the same physical location will share the same location ID and together will represent a time series. Bins associated with the same time-step interval will share the same time-step ID and together will comprise a time slice. The count value for each bin reflects the number of incidents or records that occurred at the associated location within the associated time-step interval.
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