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Macroeconomics --- Money market. Capital market --- Finance --- Econometric models --- Mathematical models --- Finance - Econometric models --- Finance - Mathematical models

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Finance --- Econometrics --- Econometric models --- Quantitative methods (economics) --- Econometrics. --- Econometric models. --- Finance - Econometric models

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Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data. Features * Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis. * Driven by real, current financial data, not just stale data deposited on some static website. * Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.

Finance --- R (Computer program language) --- Mathematical models --- Econometric models --- Mathematical statistics --- Finance - Mathematical models --- Finance - Econometric models

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Stock price forecasting - Mathematical models --- Finance - Econometric models --- Autoregression (statistics) --- Stock price forecasting --- Finance --- Modèles économétriques. --- Autoregression (Statistics) --- Autorégression (statistique) --- Mathematical models. --- Econometric models.

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This paper provides an overview of statistical measurement issues relating to alternative measures of core inflation, and the criteria for choosing among them. The approaches to measurement considered include exclusion-based methods, imputation methods, limited influence estimators, reweighting, and economic modeling. Criteria for judging which approach to use include credibility, control, deviations from a smoothed reference series, volatility, predictive ability, causality and cointegration tests, and correlation with money supply. Country practice can differ in how the approaches are implemented and how their appropriateness is assessed. There is little consistency in the results of country studies to readily suggest guidelines on accepted methods.

Electronic books. -- local. --- Finance -- Econometric models. --- Inflation (Finance) -- Econometric models. --- Finance --- Business & Economics --- Money --- Inflation (Finance) --- Econometric models. --- Inflation --- Macroeconomics --- Money and Monetary Policy --- Price Level --- Deflation --- Monetary Policy --- Monetary economics --- Consumer price indexes --- Inflation targeting --- Price indexes --- Prices --- Monetary policy --- United Kingdom