Lecture introductory

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  • Institute for Theoretical Physics University of California Santa Barbara, CA 93106 carroll@itp.ucsb.edu December 1997 Abstract These notes represent approximately one semester’s worth of lectures on introductory general relativity for beginning graduate students in physics. Topics include manifolds, Riemannian geometry, Einstein’s equations, and three applications: gravitational radiation, black holes, and cosmology. Individual chapters, and potentially updated versions, can be found at http://itp.ucsb.edu/~carroll/notes/. NSF-ITP/97-147 gr-qc/9712019 .i Table of Contents 0.

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  • Chapter 1 - Introduction. This chapter sets the scene for the book by discussing in broad terms the questions of what is econometrics, and what are the ‘stylised facts’ describing financial data that researchers in this area typically try to capture in their models. It also collects together a number of preliminary issues relating to the construction of econometric models in finance.

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  • Chapter 6 - Univariate time series modelling and forecasting. In this chapter, you will learn how to: Explain the defining characteristics of various types of stochastic processes, identify the appropriate time series model for a given data series, produce forecasts for ARMA and exponential smoothing models, evaluate the accuracy of predictions using various metrics, estimate time series models and produce forecasts from them in EViews.

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  • Chapter 2 - Mathematical and statistical foundations. This chapter presents the following content: Straight lines, plot of hours studied against mark obtained, quadratic functions, the roots of quadratic functions, calculating the roots of quadratics, manipulating powers and their indices, logarithms, how do logs work?,...and other contents.

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  • Chapter 3 - A brief overview of the classical linear regression model. In this chapter, you will learn how to: Derive the OLS formulae for estimating parameters and their standard errors, explain the desirable properties that a good estimator should have, discuss the factors that affect the sizes of standard errors, test hypotheses using the test of significance and confidence interval approaches, interpret p-values, estimate regression models and test single hypotheses in EViews.

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  • Chapter 4 - Further development and analysis of the classical linear regression model. In this chapter, you will learn how to: Construct models with more than one explanatory variable, test multiple hypotheses using an F-test, determine how well a model fits the data, form a restricted regression, derive the OLS parameter and standard error estimators using matrix algebra, estimate multiple regression models and test multiple hypotheses in EViews.

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  • Chapter 5 - Classical linear regression model assumptions and diagnostics. In this chapter, students will be able to understand: Describe the steps involved in testing regression residuals for heteroscedasticity and autocorrelation, explain the impact of heteroscedasticity or autocorrelation on the optimality of OLS parameter and standard error estimation, distinguish between the Durbin--Watson and Breusch--Godfrey tests for autocorrelation,...

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  • In this chapter, you will learn how to: Compare and contrast single equation and systems-based approaches to building models; discuss the cause, consequence and solution to simultaneous equations bias; derive the reduced form equations from a structural model; describe several methods for estimating simultaneous equations models; explain the relative advantages and disadvantages of VAR modelling;...

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  • In this chapter, students will be able to understand: Highlight the problems that may occur if non-stationary data are used in their levels form, test for unit roots, examine whether systems of variables are cointegrated, estimate error correction and vector error correction models, explain the intuition behind Johansen’s test for cointegration,...

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  • Chapter 9 - Modelling volatility and correlation. In this chapter, you will learn how to: Discuss the features of data that motivate the use of GARCH models, explain how conditional volatility models are estimated, test for ‘ARCH-effects’ in time series data, produce forecasts from GARCH models, contrast various models from the GARCH family,...

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  • The objectives of this chapter are to switching models. In this chapter, you will learn how to: Use intercept and slope dummy variables to allow for seasonal behaviour in time series, motivate the use of regime switching models in financial econometrics, specify and explain the logic behind Markov switching models,...

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  • In this chapter, you will learn how to: Design simulation frameworks to solve a variety of problems in finance, explain the difference between pure simulation and bootstrapping, describe the various techniques available for reducing Monte Carlo sampling variability, implement a simulation analysis in EViews.

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  • Chapter 14 - Conducting empirical research or doing a project or dissertation in finance. In this chapter, you will learn how to: Choose a suitable topic for an empirical research project in finance, draft a research proposal, find appropriate sources of literature and data, determine a sensible structure for the dissertation.

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  • In this chapter, you will learn how to: Describe the key features of panel data and outline the advantages and disadvantages of working with panels rather than other structures; explain the intuition behind seemingly unrelated regressions and propose examples of where they may be usefully employed; contrast the fixed effect and random effect approaches to panel model specification, determining which is the more appropriate in particular cases; construct and estimate panel models in EViews.

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  • Chapter 12 - Limited dependent variable models. In this chapter, you will learn how to: Compare between different types of limited dependent variables and select the appropriate model, interpret and evaluate logit and probit models, distinguish between the binomial and multinomial cases, deal appropriately with censored and truncated dependent variables, estimate limited dependent variable models using maximum likelihood in EViews.

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  • This introductory chapter reviewed the basic operations of domestic and foreign financial markets and institutions. It described the ways in which funds flow through an economic system from lenders to borrowers and outlined the markets and instruments that lenders and borrowers employ to complete this process.

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  • What are human languages, such that they can be acquired and used as they are? This class surveys some of the most important and recent approaches to this question, breaking the problem up along traditional lines. In spoken languages, what are the basic speech sounds? How are these sounds articulated and combined? What are the basic units of meaning? How are the basic units of meaning combined into complex phrases? How are these complexes interpreted? These questions are surprisingly hard! This introductory survey can only briefly touch on each one....

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  • This is the third volume of the Paris-Princeton Lectures in Mathematical Finance. The goal of this series is to publish cutting edge research in self-contained articles prepared by well known leaders in the field or promising young researchers invited by the editors. Particular attention is paid to the quality of the exposition, and the aim is at articles that can serve as an introductory reference for research in the field. The series is a result of frequent exchanges between researchers in finance and financial mathematics in Paris and Princeton.

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  • This is the first volume of the Paris-Princeton Lectures in Financial Mathematics. The goal of this series is to publish cutting edge research in self-contained articles prepared by well known leaders in the field, or promising young researchers invited by the editors to contribute to a volume. Particular attention is paid to the quality of the exposition and we aim at articles that can serve as an introductory reference for research in the field. The series is a result of frequent exchanges between researchers in finance and financial mathematics in Paris and Princeton.

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  • Tpreface his book is designed for a two-semester sequence in computer science, beginning with what is typically known as Data Structures and continuing with advanced data structures and algorithm analysis. It is appropriate for the courses from both the two-course and three-course sequences in “B.1 Introductory Tracks,” as outlined in the final report of the Computing Curricula 2001 project (CC2001)—a joint undertaking of the ACM and the IEEE. The content of the Data Structures course has been evolving for some time.

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