Quantitative Methods II - DrWaleKesh

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Basics of Multiple Regression and Underlying Assumptions 4 Topics

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a. describe the types of investment problems addressed by multiple linear regression and the regression process

b. formulate a multiple linear regression model, describe the relation between the dependent variable and several independent variables, and interpret estimated regression coefficients

c. explain the assumptions underlying a multiple linear regression model and interpret residual plots indicating potential violations of these assumptions

Curriculum End of Chapter Questions

Evaluating Regression Model Fit and Interpreting Model Results 4 Topics

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a. evaluate how well a multiple regression model explains the dependent variable by analyzing ANOVA table results and measures of goodness of fit

b. formulate hypotheses on the significance of two or more coefficients in a multiple regression model and interpret the results of the joint hypothesis tests

c. calculate and interpret a predicted value for the dependent variable, given the estimated regression model and assumed values for the independent variable

Curriculum End of Chapter Questions

Model Misspecification 5 Topics

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a. describe how model misspecification affects the results of a regression analysis and how to avoid common forms of misspecification

b. explain the types of heteroskedasticity and how it affects statistical inference

c. explain serial correlation and how it affects statistical inference

d. explain multicollinearity and how it affects regression analysis

Curriculum End of Chapter Questions

Extensions of Multiple Regression 4 Topics

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a. describe influence analysis and methods of detecting influential data points

b. formulate and interpret a multiple regression model that includes qualitative independent variables

c. formulate and interpret a logistic regression model

Curriculum End of Chapter Questions

Time-Series Analysis 16 Topics

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a. calculate and evaluate the predicted trend value for a time series, modeled as either a linear trend or a log-linear trend, given the estimated trend coefficients

b. describe factors that determine whether a linear or a log-linear trend should be used with a particular time series and evaluate limitations of trend models

c. explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary

d. describe the structure of an autoregressive (AR) model of order p and calculate one- and two-period-ahead forecasts given the estimated coefficients

e. explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series

f. explain mean reversion and calculate a mean-reverting level

g. contrast in-sample and out-of-sample forecasts and compare the forecasting accuracy of different time-series models based on the root mean squared error criterion

h. explain the instability of coefficients of time-series models

i. describe characteristics of random walk processes and contrast them to covariance stationary processes

j. describe implications of unit roots for time-series analysis, explain when unit roots are likely to occur and how to test for them, and demonstrate how a time series with a unit root can be transformed so it can be analyzed with an AR model

k. describe the steps of the unit root test for nonstationarity and explain the relation of the test to autoregressive time-series models

l. explain how to test and correct for seasonality in a time-series model and calculate and interpret a forecasted value using an AR model with a seasonal lag

m. explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series

n. explain how time-series variables should be analyzed for nonstationarity and/or cointegration before use in a linear regression

o. determine an appropriate time-series model to analyze a given investment problem and justify that choice

Curriculum End of Chapter Questions

Machine Learning 6 Topics

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a. describe supervised machine learning, unsupervised machine learning, and deep learning

b. describe overfitting and identify methods of addressing it

c. describe supervised machine learning algorithms—including penalized regression, support vector machine, k-nearest neighbor, classification and regression tree, ensemble learning, and random forest—and determine the problems for which they are best suited

d. describe unsupervised machine learning algorithms—including principal components analysis, k-means clustering, and hierarchical clustering—and determine the problems for which they are best suited

e. describe neural networks, deep learning nets, and reinforcement learning

Curriculum End of Chapter Questions

Big Data Projects 8 Topics

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a. identify and explain steps in a data analysis project

b. describe objectives, steps, and examples of preparing and wrangling data

c. evaluate the fit of a machine learning algorithm

d. describe objectives, methods, and examples of data exploration

e. describe methods for extracting, selecting and engineering features from textual data

f. describe objectives, steps, and techniques in model training

g. describe preparing, wrangling, and exploring text-based data for financial forecasting

Curriculum End of Chapter Questions

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Olawale Keshinro

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Course Includes

7 Lessons
47 Topics

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