R

BT Question Set P1-T2-20-22-2: AR versus MA process

P1.T2.20.22. Stationary Time Series: autoregressive (AR) and moving average (MA) processes Learning objectives Define and describe the properties of autoregressive (AR) processes. Define and describe the properties of moving average (MA) processes.

BT Question Set P1-T2-20-21-3: White Noise (WN) Process

P1-T2-20-21-3: White Noise (WN) Process) 20.21.3. Barbara was delighted to learn that she can easily simulate white noise in R with a single command. She learned that she can use arima.

BT Question Set P1-T2-20-21-2: Autocorrelation function (ACF)

20.21.2. Shown below is the autocorrelation function (ACF) for a time series object that contains the total quarterly beer production in Australia (in megalitres) from 1956:Q1 to 2010:Q2 (source: https://cran.

BT Question Set P1-T2-20-20-2: m-fold cross-validation

m-fold cross-validation Our question P1-T2-20-2 (located at https://www.bionicturtle.com/forum/threads/p1-t2-20-20-regression-diagnostics-outliers-cooks-distance-m-fold-cross-validation-and-residual-diagnostics.23497/) mimics GARP’s approach in their Chapter 9; in particular their solution 9.14. However, it makes the mistake of using cross-validation to select the regression coefficients.

BT Question P1-T2-20-20-3: Regression diagnostic plots

BT Question 20.20.3 20.20.3. Patrick generated a simple regression line for a sample of 50 pairwise observations. After generating the regression model, he ran R’s built-in plot(model) function which produces a standard set of regression diagnostics.

BT Question Set P1-T2-20-19: Regression diagnostics (1st set)

P1.T2.20.19. Regression diagnostics: omitted variables, heteroskedasticity, and multicollinearity Question 1: Fama-french 2-factor <- omitted variable 20.19.1. Jane manages a market-neutral equity fund for her investment management firm. The fund’s market-neutral style implies (we will assume) that the fund’s beta with respect to the market’s excess return is zero.

BT Question Set P1-T2-20-18: Multivariate regressions

Multiple regression Question 1: Fama-french library(tidyverse) library(broom) library(gt) intercept <- .03 intercept_sig <- .01 x1_mu <- .04 x1_sig <- .01 x1_beta <- 0.4 x2_mu <- .03 x2_sig <- .01 x2_beta <- -0.

BT Question Set P1-T2-20-17: Univariate regressions continued (2nd set v2)

Learning objectives Construct, apply, and interpret hypothesis tests and confidence intervals for a single regression coefficient in a regression. Explain the steps needed to perform a hypothesis test in a linear regression.

BT Question P1-T2-20-16-3: Univariate regression: Monthly rental versus footage

20.16.3. Sally works at a real estate firm and was asked by her client to quantify the relationship between rental size (in square feet) and rental price. She explained to her client that the relationship is multivariate but, given that caveat, she offered to perform a linear regression with a single explanatory variable.

BT Question P1-T2-20-16-2: Univariate regression: Portfolio versus benchmark returns

20.16.2. Peter is an analyst who is evaluating an investment fund whose managers claim has outperformed their benchmark. He collected monthly returns for the last five years; i.e., the sample size is excess return pairs over n = 60 months.