21.1.1 This is a seasonal model without a trend 21.1.1 The following seasonal dummy model estimates the quarterly growth rate (in percentage terms) of housing starts … The model’s intercept (δ) equals +1.
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.
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.
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 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.
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.
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.