Velocity of money

The quantity equation is given by M * V = P * Y. My motivation here is: although recent CPI (inflation) numbers are higher (> 5.0%), they aren’t nearly as high as you might expect given the dramatic jump in the quantity of money (I’m just looking at M1 here).

BT Question Set P1-T2-21-1: Non-stationary time series

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.

BT Question Set P1-T2-20-25: Long-horizon forecasts

T2-20-25 20.25.1. Over the prior ten months of the calendar year, below is plotted the monthly growth rate of a new cryptocurrency. Two months ago, the growth rate was + 0.

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.