David's (bionicturtle) data blog
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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).
Last updated on Oct 28, 2021
2 min read
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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.
Last updated on Apr 5, 2021
2 min read
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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.
Last updated on Oct 27, 2020
5 min read
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BT Question Set P1-T2-20-24-3: AIC and BIC
20.24.3. Eric is a commodity analyst who fit four different candidate AR(p) models to a series of oil prices. For each of the candidate models, he then retrieved the Akaike information criterion (AIC) and the Bayesian information criterion (BIC).
Last updated on Oct 21, 2020
10 min read
BT Question Set P1-T2-20-24-2: Box-Pierce
20.24.2. Elizabeth is an economist tasked with modeling quarterly gross domestic product (GDP) in the United States. She starts with the plots displayed below. The raw data is displayed in the upper; she observes this GDP trend is obviously not stationary (why?
Last updated on Oct 21, 2020
9 min read
BT Question Set P1-T2-20-23 set: ARMA process
Learning objectives Explain mean reversion and calculate a mean-reverting level. Define and describe the properties of autoregressive moving average (ARMA) processes. Describe the application of AR, MA and ARMA processes.
Last updated on Oct 16, 2020
5 min read
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.
Last updated on Oct 8, 2020
2 min read
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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.
Last updated on Sep 30, 2020
3 min read
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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.
Last updated on Sep 30, 2020
1 min read
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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.
Last updated on Sep 25, 2020
3 min read
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