AD 685 Project – Fall 2020
Instructions:
This project consist of two parts:
Data Description:
Documentation for Stock_Returns_1931_2002
This file contains 2 monthly data series over the 1931:1-2002:12 sample period.
The data were supplied by Professor Motohiro Yogo of the University of Pennsylvania and were used in his paper with John Campbell:
(Double click in the window below to access the data)
Some Background
exreturn: is the excess return on a broad-based index of stock prices, called the CRSP value-weighted index, using monthly data from 1960:M1 to 2002:M12, where “M1” denotes the first month of the year (January), “M2” denotes the second month, and so forth.
Calculating k-period stock returns:
One-period holding return:
Two-period holding return:
Other way
Three-period’s returns:
k-period’s returns:
When to apply a “buy and hold” strategy:
Note: In all your calculations use Huber-White heteroskedasticity consistent standard errors and covariance.
AR(1) Model
AR(2) Model
AR(4) Model
Autoregressive Models of Monthly Excess Stock Returns, 1960:M1–2002:M12 | |||||
Dependent variable: Excess returns on the CRSP value-weighted index | |||||
(1) | (2) | (3) | |||
Specification | AR(1) | AR(2) | AR(4) | ||
Regressors | |||||
Excess Ret(t-1) | |||||
Std. Error | |||||
p-value | |||||
Excess Ret(t-2) | |||||
Std. Error | |||||
p-value | |||||
Excess Ret(t-3) | |||||
Std. Error | |||||
p-value | |||||
Excess Ret(t-4) | |||||
Std. Error | |||||
p-value | |||||
Intercept | |||||
Std. Error | |||||
p-value | |||||
Adj R^2 | |||||
Wald F-statistic | |||||
p-value | |||||
T= |
Autoregressive Distributed Lag Models of Monthly Excess Stock Returns, 1960:M1–2002:M12 | |||||
Dependent variable: Excess returns on the CRSP value-weighted index | |||||
(1) | (2) | (3) | |||
Specification | ADL(1,1) | ADL(2,2) | ADL(1,1) | ||
Eatimation Period | 1960:M1–2002:M12 | 1960:M1–2002:M12 | 1960:M1–1992:M12 | ||
Regressors | |||||
Excess Ret(t-1) | |||||
Std. Error | |||||
p-value | |||||
Excess Ret(t-2) | |||||
Std. Error | |||||
p-value | |||||
Change_ln_DP(t-1) | |||||
Std. Error | |||||
p-value | |||||
Change_ln_DP(t-2) | |||||
Std. Error | |||||
p-value | |||||
ln_DP(t-1) | |||||
Std. Error | |||||
p-value | |||||
Intercept | |||||
Std. Error | |||||
p-value | |||||
Adj R^2 | |||||
F-statistic | |||||
p-value | |||||
Obs = |
ADL(1,1) specification:
Constant Forecast: (in which the recursively estimated forecasting model includes only an intercept)
Zero Forecast: the sample RMSFEs of always forecasting excess returns to be zero.
Model | RMSFE |
Zero Forecast | |
Constant Forecast | |
ADL(1, 1) |
Part 2
Forecasting models for the rate of inflation – Guidelines
Go to FRED’s website (https://fred.stlouisfed.org/) and download the data for:
In this hands-on exercise you will construct forecasting models for the rate of inflation, based on CPIAUCSL.
For this analysis, use the sample period 1970:M01–2012:M12 (where data before 1970 should be used, as necessary, as initial values for lags in regressions).
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