Nima Vafai April 22nd, 2017

HW6 Nima Vafai April 22nd, 2017 #Problem 1 Obtain quarterly time series for U.S. real GDP FRED/GDPC96, GDP deflator FRED/GDPDEF and quarterly closing value of S&P 500 Index YAHOO/INDEX_GSPC/CLOSE. Use them to construct the following two time series: y1,t = 400??? log GDPt which approximates the annualized growth rate of the U.S. real GDP, and y2,t = 400(??? log SP500t ??? ??? log p GDP t ) which approximates the inflation adjusted annual return of S&P 500. (a) Estimate a bivariate reduced form VAR for yt = (y1,t, y2,t) 0 for the period 1961Q1-2016Q4, use information criteria to select number of lags. How large is the correlation of residuals in the model, and what are the implications for IRFs and FEVDs based on Choleski decomposition? (b) Run the Granger causality tests for both variables. What do the results suggest about the predictive power of the two variables? Discuss the economic intution behind your results of Granger causality test. (c) Estimate a restricted VAR model in which you remove lags based on Granger causality test from (b). (d) Plot IRFs and FEVD for the VAR model based on Choleski decomposition. Afterwards reverse the order of the variables in the VAR and plot IRFs and FEVD based on Choleski decomposition for the alternative order. Does the order matter much in this particular case? (e) Reestimate your VAR, with a third variable, Leading Index for the United States, FRED/USSLIND. (f) Use the augmented VAR from (e) to create a forecast for the period 2017Q1-2017Q4. Compare your forecast for real GDP growth rate in 2017Q1 with (1) the Federal Bank of New York Nowcast, (2) the GDPNow Federal Bank of Atlanta forecast, and (3) the minimum, the average, and the maximum forecasts in the Wall Street Journal Economic Forecasting Survey. #Method #a) Estimating Bivariate Reduced Form. library(zoo) ## ## Attaching package: 'zoo' ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric library(Quandl) ## Loading required package: xts library(stargazer) ## ## Please cite as: ##

Hlavac, Marek (2015). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##

R package version 5.2. http://CRAN.R-project.org/package=stargazer

library(vars) ## Loading required package: MASS ## Loading required package: strucchange ## Loading required package: sandwich

1

## Loading required package: urca ## Loading required package: lmtest Growth