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Apr 3, 2024

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Economics 618B: Time Series Analysis Department of Economics State University of New York at Binghamton Problem Set #1 1. Generate n = 500 random numbers from both the uniform 1 b a ( U [0 , 1] , uniform be- tween zero and one) and exponential λ exp ( λx ) (set λ = 2 and let x U [0 , 1] ) distributions. Plot the histograms of each of the variables. What are the true and estimated means and variances? 2. Generate a random variable with n = 500 from the mixed normal distribution with P [ x N ( 3 , 1)] = P [ x N (3 , 1)] = 0 . 5 . Plot the histogram. Note that this should be a bimodal distribution. Do not add the two variables together. This will lead to a unimodal density with mean near zero. 3. Generate a random variable with n = 500 from the Student-t distribution with 5 degrees of freedom, t 5 . Plot the histogram. 4. Generate a random variable with n = 500 from the χ 2 distribution with 1 degree of freedom. Plot the histogram. 5. Generate a random variable with n = 500 from the Normal distribution with mean 1 and variance 0.5. Plot the histogram. 6. This problem will show you how the central limit theorem works. Using a programming software show how the CLT works for the sample mean with the fi ve separate distrib- utions. Use n = 10 , 50 , and 100 with m = 100 , 200 , and 500 Monte Carlo replications. Plot histograms of the sample means for each distribution and each sample size. (a) Standard normal, N (0 , 1) (b) Uniform, U [0 , 1] (c) Mixed Normal, P [ x N ( 3 , 1)] = P [ x N (3 , 1)] = 0 . 5 (d) Exponential, λ exp ( λx ) (set λ = 2 and let x U [0 , 1] ) (e) Student- t, t 5 7. This problem is to conduct a Monte Carlo experiment to examine the fi nite sample per- formance of a test in size ( P ( reject H 0 : H 0 is true ) ) and power ( P ( reject H 0 : H 0 is false ) ). (a) Size: Generate a random sample x 1 , x 2 , . . . , x n of size n = 50 , with μ = 0 and σ 2 = 1 from two distributions, (i) normal and (ii) uniform. Pretend you do not know the mean and variance. Then test the null hypothesis that μ = 0 against the alternative that it is not equal to zero. Use Monte Carlo experiments with 500 replications to evaluate the size of the test statistic t. Use the asymptotic critical value at the 5% level (1.96). Repeat with n = 100 and 200 . What do you fi nd? (b) Power: Repeat part (a) with μ = 0 . 1 , 0 . 3 , 0 . 1 , and 0 . 3 . Plot the power function (include μ = 0 in the plot). 1
Economics 618B: Time Series Analysis Department of Economics State University of New York at Binghamton Problem Set #2 1. The fi le entitled SIM_2.XLS contains simulated data sets. The fi rst series, denoted Y1, contains 100 values of a simulated AR(1) process. Use this series to perform the following tasks. (a) Plot the sequence against time. Does the series appear to be stationary? (b) Plot the ACF and PACF. (c) Estimate the AR(1), AR(2), ARMA(1,1), ARMA(1,(1,4)), ARMA(2,1) and ARMA(2,(0,4)) models. (d) Estimate the series as both an AR(2) and ARMA(1,1) process without an inter- cept. (e) Use MSE, AIC and SBC to choose the best model from parts (c) and (d). (f) Obtain the one-step-ahead forecast and one-step-ahead forecast error from model. Which model performs the best? Is this surprising? (g) The second series in SIM_2.XLS, denoted Y2, contains 100 values of a simulated ARMA(1,1) process. Repeat steps (a-f) with the series Y2. (h) The third series in SIM_2.XLS, denoted Y3, contains 100 values of a simulated AR(2) process. Repeat steps (a-f) with the series Y3. 2. The fi le QUARTERLY.XLS contains demand deposits reported by commercial banks ( DDNSA ). The series is not seasonally adjusted. The series is quarterly averages over the period 1960:Q1 to 2002:Q1. Be sure to brie fl y explain the relevance of each step. (a) Plot the DDNSA sequence against time. Does the series appear to be stationary? (b) Plot the ACF and PACF of DDNSA . (c) Create the growth rate series log ( DDNSA t /DDNSA t 1 ) and plot this series against time. Does the series appear to be stationary? (d) Plot the ACF and PACF of log ( DDNSA t /DDNSA t 1 ) . (e) Seasonally di ff erence demand deposits using log ( DDNSA t /DDNSA t 4 ) . Does this series appear to be stationary? (f) Plot the ACF and PACF of log ( DDNSA t /DDNSA t 4 ) 2
Economics 618B: Time Series Analysis Department of Economics State University of New York at Binghamton Problem Set #3 1. The fi le QUARTERLY.XLS contains data on the U.S. Producer Price Index ( PPI ) and M1 ( M 1 NSA ). Here the goal is to model both simultaneously using a VAR. The series are quarterly averages over the period 1960:Q1 to 2002:Q1. (a) Construct the rate of growth of the money supply and the in fl ation rate as mea- sured by the PPI as m t = log ( M 1 NSA t /M 1 NSA t 1 ) π t = log ( PPI t /PPI t 1 ) (b) Plot each series. Is there evidence of seasonality? (c) Estimate the VAR with seasonal dummies D 1 , D 2 , and D 3 where D i = 1 in the i -th quarter of each year and zero otherwise m t = a 1 + δ 11 D 1 + δ 21 D 2 + δ 31 D 3 + a 11 m t 1 + a 21 π t 1 + e 1 t π t = a 2 + δ 12 D 1 + δ 22 D 2 + δ 32 D 3 + a 12 m t 1 + a 22 π t 1 + e 2 t (d) Estimated the VAR model in (b) using 12 lags of each variable and save the residuals. (e) Explain why the estimation in (d) cannot begin earlier than 1963:Q2. (f) Estimated the VAR model in (b) using 8 lags of each variable and save the resid- uals. (g) Construct a likelihood ratio test for the null hypothesis of 8 lags versus 12 lags. (h) Repeat the procedure to see if it is possible to further restrict the system to 4 lags of each variable. (i) Determine whether m t Granger causes π t . (j) Determine whether π t Granger causes m t . (k) Show that each variable Granger causes itself. (l) Some argue that it would be better to estimate the VAR using log-levels of the variables instead of the logarithmic fi rst di ff erences. Estimate an eight-lag model with seasonal using the log-levels of the variables. (m) Do the causality tests using undi ff erenced data di ff er from those using di ff erenced data? 3
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