1. Time Series Model Estimation Materials for lecture 6 Read Chapter 15 pages 30 to 37 Lecture 6 Time Series.XLSX Lecture 6 Vector Autoregression.XLSX

2. Time Series Model Estimation Outline for this lecture –Review stationarity and no. of lags lecture –Discuss model estimation –Demonstrate how to estimate Time Series (AR) models with Simetar –Interpretation of model results –How you forecast the results for an AR model

3. Time Series Model Estimation Plot the data to see what kind of series you are analyzing Make the series stationary by determining the optimal number of differences based on =DF() test, say D i,t Determine the number of lags to use in the AR model based on =AUTOCORR() or =ARLAG() D i,t =a + b 1 D i,t-1 + b 2 D i,t-2 +b 3 D i,t-3 + b 4 D i,t-4 Create all of the data lags and estimate the model using OLS (or use Simetar)

4. Time Series Model Estimation An alternative to estimating the differences and lag variables by hand and using an OLS regression package, use Simetar Simetar time series function is driven by a menu

5. Time Series Model Estimation Read output as a regression output –Beta coefficients are OLS slope coefficients –SE of Coef used to calculate t ratios to determine which lags are significant –For goodness of fit refer to AIC, SIC and MAPE –Can test restricting out lags (variables)

6. Before You Estimate TS Model (Review) Dickey-Fuller test indicates whether the data series used for the model, D i,t, is stationary and if the model is D 2,t = a + b 1 D 1,t the DF it indicates that t stat for b 1 is < -2.90 Augmented DF test indicates whether the data series Di,t are stationary, if we added a trend to the model and one or more lags D i,t =a + b 1 D i,t-1 + b 2 D i,t-2 +b 3 D i,t-3 + b 4 T t SIC indicates the value of the Schwarz Criteria for the number lags and differences used in estimation –Change the number of lags and observe the SIC change AIC indicates the value of the Aikia information criteria for the number lags used in estimation –Change the number of lags and observe the AIC change –Best number of lags is where AIC is minimized Changing number of lags also changes the MAPE and SD residuals

7. Time Series Model Forecasting Assume a series that is stationary and has T observations of data so estimate the model as an AR(0 difference, 1 lag) Forecast the first period ahead as Ŷ T+1 = a + b 1 Y T Forecast the second period ahead as Ŷ T+2 = a + b 1 Ŷ T+1 Continue in this fashion for more periods This ONLY works if Y is stationary, based on the DF test for zero lags

8. Time Series Model Forecasting What if D 1,t was stationary? How do you forecast? First period ahead forecast is D 1,T = Y T – Y T-1 D̂ 1,T+1 = a + b 1 D 1,T Add the forecasted D 1,T+1 to Y T to forecast Ŷ T+1 Ŷ T+1 = Y T + D̂ 1,T+1

9. Time Series Model Forecasting Continued Second period ahead forecast is D̂ 1,T+2 = a + b D̂ 1,T+1 Ŷ T+2 = Ŷ T+1 + D̂ 1,T+2 Repeat the process for period 3 and so on This is referred to as the chain rule of forecasting

10. For estimated Model D 1,t = 4.019 + 0.42859 D 1,T-1 YearHistory and Forecast Ŷ T+i Change Ŷ or D̂ 1,T Forecast D 1T+i Forecast Ŷ T+i T-11387 T1289-98.0-37.925 = 4.019 + 0.428*(-98) 1251.1 = 1289 + (-37.925) T+11251.1-37.9-12.224 = 4.019 + 0.428*(-37.9) 1238.91 = 1251.11 + (-12.22) T+21238.91-12.19-1.198 = 4.019 + 0.428*(-12.19) 1237.71=1238.91 + (-1.198) T+31237.71

11. Time Series Model Forecast

12. Time Series Model Estimation Impulse Response Function –Shows the impact of a 1 unit change in Y T on the forecast values of Y over time –Good model is one where impacts decline to zero in short number of periods

13. Time Series Model Estimation Impulse Response Function will die slowly if the model has to many lags Same data series fit with 1 lag and a 6 lag model

14. Time Series Model Estimation Dynamic stochastic Simulation of a time series model Lecture 6

15. Time Series Model Estimation Look at the simulation in Lecture 6 Time Series.XLS

16. Time Series Model Estimation Result of a dynamic stochastic simulation

17. Vector Autoregressive (VAR) Models VAR models a time series models where two or more variables are thought to be correlated and together they explain more than one variable by itself For example forecasting –Sales and Advertising –Money supply and interest rate –Supply and Price We are assuming that Y t = f(Y t-i and Z t-i )

18. VAR Time Series Model Estimation Take the example of advertising and sales A T+i = a +b 1 DA 1,T-1 + b 2 DA 1,T-2 + c 1 DS 1,T-1 + c 2 DS 1,T-2 S T+i = a +b 1 DS 1,T-1 + b 2 DS 1,T-2 + c 1 DA 1,T-1 + c 2 DA 1,T-2 Where A is advertising and S is sales DA is the difference for A DS is the difference for S In this model we fit A and S at the same time and A is affected by its lag differences and the lagged differences for S The same is true for S affected by its own lags and those of A

19. Time Series Model Estimation Advertising and sales VAR model Highlight two columns Specify number of lags Specify number differences

20. Time Series Model Estimation Advertising and sales VAR model

21. Equilibrium Displacement Model Forecasting Elasticities of demand and supply can be used to forecast changes in price, quantity demanded and quantity supplied Small changes in the exogenous variables allows one to forecast the dependent variable This method is simple and reliable for small changes from equilibrium Information needed: –A Baseline of equilibrium quantities and prices –Own and cross elasticities for demand and supply –Residuals from trend (or a structural model) for the dependent variable if the forecast is to be stochastic

22. Equilibrium Displacement Model Forecasting Baseline prices and quantities are available from FAPRI, USDA, and some private consulting firms Here is an example of the corn S&U from the FAPRI March 2013 Baseline

23. Equilibrium Displacement Model Forecasting To forecast a price change given a change in quantity supplied Price 1 = P 0 * [1 + Price Flex *( Q 1 - Q 0 ) / Q 0 )] + ẽ P 0 and Q 0 are baseline values Q 1 is assumed change Price Q/UT Q0Q0 Q1Q1 P0P0 ?P 1 Demand

24. Equilibrium Displacement Model Forecasting To forecast the Q Supplied given a change in price Qt Supplied 1 = Qs 0 * [1 + E s *( P 1 - P 0 ) / P 0 )] + ẽ P 0 and Q 0 are baseline values P 1 is assumed change Price Q/UT Q0Q0 ?Q 1 P0P0 P1P1 Supply

25. Equilibrium Displacement Model Forecasting We can expand the supply response equation by including cross elasticities Qt Supplied x = Qsx 0 * (1 + [Ex s *( Px 1 - Px 0 ) / Px 0 )] + [Ex,y s *( Py 1 - Py 0 ) / Py 0 )] + [Ex,z s *( Pz 1 - Pz 0 ) / Pz 0 )] ) + ẽ Where x is the own crop (say, corn) and y is the price of soybeans, and z is the price of wheat The supply response equation can be expanded to contain cross elasticities for all other crops Note: Ex,y s is the elasticity of corn supply with respect to the price of soybeans

26. Equilibrium Displacement Model Forecasting Elasticity of demand equation can be used to forecast quantity exported, or quantity demanded for ethanol or any other quantity demanded All we need is the Baseline quantity demanded, baseline price and the own and cross elasticity QD 1 = QD 0 * (1 + ED for exports * ( P 1 - P 0 ) / P 0 )) + ẽ Price Q/UT Q0Q0 ?Q 1 P0P0 P1P1 Demand for Exports

27. Equilibrium Displacement Model Forecasting This method of forecasting is widely used in agricultural economics Particularly useful for policy analysis and consulting This is why economists place so much emphasis on estimating unbiased elasticities