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Exploring VARs and VECMs: Estimation, Forecasting, and Analysis Using STATA

This comprehensive guide delves into the world of Vector Autoregressions (VARs) and Vector Error Correction Models (VECMs). We cover the entire process from estimation and forecasting to conducting impulse-response functions using quarterly data from January 1995 to December 2004. Utilizing STATA, we explore generating real variables, testing for stability, Granger causality, cointegration, and long-run relationships among key economic indicators such as GDP, consumption, and the GDP deflator. Perfect for anyone looking to enhance their macroeconomic analysis skills!

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Exploring VARs and VECMs: Estimation, Forecasting, and Analysis Using STATA

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  1. MACROECONOMETRICS LAB 4 – VARs and VECMs

  2. ROADMAP • Everything is by definition linked with everything? • Estimating VARs • Estimation and forecasting • IRF • VECM • All about STATA 

  3. Dataset • Timing • Quarterly data, Jan. 1995 – Dec. 2004 • Data • GDP • Consumption • GDP deflator • M3 • Quarter dummies

  4. STATA (1) • set memory 99m • set matsize 800 • Open downloaded file • Generate real variables: • Real GDP: gen rGDP=GDP/def_CPI • Real consumption • Generate logs (same way) • Log (realGDP) • Log (realconsumption) • Log (def_CPI) • Log (m3)

  5. STATA (2) • Graphing • twoway (line lrpkb lrconsumption if date<q(2004.4)) (or alternatively, using the menu) • twoway (line lm3 ldef_CPI if date<q(2004.4)) (or alternatively, using the menu) • VARs • var lrpkb ldef_CPI lrconsumption lm3, exog(_q*)

  6. STATA (3) • Diagnosis • Testing for number of lags • varsocvariables, maxlag(3) • Testing for VAR stability • varstable • Testing for Granger causality • vargranger • Testing for autocorrelation • varlmar

  7. STATA (4) • Impulse-response functions • Starting • cap irf drop • Creating (Option #1) • varbasic lrpkb lrconsumption ldef_CPI lm3 if date<=q(2004q4), lags(1/3) • Creating (Option #2) • varirf create irf, set(irf, replace) • varirf graph irf, i(lrpkb) r(lrconsumption) / UNIQUE SHOCK/ AND • varirf graph oirf, i(lrpkb) r(lrconsumption) /ORTHOGONAL SHOCK/

  8. STATA (5) • Forecasting • Computing • varfcast compute, step(8) dynamic(q(2005.1)) • Graphing • varfcast graph lrconsumption ldef_CPI lm3 lrpkb

  9. STATA (6) • No. of cointegrating relations (Johansen trace test for the rank) • vecrank lrconsumption ldef_CPI lrpkb lm3, sindicators(_q*) lags(3)

  10. STATA (7) • VECM • vec lrconsumption ldef_CPI lrpkb lm3, rank(3) sindicators(_q*) lags(2) REMEMBER: Cointegrating vectors not interpretable • Testing for stability • vecstable • Testing for autocorrelation • veclmar

  11. STATA (8) • Testing for long run relationships (examples the same as in the lecture) • constraint 1 [_ce1]lrconsumption=-1 • constraint 2 [_ce1]ldef_CPI • constraint 3 [_ce1]lm3 • constraint 4 [_ce2]lm3=-1 • constraint 5 [_ce2]lrconsumption • constraint 6 [_ce2]ldef_CPI=1 • constraint 7 [_ce2]lrpkb=1 • vec lrconsumption ldef_CPI lrpkb lm3, rank(2) sindicators(_q*) lags(3) dforce bconstraints(1/7)

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