Time Series Econometrics Using Microfit 5.pdf -

The output appeared:

But the short run? That’s where the ghost hid. Microfit 5 made the Error Correction Model (ECM) seamless. From the same VAR output, she clicked View → Long Run Form (ECM) . Time series econometrics using Microfit 5.pdf

She first-differenced the non-stationary variables (Microfit 5 → Generate → d(x) ). Now, D(LAGOS_CONSUMPTION) and D(LONDON_REMITTANCES) became stationary. But she had lost the long-run relationship. For that, she needed Chapter 2. Chapter 2: The Long-Run Marriage (Cointegration) The PDF’s most dog-eared section was on Cointegration . "If two non-stationary series move together over time," it read, "their linear combination might be stationary. That is cointegration." The output appeared: But the short run

In Microfit 5: . She ordered: REMITTANCES → CONSUMPTION (remittances cause consumption, not vice versa). From the same VAR output, she clicked View

The PDF explained: "The error correction term (ECT) measures the speed of adjustment back to equilibrium after a shock."

D(LAGOS_CONSUMPTION) = 0.15 * D(LONDON_REMITTANCES) - 0.32 * ECT(-1) (short-run) (adjustment speed) That -0.32 was gold. It meant that 32% of any disequilibrium from last quarter was corrected this quarter. Shocks faded in about three quarters. But why was Lagos consumption not rising? She saw the answer: the short-run coefficient (0.15) was much smaller than the long-run (0.86). Remittances boosted consumption weakly in the short term—people saved or paid debt first. The PDF’s footnote warned: "Policy based on long-run elasticities alone is blind to liquidity traps." To convince policymakers, Aliyah needed a story. She turned to Impulse Response Functions (IRFs) .