Multivariate Time Series Analysis of the USD/IQD Exchange Rate Using VAR, SVAR, and SVECM Models

This study examines the USD/IQD exchange rate using multivariate time series models. We implement vector autoregressive (VAR), structural VAR (SVAR), and structural vector error correction (SVEC) models using the 'vars' package in R. The analysis includes diagnostic testing, a constrained model estimation, prediction, causality analysis, impulse response functions, and forecast error variance decomposition. Variables are selected using the Granger causality test, leading to various model combinations. Model 3, which includes USD, gold, and copper, is identified as optimal for accurate forecasting. Although the oil variable has a high p-value (0.4674), its inclusion is justified based on economic intuition and statistical reasoning, given its influence on exchange rates and commodity prices that is crucial for making good investment decisions.