Dynamic Interlinkages Among Macroeconomic Variables — VAR Model Results

To understand how financial and macroeconomic variables influence each other over time, a Vector Autoregression (VAR) model was estimated using four key indicators:

• Close Price (Stock Market Performance)

• GDP Growth (%)

• Inflation Rate (%)

• Interest Rate (%)

The VAR model treats all variables as endogenous — each can influence and be influenced by others — making it ideal for studying macro-financial dynamics and policy transmission effects.

Model Overview

These metrics indicate a well-specified model with stable variance across equations, suitable for forecasting and impulse response analysis.

Key Results by Equation

1️⃣ Close Price Equation

• Inflation Rate (L1) shows a positive and significant coefficient (15.39, p=0.033).
  ➤ This implies that rising inflation in the short term is associated with an increase in stock prices, possibly reflecting nominal adjustments or speculative behavior during inflationary periods.

• Other predictors — GDP Growth, Interest Rate, and lagged stock prices — are statistically insignificant, highlighting short-term inertia in financial markets.

2️⃣ GDP Growth Equation

• None of the lagged variables shows statistical significance.
  This suggests that GDP growth in this period behaved largely as an exogenous process, with limited feedback from financial indicators within short lags.

3️⃣ Inflation Rate Equation

• No significant predictors emerge, indicating that inflation dynamics were not directly driven by immediate past values of GDP, interest, or stock prices.
  This pattern is consistent with price stickiness and policy-driven stability mechanisms.

4️⃣ Interest Rate Equation

• Inflation Rate (L1) exhibits a negative and significant effect (–0.036, p=0.033).
  This confirms that central banks likely raised interest rates in response to previous inflation increases, consistent with the monetary policy rule.

• Other variables remain insignificant, underscoring that policy adjustments were primarily reactive to inflation pressures.

Correlation Matrix of Residuals

The low correlations between residuals indicate no serious endogeneity or omitted-variable bias, validating the model’s structural independence.

Analytical Insight

• The VAR model confirms that macroeconomic and financial variables interact weakly at daily frequencies, suggesting delayed or policy-moderated feedback loops rather than immediate cause–and–effect relationships.

• Inflation remains the most influential short-run variable, affecting both stock prices and interest rates.

• The low residual correlations and stable coefficients indicate that the system is dynamically consistent — ideal for advanced tools like Impulse Response Functions (IRFs) and Variance Decomposition.

Summary Interpretation

This VAR analysis shows that:

• Inflation acts as a short-term driver of both financial and monetary variables.

• Interest rate policy responds systematically to inflation shocks.

• GDP growth and stock prices exhibit inertia, reacting more slowly to macroeconomic shifts.

These results emphasize the importance of inflation management for macro-financial stability and highlight lagged transmission mechanisms in the economy.

Source & Acknowledgment

Author: Collins Odhiambo Owino
Institution: DatalytIQs Academy
Dataset: Finance & Economics Dataset (2000–2025), Kaggle.
Source: DatalytIQs Academy Research Repository — Compiled from open global financial and macroeconomic sources (2025).