The impact of monetary policy on agricultural output and food prices in Nigeria: A Structural Vector Autoregressive Analysis

📄 Data Description
This dataset supports the econometric analysis employed in the manuscript, with emphasis on structural vector autoregressive (SVAR) modeling of Nigeria’s macroeconomic and agro-climatic variables spanning Q1 2000 to Q4 2022.
🗂️ 1. Dataset Overview
The data consist of quarterly time-series indicators sourced from multiple editions of the Central Bank of Nigeria’s Statistical Bulletin. Variables capture the interplay between monetary policies, agricultural investments, inflationary trends, exchange rate dynamics, climatic conditions, and sectoral productivity.
📊 2. Variables Included
Symbol Variable Description Unit
PR Policy Rate Monetary policy rate Percent (%)
AL Agric Loan Bank loans to agricultural sector Millions of NGN
FP Food Price Food Consumer Price Index Base: Nov. 2009 = 100
EX Exchange Rate USD to Nigerian Naira US$/₦
RN Rainfall Climatic variable measuring precipitation Millimeters
AP Agric Productivity Nominal GDP for agriculture sector Millions of NGN
🧪 3. Model Diagnostics and Statistical Properties
• Unit root tests via Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) confirm all variables are integrated of order 1 [I(1)] except for Agricultural Productivity (AP), which is stationary at level [I(0)].
• Variables were differenced to achieve stationarity before inclusion in the SVAR model.
• Diagnostic tests (Tables 4 & 5) validate the model's suitability, showing no serial correlation and roots within the unit circle, indicating stability.
⚙️ 4. Analytical Decisions
• Global food prices were deliberately excluded due to multicollinearity with domestic food CPI, based on strong correlation and country-specific relevance.
• Rainfall data was adjusted for seasonality to accurately reflect agricultural cycles.
📈 5. Data Usage
This dataset underpins variance decomposition analysis, impulse response functions, and significance testing of contemporaneous macroeconomic shocks in a SVAR framework. It also facilitates replication of empirical results for macroeconomic modeling in agro-based economies.
📚 6. Software Used
• EViews: Estimation of SVAR model, unit root tests, and diagnostics
• Microsoft Excel: Data entry, cleaning, and preprocessing
• Scripts and setup files are available to ensure reproducibility.