The impact of monetary policy on agricultural output and food prices in Nigeria: A Structural Vector Autoregressive Analysis
Description
📄 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.
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Steps to reproduce
Data Collection Protocol 🗓️ 1. Timeframe and Sampling • Quarterly data spanning 2000–2022 • Sourced from various editions of the Central Bank of Nigeria Statistical Bulletin • All variables formatted into consistent quarterly frequency using calendar-based interpolation (where missing) 🧩 2. Variables and Classification Endogenous Variables: • PR: Monetary policy rate (%) • AL: Bank loan to agricultural sector (₦ millions) • AP: Agricultural productivity (nominal GDP in ₦ millions) • FP: Food Consumer Price Index (CPI) Exogenous Variables (Strictly exogenous for identification): • EX: Exchange rate (USD/₦) • RN: Rainfall (millimeters, seasonally adjusted) 🧪 3. Seasonality Treatment • Applied ACF correlogram analysis to detect quarterly seasonality • Seasonal effects confirmed and validated using seasonal regression models (Sims, 1974) • Seasonal dummy variables (Q1–Q4) incorporated and cyclical components extracted ________________________________________ ⚙️ Data Processing & Analytical Workflow 🛠️ 4. Software & Instruments • EViews 12: Primary tool for SVAR estimation, impulse response functions (IRFs), and variance decompositions (FEVDs) • Microsoft Excel: Data entry, cleaning, and unit conversion • All scripts and transformation steps documented in project notebook (available for reproducibility) 📉 5. Stationarity & Diagnostics • Unit Root Tests: Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests performed • Differencing applied to I(1) variables to ensure stationarity before SVAR estimation • Structural break detection confirmed for seasonally adjusted rainfall series 🧮 6. SVAR Model Identification • Recursive short-run restrictions implemented using Cholesky decomposition • Ordering informed by Cobb-Douglas production function and macroeconomic transmission lags • Alternative identification strategies explored (sign restrictions, long-run restrictions, and heteroskedasticity-based methods) ________________________________________ 🔍 Model Validation ✅ 7. Diagnostic Tests • Residual Serial Correlation LM Tests: Confirm absence of autocorrelation • Roots of Characteristic Polynomial: All roots inside the unit circle—model satisfies stability condition • Statistical significance assessed at 5% level ________________________________________ 📜 8. Replication & Integrity Notes • All datasets, unit root test outputs, seasonal regression diagnostics, and model estimation results are saved and versioned • Food price index selection based on country-specific responsiveness and data reliability (excluding global food prices to avoid multicollinearity) • Full documentation of variable definitions and transformation rules included
Institutions
- Central Bank of Nigeria