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Examples of application of econometric methods
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Methods for Forecasting demand and supply
The section catalogs some methods used in forecasting demand and supply of products.
Study1: Supply and Demand for Cereals in Nepal, 20102030

Reference: Prasad, Pullabolta and GaneshKumar. Supply and Demand for Cereals in Nepal, 20102030. IFPRI. September 2012. Link

Motivation: The study attempts to estimate supply and demand models for the 3 most important cereals: rice, wheat, and maize.

Methods Overview:
1) Supply projection  based on a singlecrop production function (using data from 19952008)
2) Demand function/projection  based on the data from NLSS II (200304)

Methods in Detail:
1) SUPPLY PROJECTION
A. Building a model:
 Model: Three singlecrop production functions
 Model specification:
 Y: crop production amount (tons)
 Xs:
 area under crop (A)
 improved seed quantity (S)
 gross irrigated area (I)
 total fertilizer supply (F)
 annual average rainfall (R)
 Functional form: Doublelog CobbDouglas production function models is prepared for each of the 3 crops
 Multicollinearity & Heteroskedasticity:
 For each, DurbinWatson dstatistic was used to test for autocorrelation, and if AC present, the regression model was estimated through a PraisWinstein regression
 Final production function:
 Only significant variables were retained for each model and the model was reestimated
B. Use the model for projection:
 Build auxiliary models to first build the future values of Xvariables; then use these to predict Y
 Identify policy variables
 Variables that the government can manipulate  such as irrigation level, fertilizer supply and seed supply)
 Since these are manipulated by the government, we can assume several scenarios and determine future Xvalues accordingly.
 Business as usual: We can examine the growth rate and assume this growth rate will continue (Business as Usual); for this we need to regress each of the three policy variables with time variables (years) and calculate growth rate (bcoeff in double log auxiliary regression  b/c log allows us to interpret in % terms)
 Other growth rate assumptions: We can also assume certain growth rate and use that in the model.
 Identify behavioral variables
 Variables that depend on farmer choices and preferences, such as crop acreage
 Here, we can regress crop acreage with other Xvariables and see which ones have what kind of influence on farmer's preference of how many acres of crop to plant (Xvars include  irrigation, rainfall, fertilizer, seed supply)
 Now predict Y
 Now, determine which years you want to forecast for: 2010, 2015, 2020, 2025 and 2030
 Now use the time trends of policy variables > predict crop acreages (behavioral variable)
 Then use crop acreages & policy variables > predict Ys
 This can be done for several scenarios:
 Business as Usual
 Pessimistic
 Optimistic
2) Demand Projection
 Theory/background:
 Total domestic demand for 3 cereals consists of:
 Direct demand by households
 Indirect demand for processing (ex: seed, feeds, wastage)
 To determine direct demand by households
 Use Household demand model  Linear Approximate  Almost Ideal Demand System (AIDS) Model
 Formulation:
 Start with AIDS model
 Use linear price index (Stone price index) to use LAAIDS
 Parameters
 Y: wi = % of HH expenditure on food items
 pi = price of i good
 y = total expenditure on all goods
 P = price index
 Estimate the model
 Use seemingly unrelated regression (SUR) procedure
 B/c of possible crossequation correlations
 Compute elasticities
 Expenditure elasticity
 Price elasticity
 To determine indirect demand for processing
 Use Food budget model
 B/c AIDS model captures % expenditure on food items only and not the budget itself
 Estimate seed, feed and wastage
 Data:
 Nepal Living Standards Survey (Household survey)

Examples of application of econometric methods

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