| 
  • If you are citizen of an European Union member nation, you may not use this service unless you are at least 16 years old.

  • Social distancing? Try a better way to work remotely on your online files. Dokkio, a new product from PBworks, can help your team find, organize, and collaborate on your Drive, Gmail, Dropbox, Box, and Slack files. Sign up for free.

View
 

Examples of application of econometric methods

Page history last edited by editor 7 years, 6 months ago

 


 

 

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, 2010-2030

Reference: Prasad, Pullabolta and Ganesh-Kumar. Supply and Demand for Cereals in Nepal, 2010-2030. 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 single-crop production function (using data from 1995-2008)

2) Demand function/projection - based on the data from NLSS II (2003-04)

 

Methods in Detail:

 

1) SUPPLY PROJECTION

 

A. Building a model:

  • Model: Three single-crop 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: Double-log Cobb-Douglas production function models is prepared for each of the 3 crops
  • Multicollinearity & Heteroskedasticity:
    • For each, Durbin-Watson d-statistic was used to test for autocorrelation, and if AC present, the regression model was estimated through a Prais-Winstein regression
  • Final production function:
    • Only significant variables were retained for each model and the model was re-estimated

 

B. Use the model for projection:

 

  • Build auxiliary models to first build the future values of X-variables; 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 X-values 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 (b-coeff 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 X-variables and see which ones have what kind of influence on farmer's preference of how many acres of crop to plant (X-vars 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 LA-AIDS
        • 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 cross-equation 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)
       

 

 

 

 

 

 

Comments (0)

You don't have permission to comment on this page.