HOW DOES INFLATION AFFECT CONSUMERS PART XII

ADDING EXTRA VARIABLES THAT HAS NEGLIGIBLE AFFECT OR NO EFFECTS WILL MAKE YOUR ANALYSIS COMPLICATED AND TOO MUCH WORK SO TIS KEPT SIMPLE. STEP 2   PAST DATA COLLECTIONS ONCE YOU HAVE YOUR VARIABLES SET AND DETERMINED THE NEXT IMPORTANT STEP IN THE PROCESS IS THE COLLECTION OF THE PAST DATA. THE DATA THAT SHOULD BE GATHERED MUST BE WITH RESPECT TO THE VARIABLES THAT YOU HAVE DETERMINED OR SET. THE DATA COLLECTION CAN BE DONE IN TWO DIFFERENT UNIQUE WAYS DESCRIBED BELOW FIRSTLY, IT CAN BE COLLECTED WITH RESPECT TO THE POPULATION IN TERMS OF INCOME, PRICES, AND ETC. FOR VARIOUS TIME PERIODS ALSO IN CAN BE COLLECTED WITH RESPECT TO INCOME AND PRICES FOR THE DIFFERENT REGIONS OF THE MARKET FOR A PARTICULAR TIME PERIOD. 

REMEMBER THAT FREQUENCY OF THE DATA COLLECTED CAN BE OF VARIOUS PERIODS SUCH AS FOR A MONTH, HALF A YEAR OR A YEAR OR SO, BUT THE REGULARITY OF THE FREQUENCY IS IMPORTANT AND IT SHOULD BE MAINTAINED, FOR EXAMPLE IF THE DATA COLLECTED IS FOR EVERY OTHER YEAR OR SO IT IS BEST TO KEEP ON COLLECTING THE DATA YEAR WISE AND NOT TO SHIP ANY YEAR. STEP 3 SELECTION OF THE DEMAND FUNCTION AFTER SETTING THE VARIABLES AND THE COLLECTION OF PAST DATA, THE NEXT STEP BEFORE FINALLY COMPILING EVERYTHING IS TO DETERMINE THE COMPLETE DEMAND FUNCTION. WHICH IS THE DEPENDENT OR THE VARIABLE THAT IS TO BE PREDICTED. THE FACTORS THAT EFFECT THIS ARE THE VARIABLES THAT WERE DEFINED IN THE FIRST STEP. THE DEMAND FUNCTION CAN BE OF LINEAR OR IN LOGARITHMIC FORM, BOTH OF THEM ARE DISCUSSED INDIVIDUALLY BELOW.

LINEAR INVOLVES FINDING THE LINE OF BEST FITS BETWEEN TWO OR MORE VARIABLES, THUS BY INPUTTING ONE OR MULTIPLE VARIABLES OTHER VARIABLE CAN BE PREDICTED, USING A LINEAR EQUATION. NOW CONSIDERING THE FUNCTION FORMED ABOVE IN STEP 1 AND DETERMINING ITS DEMAND FUNCTION IN THE LEANER EQUATION FORM BELOW, ZDA = M0+M1PZ+M2Y+M3A+M4PS+M5PC WHERE, M1, M2, M3, M4 AND M5 ARE THE REGRESSION COEFFICIENTS. THESE COEFFICIENTS REPRESENT THE ELASTICITY OF DEMAND ALSO INCLUDE PRICE ELASTIC, INCOME ELASTIC, PROMOTIONAL ELASTICITY AND CROSS ELASTICITY OF DEMAND.

THESE COEFFICIENTS ARE RESPONSIBLE FOR THE AMOUNT OF CHANGE AS WELL AS THE NATURE OF THE CHANGE (POSITIVE OR NEGATIVE) FOR EXAMPLE IN THE ABOVE EQUATION EFFECT OF INCOME, ADVERTISEMENT AND SUBSTITUTE WILL HAVE A POSITIVE EFFECT ON THE DEMAND, WHEREAS THE PRICE OF COMMODITY A MIGHT HAVE A NEGATIVE EFFECT ON THE FUTURE DEMANDS OF IT. THEN COMES THE POLYNOMIAL FORMS IN WHICH THE RELATIONSHIP IS NOT A STRAIGHT LINE, NEVER THE LESS THEY CAN BE CONVERTED INTO A STRAIGHT LINE BY TRANSFORMING THE VARIABLE USING LOGARITHMS INTO A STRAIGHT LINE FOR SIMPLICITY, IT IS MOST COMMONLY USED IN META MODELS FOR MECHANICAL SYSTEMS. 

STEP 4 FUNCTION ESTIMATION THIS IS THE MOST IMPORTANT STEP IN WHICH THE COEFFICIENT VALUES ARE FOUND, THESE COEFFICIENTS EACH REPRESENTS THE MEAN CHANGE IN THE RESPONSE VARIABLE FOR ONE UNIT OF CHANGE WHILE KEEPING THE OTHER VARIABLES CONSTANT. THESE CAN BE EASILY DETERMINED USING THE SOFTWARE USED FOR PLOTTING THE REGRESSION ANALYSES GRAPH OR MANUALLY STANDARDIZING EACH VALUES. IT IS ESSENTIAL TO REMEMBER THAT THE VALUES OF THESE COEFFICIENT CANNOT BE MORE THAN ONE, ALWAYS <1.

STEP 5 FORECAST DERIVATION THE LAST RAD THE FINAL STEP IS DERIVING THE FORECAST USING YOUR PREDICTED VALUES FOR THE COEFFICIENTS AND YOUR FUNCTION. IN THIS STEP YOU CAN ESTIMATE THE VALUES OF INCOME, PRICES, PRICES OF RELATED SUBSTITUTED, PROMOTIONAL EXPENDITURES ETC. FOR THE UPCOMING TIME USING THIS REGRESSION METHOD.