Neo classical general equilibrium theory which is based on Walrasian theory of general equilibrium 2*2*2 model and Marshallian graphical representation
Neo classical general equilibrium theory which is based on Walrasian theory of general equilibrium 2*2*2 model and Marshallian graphical representation
Green accounting is a type of accounting that attempts to factor environmental costs into the financial results of operations. It has been argued that gross domestic product ignores the environment and therefore policymakers need a revised model that incorporates green accounting.
Dynamic Pricing over Finite HorizonsSingle Resource Case.docxjacksnathalie
Dynamic Pricing over Finite Horizons:
Single Resource Case
Guillermo Gallego
Spring 13
Abstract
In this chapter we consider the problem of dynamically pricing one or more products
that consume a single resource to maximize the expected revenue over a finite horizon.
We assume that there is a sunk investment in capacity that is not-replenishable over
the sales horizon. We formulate continuous and discrete optimal control problems for
price sensitive, Poisson and compound Poisson, demands. We discuss the advantages
and disadvantages of dynamic pricing versus fixed pricing and versus quasi-static pricing
policies. We use Approximate Dynamic Programming with affine functions to obtain
an upper bound on the value function and to develop heuristics that are asymptotically
optimal as the size of the system scales. We then consider pricing with finite price menus
and semi-dynamic pricing strategies.
1 Single Product Dynamic Pricing
In this Chapter we consider the problem of dynamically pricing one or more products that
consume a single resource over a finite horizon with the objective of maximizing the expected
revenue that can be obtained from c units of capacity over a given selling horizon. We will
measure time backwards so that t is the time-to-go until the end of the horizon. At the start
of the selling season the time-to-go is T . We assume that the salvage value at the end of the
horizon is zero to reflect the fact that in many applications the product is perishable. If there
is a positive salvage value then the objective is to maximize the expected revenue in excess
of salvage value, so the zero salvage value can be made without loss of optimality. We will
assume that the capacity provider cannot replenish inventory during the sales horizon. This
assumption holds for hotels and seasonal merchandise including fashion retailing, and to a
large extent to airlines who allocate planes to routes but may, in some cases, swap planes of
different capacities to better align capacity with demand.
We will assume that customers arrive as a time heterogeneous Poisson or compound Poisson
process. Expositionally, it helps to introduce the basic formulation for the Poisson case and
later take care of the changes needed to deal with the compound Poisson case. It is also
1
helpful to initially work with a single product and then show how that under mild conditions
the same formulation works for multiple products consuming a single resource. The pricing
problem for multiple resources will be dealt in a different Chapter.
Let dt(p) be the Poisson arrival rate of customers willing to buy at price p ∈ <+ at time
t. We assume that customers unwilling to buy at price p leave the system. Let rt(p,z) =
(p−z)dt(p). We know from the Static Pricing Chapter that if dt(p) is upper semi-continuous,
and
∫∞
0 d̄t(p)dp < ∞, where d̄t(p) = supq≥p d(q), then there exist a finite price pt(z), increasing
in z, such that rt(z) = supp≥0 rt(p,z) = maxp≥0 rt(p,z) = rt(pt( ...
Basic concepts and how to measure price volatility
Presented by Carlos Martins-Filho at the AGRODEP Workshop on Analytical Tools for Food Prices
and Price Volatility
June 6-7, 2011 • Dakar, Senegal
For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop
Chapter 2 Market forces Supply & DemandThis chapter includes f.docxarnit1
Chapter 2: Market forces Supply & Demand
This chapter includes four important elements:
1. A “change in quantity” demanded or supplied as a result of a change in the current price.
This is a movement along the demand or supply curve. This helps us understand the slope of demand or supply with respect to the price and then estimate their own price elasticities.
2. A “shift or change in the demand or supply” as a result of a change in a relevant “factor other” than the current price. This change represents a change in the entire demand or supply or a shift. Understanding the factors that shift the demand or supply help us specify and estimate a demand or supply equation and estimate the other factors’ elasticities
How do we distinguish a “change in quantity demanded or supplied” from a “change in demand or supply”? If the factor that changes is on any of the axes (such as the current price is on the vertical axis), then there is a “change in quantity demanded or supplied”. But if the change is in a factor that is not on any of the axes such as income or cost of production, then there is a “shift or change in demand or supply”.
The student should define the slope of direct demand or supply with respect to current price as “change in quantity over change in price”. Not the other way!
Example:
(Direct) demand: Qdx = 6,060 – 3Px. Slope of demand = ∆Q/∆P = -3
Inverse demand: Px = 2020 -1/3Qdx. Slope of inverse demand = ∆P/∆Q= -1/3
3. Consumer and producer surplus
What is the usefulness of calculating the consumer surplus for the manager? The manager can use it in price discrimination and in valuing full economic prices. What’s the usefulness of knowing the producer surplus? The producer can use it to bargain with the distributor over the surplus above minimum cost of producing the good accruing to the distributor.
4. Market equilibrium and disequilibrium (or price restrictions)
“Market equilibrium” means supply equals demand and there is no surplus or shortage. This helps determine equilibrium price and quantity.
“Market disequilibrium” means that supply and demand do not intersect or are not equal at any price in the market. In this case, we have either a surplus (quantity supplied exceeds quantity demanded) or a shortage (quantity demanded exceeds quantity supplied). This helps us determine the size of shortage or surplus.
When a government intervenes in the market and buys the surplus to set a price above the equilibrium price, then there is a “price floor” as is the case with agricultural products.
If the government issues a decree and sets the price below the equilibrium price then there is a “price ceiling or control” which leads to shortages. Some governments set a rent control for apartments.
THE SUPPLY FUNCTION
Supply function and Shifts in Market Supply
Supply Specification: The simple supply equation is defined as:
Qs = a + bP
and the slope with the respect to the price ∆Q/∆P is positive. That is the supply curve is ...
Supply Response of Cereal Crop Farmers’ to Price and Non-Price Factors in Raj...Premier Publishers
The present study investigated cereal crop farmers’ acreage response to price and non-price factors in Rajasthan State of India using time series data spanning from 1981 to 2014. The cereal crops considered for the study were jowar, maize, bajra, wheat and barley. Furthermore, the Nerlovian model was used for data synthesis. From the results, it was observed that farmers in the state were not price responsive except for maize. The growers of these crops considered the lagged area and lagged price of competing crops to be the major factors for area allocation decision. The lagged price and lagged yield emerged as an important factor in deciding area allocation to maize and barley crops respectively. Therefore, findings showed that farmers’ decision on cereal crops acreage allocation were governed by both price and non-price factors. Hence price incentive alone was not sufficient in bringing desirable change in cropping pattern as well as the production of these crops. Therefore, the creation of other infrastructural facilities like irrigation is important to increase acreage and production with stability in the studied area.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Doctoral Symposium at the 17th IEEE International Conference on Software Test...
Supply response models
1.
2. NERLOVIAN SUPPLY RESPONSE MODEL
Speaker: Samriti (F-17-05-D)
Department of Social Science
Dr YSP UHF Nauni, Solan
3. Supply: Supply of a commodity refers to the quantity of a commodity
that is offered for sale in market at a given price during a particular
period of time.
The definition of supply is complete when it has the following
elements:
(i)Quantity of a commodity offered for sale.
(ii)Price of the commodity.
(iii)Time during which the quantity is offered.
Law of supply: law of supply states that, other things remaining
constant, there is a positive relationship between price of commodity
and its quantity supplied. Thus more is supplied at higher price and
less at lower price.
4. Supply Response Function:
The supply response function shows the functional relationship between price of
the commodity and quantity supplied on the assumption that other determinants
of supply are held constant. These other determinants are rainfall, fertilizer
prices of competing crops, wages etc.
When the 'ceteris paribus' assumption is not met then there will be shifts in the
supply curve. As such the 'ceteris paribus’ assumption is not satisfied and the
simple relationship extend to a multivariate one.
5. Need for the supply response function
Supply responses of primary producers vary considerably according to the
characteristics of the crops analyzed.
Annual/seasonal and perennial crops have different characteristics and present
different conceptual problems. Therefore, they require the use of different models.
Most of the econometric studies on supply responses have been carried out on
annual/seasonal crops. These crops form the staple foods of most underdeveloped
countries and as self-sufficiency in food became the overriding aim of most of the
underdeveloped countries, emphasis was placed on such crops.
Policies which could encourage greater production of these staple crops were
required and formulation of these policies necessitated supply response studies on
such crops. What therefore, needed is a comprehensive supply model which can
incorporate the various alternative opportunities open to the farmer.
6. RESPONSIVENESS OF PERENNIAL CROPS
Some of the models combining planting - decisions with output - planting
relationship in order to estimate the price responsiveness of producers are;
The Batesman Model
The Ady Equation
The Behrman Equation
The time horizon involved for the producers of perennial crops is much
longer than that of annual crops. For perennial crops yield are dependent
upon age of the tree, previous output and current as well as previous level of
input.
7. Responsiveness of Annual Crops
The aim of all supply response
studies is to find out how a farmer
intends to react to movements in the
price of the crop he produces.
When more than one crop is being
cultivated the aim is to find out how the
farmer intends to reallocate his efforts
between the various crops in response to
changes in the relative price levels.
In attempts to quantify such price responsiveness, acreage planted
should be used as the dependent variable because actual output is not a
good proxy for intended output. The acreage planted would give a better
indication of the farmer's intention as he has greater control over this
variable.
8. Some of the supply response function models used for annual crops.
The simple Koyck
distribution lag
model
Native
expectation model
The
Extrapolative
Expectation
Model
The Partial
adjustment model
Adaptive
expectation model
The complex
Nerlovian
expectations
model
9. According to Koyck, current value of a variable say A (area) depends on many
lagged values for another variable (price) Pt, Pt-1, Pt-2, Pt-3 ….etc. it is normally
expected that more remote values would tend to have smaller influence than
more recent.
In the Native Expectation Model (i.e., the Cobweb model) the current
expected price P*
t is assumed to be equal to the previous period's actual price
(Pt-1). This is usually done in the case where agricultural markets are
controlled by the government and future prices are announced prior to planting
time.
The Extrapolative Expectation model (Goodwin, 1947) It assumes the
expected price to be a function of the lagged price plus or minus some fraction
of the price change in the previous two periods.
P*
t = Pt-1 + β (Pt-1 - Pt-2)
10. The Nerlovian model is considered one of the most influential and
successful models used to estimate agricultural supply response among
all the econometric models.
The reduced form of the
Nerlovian model is an
autoregressive model because it
includes lagged values of the
dependent variable (output)
among its explanatory variables.
The Nerlovian model is a
dynamic model, stating that
output is a function of expected
price, area adjustment, and some
exogenous variables.
11. Nerlovian model was given by Marc Leon Nerlove in 1958
who is an American economist specialized in agricultural
economics and econometrics.
12. Nerlovian models are built to examine the farmers’ output reaction based on
price expectations and area adjustment.
. Time series data are often used for the commodity under study to capture the
dynamics of agriculture production.
The Nerlovian supply response approach enables us to determine short- run
and long-run elasticities..
13. This is the simplest version, with one determinant
and the assumption of a linear relationship, which is
based on the hypothesis that desired level of area A*t
in period t depends on the price at time t-1.
A*t = b0 + b1Pt-1 + ut
Partial Adjustment
14. As A*
t is desired area under cultivation at time t and is
unobservable, equation cannot be estimated. Therefore,
At - At-1 = α (A*
t – At-1) + ut ; 0≤ α ≤ 1
= α [ (b0 + b1Pt-1 + ut) – At-1]+ ut
= α b0 + α b1Pt-1 + α ut - α At-1 + ut
At = α b0 + α b1Pt-1 + (1- α) At-1 + α ut + ut
or At = π1 + π2 Pt-1 + π3 At-1 + ut
Where,
At = actual area under cultivation at time t
At-1 and A*
t-1 = actual and desired area at time t-1
Pt-1 = actual price at time t-1
ut = unobserved random factors affecting the area under cultivation
α = adjustment coefficient ( α = 1 when actual area = desired area & α = 0
when actual area at time t = observed area at t-1).
15. This model is based on the following behavioural
hypothesis: The value of area in any one period t
depends not on the actual value of Pt but on the
expected level of P at time t, say P*
t.
Adaptive Expectation
16. This model has become popular because it can deal with
“expectation” (about future factors) whose importance in
economic behaviour is being increasingly recognised.
At = b0 + b1P*
t + ut
P*t - P*t-1 = ɤ (Pt-1 – P*
t-1) ; 0≤ ɤ ≤ 1
P*
t = P*
t-1 + ɤ (Pt-1 – P*
t-1)
where ɤ is coefficient of expectation
P*
t = -b0/b1 + At/b1 - ut/b1
P*
t-1 = -b0/b1 + At-1/b1 - ut-1/b1 (lagged one period)
Thus area under cultivation at time t is
At = (ɤ b0) + ɤ b1Pt-1 + (1- ɤ) At-1 + {ut – (1- ɤ) ut-1}
or At = π1 + π2 Pt-1 + π1 At-1 + ut
17. Thus Nerlovian expectations model is
supposed to reflect the way in which past
experience determines the expected prices
and other expectational variables which in
turn determine the acreage planted.
19. As full adjustment to the desired allocation of land may not be
possible in the short run, the actual adjustment in area will be only a
fraction α of the desired adjustment.
At – At-1 = α (A*
t - At-1 ) + vt ……………..(2)
( α is partial adjustment coefficient)
The price that the producer expects to prevail at harvest time
cannot be observed. Therefore, one has to specify a model that explains
how the agent forms expectations based on actual and past prices and
other observable variables.
20. For eg., farmers adjust their expectations as a fraction of the deviation
between their expected price and the actual price in the last period, t-1.
P*t - P*t-1 = ɤ (Pt-1 – P*
t-1 ) + wt
P*t = ɤ Pt-1 + (1- ɤ) - P*
t-1 ; 0 ≤ ɤ ≤ 1 ……………(3)
( ɤ is the adaptive-expectations coefficient)
Since A*
t and P*t are unobservable we eliminated them from the system and
substitution of Equation (1) and (3) into Equation (2)
αA*
t = At - At-1 +α At-1 – ut
A*
t = (1/ α )At + (α -1/ α)At-1 – (1/ α)ut
21. By putting the value of A*
t in equation 2
(1/ α)At + (α -1/ α)At-1 – (1/ α)ut = b0 + b1P*
t
P*
t = (1/b1 α)At + (α -1/b1v)At-1 – (1/b1 α)ut – b0/b1
If equation is lagged by 1 therefore equation becomes:
P*
t-1 = – b0/b1 + (1/b1 α)At-1 + (α -1/b1 α)At-2 – (1/b1 α)ut-1
P*
t - P*
t-1 = ɤ( Pt – P*
t-1)
[(1/b1 α)At + (α -1/b1α)At-1 – (1/b1α)ut – b0/b1 ] – b0/b1 + (1/b1α)At-1 +
(α -1/b1)At-2 – (1/b1 α)ut-1
= ɤ [ Pt– (b0/b1 + (1/b1α)At-1 + (α -1/b1α)At-2–(1/b1α)ut-1]
22. Thus rearrangement gives the reduced form
At = π1 + π2 Pt-1 + π3 At-1 + π4 At-2 + π5 Zt + et
……………(4)
This equation is the estimable form of the supply response model.
Where;
• π1 = b0ɤα
• π2 = b1ɤ α ; short run coefficient of supply response
• π3 = (1- α) + (1-ɤ)
• π4 = - (1- α) (1-ɤ)
• et = vt - (1- ɤ)vt-1 + α ut - α(1- ɤ)ut-1 + b1 α wt
Since only the actual output rather than the optimal output is observed in reality.
The reduced form is a distributed lag model with lagged dependent variable.
23. The short-run price response of each explanatory variable is estimated directly
by its coefficient.
The long-run price response is obtained by dividing short-run
elasticities by an adjustment coefficient (the coefficient of the lagged dependent
variables).
The short run elasticity can be calculated from;
e = π2 .P/ A
where P and A are mean price and acreage respectively.
and π2 is regression coefficient.
The long run elasticity can be calculated as;
e = π2/1- π3 – π4 × P/ A
The short run and the long run elasticities of supply with respect to the
other determining variables are obtained in the same way.
24. Case study:
Leaver Rosemary (2003) studied an estimate of the price
elasticity of supply for tobacco output in Zimbabwe using an
adapted Nerlovian model and covered the period 1938 to
2000.
The supply response equation is expressed as:
LOUTPUTt = b0 + b1LREALPRICEt-1 + b2LOUTPUTt-1 +
b3 LOUTPUTt-2 + b4 QUOTA + b5 RAINt-1 +
b6 TIME + b7 TIME2 + Ut
25. The results show that price lagged one period, output lagged both
one and two periods, and the simple time trend all exert a positive
influence on tobacco production.
The negative coefficient of the quadratic time trend variable implies
that unspecified effects are causing tobacco output to increase,
although at a decreasing rate.
These variables together explain about 96 percent of the variation in
Zimbabwean tobacco output.
27. CONCLUSION
This Nerlovian Model has become popular because it
can deal with ‘expectations’ (about future factors)
whose importance in economic behaviour is being
recognised. And Nerlovian Complex Expectation
model contains an additional variable At-2 when
compared with Partial Adjustment and Adaptive
Expectation models which gives more reliable results