Many studies have shown that consumers are strategic and forward looking, so they conclude that in the context of finite season products firms should adopt everyday low price strategy to optimize their revenue. Other studies are proponent of limited availability policy, emphasizing the effect of consumer regret. The effect of consumer regret on consumer’s decision has not been tested yet, to the best of our knowledge. In this study we structurally model and analyze the effect of consumer regret on consumer choice behavior, and firms’ revenue. Our model considers two types of consumer regret: high price regret and stock out regret. At the beginning of the first period, Consumers decide which period of two-period selling season select to purchase. Consumer experiences high price regret when she purchases the product in the first period, but the product is available in the second period with lower price. On the other hand, if consumers decide to purchase in the second period and the product is not available then, she will experience stock out regret. Consumers are uncertain about the availability of product in the second period, so they form an expectation about the availability of product. Our structural model incorporates five aspect of consumers’ decision including changing price, limited availability, consumption utility, ownership utility, and regret. We consider that consumers are heterogeneous in terms of their level of price sensitivity and regret. Our data set includes aggregate sales and inventory data. Counterfactual analysis will tell us how firms can affect consumers regret by signaling to optimize its revenue.

1. Demand Dynamics Under Consumer Regret: An
Empirical Analysis
Meisam Hejazi Nia
with Dr. Ozalp Ozer and Dr. Gonca Soysal
University of Texas at Dallas
meisam.hejazinia@utdallas.com
April 23, 2014
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 1 / 33

2. Consumers are Regretful
(a) Fashion Goods (b) Mark Down
(c) Counterfactual
Thinking
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 2 / 33

3. Motivation: Evidence from Press for Consumer Regret
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 3 / 33

4. Research Questions
Does the theory of emotionally rational consumer (consumer who regrets)
describe consumer’s choice better than theory of rational forward looking
consumer?
Are consumer’s more regretful for high price, or to fashion item
unavailablity?
Does the firm leave money on the table when it ignores consumer’s
emotion?
To what extend firm’s profitability increases, if it accounts for consumer’s
regret, in its pricing decision (counterfactual)?
How can firm leverage bounded rationality of consumer through signaling
to reap off more revenue (counterfactual)?
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 4 / 33

5. Every day Low price or Promotion Strategy?
Rational Consumers (FW LK) Emotionaly Rational Consumer (Re-
gret) + Bounded Rationality
Pricing and Markdown
Studies
Soysal and Krishnamurthi (2012), Nair
(2007),Li et al. (2009), Erdem and Keane
(1996), Sun et al. (2003), , Song and
Chintagunta (2003), Erdem et al. (2003),
Chevalier and Goolsbee (2009), Pazgal
(2008),Cachon and Swinney (2009), Levin
et al. (2009), Yin et al. (2009)
Pricing and Markdown
Theoretical Research
(Behav. Econ., Dec.
Sci., Manag. Sci.)
Ozer & Zhang(2013), Nasiry & Popescu
(2009), Rotemberg (2010), Decidue et
al. (2012), Heidhues and Koszegi 2008,
Su 2009, Engelbrecht-Wiggans and Katok
(2008), Qiu and Steiger 2011, Van de Kuilen
and Wakker (2011)
Regret in Marketing and
Psych. (Service, Product
assortment, product cus-
tomization, equity pur-
chase, choice model)
Lemon, White & Winder (2002), Gourville
& Soman (2005), Solnik (2008), Syam et al.
(2008), Thiene et al. (2012), Peluso (2011),
Pieters and Zeelenberg (2007), Zeelen-
berg and Pieters (2007), Boles and Messik
(1995), Tsiros and Mittaal (2000), Simon-
son (1992), Zeelenberg et al. (2000), Inman
and Zeelenberg (2002), Roes (1994), Bell
(1982), Keinan and Kivetz (2008), Loomes
and Sugden (1982), Smith (1996) and Yaniv
(2000), Gollier and Salanie (2006), Muer-
mann et al. (2006), Braun and Muermann
(2004),Barberis et al. (2006), Michenaud
and Solnik (2008), etc.
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 5 / 33

6. Overview of This paper
Objectives
Model demand of emotionally rational consumer (consumer who
regrets) structurally and estimate regret parameters
Test theory of emotionally rational consumer against rational forward
looking consumer theory
Analyze counterfactuals on pricing policy of the firm when consumers
are emotionally rational
Analyze counterfactuals on profit impact of signaling strategies that
affect consumer’s misperception of time and depth of markdown, and
availablity
Test hypothesis on consumer regret coefficients across fashion item
categories (i.e. cold vs. hot apparels, male vs. female apparels, simple
vs. sophisticated items)
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 6 / 33

7. Overview
Data
Leading specialty fashion apparel retailer in US
Current category under analysis: Women’s coats over a course of Two
years (105 SKU’s, PLC: 30 weeks)
Aggregate weekly sales, revenue, starting inventory, unit acquisition
cost
Methodology and Estimation
OLS for no heterogeneity
BLP and MPEC for latent type heterogeneity model (Static and
Dynamic Model)
Delta method for non-linear parameters
Fixed effect for product heterogeneity and Hierarchical Bayesian for
cross category analysis
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 7 / 33

8. Overview of Paper
Firm’s Controls:
List Price
Markdown Depth
Markdown Time
Availablity Expectation
Contigency on
Segments (Myopic,
Static, Dynamic)
Product Heterogeneity
→
Consumer’s Ex-ante:
Valuation (Ownership,
Consump. LC)
Price
Anticipated High Price
Regret
Anticipated Stock Out
Regret
Bounded Rationality
Rejoice
→
Ex-Post:
Firm’s Profit
Consumer’s Utility
Consumer’s Regret
(Cognitive Cost)
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 8 / 33

9. Evolution of Price and Sales
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 9 / 33

10. Basic Statistics: Average revenue and quantity sold at
different First Markdown Level
Relative Price (%) Revenue (%) Quantity sold (%)
70-100 0.896 1.231
60-69 0.855 1.325
50-59 0.671 1.224
40-49 0.966 2.087
30-39 1.193 3.194
20-29 1.517 6.447
< 20 0.081 0.490
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 10 / 33

11. Firm and Consumer Decisions’ Timing
Note: Consumer’s have finished search, and now they are only decide
about the Fashion good to purchase (Soysal and Krishnamurthi 2012)
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 11 / 33

12. First Model:Basic
Model
Ui1 = αi + (0.5di1 + ri di2)θ + βppi1
+αpai2(pi1 − pi2) + ξi1 + i1
i = 1..105, t = 0..2 ,
ri = 1
1.0025
di1
it ∼ EV 1(0, π2
6 ) , ξit ∼ N(0, σ2
ξ )
Ui2 =
ri (ai2(αi + 0.5di2θ + βpPi2)
+(1 − ai2)βr (0.5di1 + ri di2)θ) + ξi2 + i2
Ui0 = i0
Assumption: Two period as consumer’s are ra-
tionally bounded
i:
Product index
t:
Period index (0 for not purchase)
Uit:
Utility of Consumer for Purchase
in period (t = 0 not purchase)
Pit:
Price of product i at period t
ait:
Probability that product i is avail-
able in t: Distribution factor
ri :
Discount factor for product i
dit:
Duration of period t for product i
ξit:
Unobserved aggregate demand
shock
αi : Ownership utility
θ: Weekly consumption Utility
βp: Price Sensitivity
αp: High Price Regret
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 12 / 33

13. First Model:No Consumer Heterogeneity
Model
Ui1 = αi + (0.5di1 + ri di2)θ + βppi1
+αpai2(pi1 − pi2) + ξi1 + i1
i = 1..105, t = 0..2 ,
ri = 1
1.0025
di1
it ∼ EV 1(0, π2
6 ) , ξit ∼ N(0, σ2
ξ )
Ui2 = ri (ai2(αi + 0.5di2θ + βpPi2)
+(1 − ai2)βr (0.5di1 + ri di2)θ) + ξi2 + i2
Ui0 = i0
Assumption Counterfactual Thinking
i:
Product index
t:
Period index (0 for not purchase)
Uit:
Utility of Consumer for Purchase
in period (t = 0 not purchase)
Pit:
Price of product i at period t
ait:
Probability that product i is avail-
able in t: Distribution factor
ri :
Discount factor for product i
dit:
Duration of period t for product i
ξit:
Unobserved aggregate demand
shock
αi : Ownership utility
θ: Weekly consumption Utility
βp: Price Sensitivity
αp: High price regret
βr : Stock Out Regret
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 13 / 33

14. Second Model: Aggregate Demand with Unobserved
Demand Shocks (BLP)
Model
Uij1 = αi + (0.5di1 + ri di2)θ + βpj pi1
+αpj ai2(pi1 − pi2) + ξi1 + i1
i = 1..105, t = 0..2 , j = 1, 2
ri = 1
1.0025
di1
it ∼ EV 1(0, π2
6 ) , ξit ∼ N(0, σ2
ξ )
Ui2 = ri (ai2(α + 0.5di2θ + βpj Pi2)
+(1 − ai2)βrj (0.5di1 + ri di2)θ) + ξi2 + i2
Ui0 = i0
Assumption: Only two segment (H and L) for
ease of exposition (j = 1, 2)
Uit:
Utility of Consumer for Purchase
in period (t = 0 not purchase)
Pit:
Price of product i at period t
ait:
Availability of product i at period
t
ri :
Discount factor for product i
dit:
Duration of period t for product i
ξit:
Unobserved aggregate demand
shock
α: Ownership utility
θ: Weekly consumption Utility
βpj : Price Sensitivity
αpj : High price regret
βrj : Stock Out Regret
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 14 / 33

15. Third Model: Add Rejoice to the Model
Model
Uij1 = αi + (0.5di1 + ri di2)θ + βpj pi1
+αpj ai2(pi1 − pi2) + ξi1 + i1
i = 1..105, t = 0..2 , j = 1, 2
ri = 1
1.0025
di1
it ∼ EV 1(0, π2
6 ) , ξit ∼ N(0, σ2
ξ )
Ui2 = ri (ai2(α + 0.5di2θ + βpj Pi2+
αhj (pi1 − pi2)) + (1 − ai2)
βrj (0.5di1 + ri di2)θ) + ξi2 + i2
Ui0 = i0
Assumption: Only two segment (H and L) for
ease of exposition (j = 1, 2)
Uit:
Utility of Consumer for Purchase
in period (t = 0 not purchase)
Pit:
Price of product i at period t
ait:
Availability of product i at period
t
ri :
Discount factor for product i
dit:
Duration of period t for product i
ξit:
Unobserved aggregate demand
shock
α:
Ownership utility
θ:
Weekly consumption Utility
βpj :
Price Sensitivity
αpj :
High price regret
βrj :
Stock Out Regret
αhj :
Rejoice coefficient
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 15 / 33

16. Fourth Model: Misperception, Product Fixed Effect, New
Consumer’s Segments
Model
αi = α + α1 ∗ ci + α2 ∗ mi + α3 ∗ cli + α4 ∗ api
dei1 = di1 + κi
pei2 = pi2 + ηi
aei2 = ai2 + µi
κi , ηi , µi ∼ N(0, Σ)
α:
Ownership utility intercept parameter
αk:
Ownership utility parameter for mate-
rial, color and cloth (k = 1..3)
κi :
Misperception error for the time of
markdown
ηi :
Misperception error for the price of
item i after markdown
µi :
Misperception error for the availablity
of fashion item i after markdown
Assumption: We can have segment of Static,
Dynamic and Myopic decision
makers
ci :
per unit cost of acquistion of fash-
ion item i
mi :
material of fashion item i (i.e.
Wool, Nylon, Fur, others)
cli :
color of fashion item i (i.e. Dark,
Bright, texture, others)
api :
Type of apparel of fashion item i
(i.e. coat, jacket, suit, short, oth-
ers)
dei1:
Expectation time of markdown for
product i
pei2:
Expected price of fashion item i
after markdown
aei2:
Expected availablity of fashion
item i after markdown
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 16 / 33

17. Estimation: Aggregate Logit
Basic Model (Demand Side)
Ui1 = αi + (0.5di1 + ri di2)θ + βppi1
+αpai2(pi1 − pi2) + ξi1 + i1
it ∼ EV 1(0, π2
6 ) , ξit ∼ N(0, σ2
ξ )
Ui2 =
ri (ai2(αi + 0.5di2θ + βpPi2)
+(1 − ai2)βr (0.5di1 + ri di2)θ) + ξi2 + i2
Ui0 = i0
Estimation
Ui1 = Vi1 + i1
Ui2 = Vi2 + i2
Ui0 = i0
Sit = eVit
2
s=0 eVis
Vi1 = ln(Si1) − ln(Si0)
Vi2 = ln(Si2) − ln(Si0)
Sit = salesit
Mi
Mi = 1.25Invi
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 17 / 33

18. Estimation: Aggr. demand with unobs. demand shock
(static)
BLP type Model (Demand Side)
Ui1 = αi + (0.5di1 + ri di2)ci θ + βpj pi1
+αpj ai2(pi1 − pi2) + ξi1 + i1
it ∼ EV 1(0, π2
6 ) , ξit ∼ N(0, σ2
ξ )
Ui2 = ri (ai2(α + 0.5di2ci θ + βpj Pi2)
+(1 − ai2)βrj (0.5di1 + ri di2)ci θ) + ξi2 + i2
Ui0 = i0
L(Ω) = T
t=1 fξ(D−1
t (qt; Ω)) J
J =
∂D−1
t (qt ;Ω)
∂qit
= |∂ξit
∂qit
|
= | − ∂G/∂qit
∂G/∂ξit
= | − −1
2
k=1 Nkit Pikt (qt |Ω)[1−skit (qt |Ω)]
|
Estimation
Ω = (α, c, β, π, σxi )
δi1 = α + c + γc + βp1pi1
αp1ai2(pi1 − pi2) + ξi1
δi2 = ri (ai2(α + c + βp1pi2)
+(1 − ai2)βr1((0.5di1 + ri di2)ci θ) + ξi2
¯β2 = (β2 − β1), , β2 = (β2p, α2p, β2r )
MSi1 = π1i
exp(δi1)
2
t=0 exp(δit )
+(1 − π1i )
exp(δi1+¯βp2pi1+¯αp2ai2(pi1−pi2))
2
t=0 exp(Uit2)
MSi2 = π2i
exp(δi1)
2
t=0 exp(δit )
+ (1 − π1i )
exp(δi2+ri (ai
¯βp2pi2+(1−ai )¯βr2ai2(0.5di1+ri di2)ci θ))
2
t=0 exp(Uit2)
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 18 / 33

19. MPEC (Constraint) versus BLP (Fixed Point)
Objective Function
BLP (Fixed Point): maxβ2 L(ξ1, ξ2)
Subj. to ˆsit = sit, k < 1−10
β2 = (π, ∆βp, ∆αp, ∆βr )
MPEC (constraint): maxβ2,δ1,δ2 L(ξ1, ξ2)
Subj. to ˆsit = sit
We need to supplement analytically calculated Gradient and Hessian:
G = (G1, . . . , G4) = (∂LL
∂π , . . . , ∂LL
∆βr
)
H =
∂G1
∂π
...
∂G1
βr
...
...
...
∂G4
∂π
...
∂G4
βr
Dynamic Model
Consumer decides whether to buy now or wait:
Wijt = γaitln[exp(δj,t+1) + exp(Wijt+1)]
Wijt(St) = 1
N(ξit+1)
N(ξit+1)
n=1 Wijt
WijTj
= 0, Gausian Quadrature
Segments:
Sit = s
j=1 πj sijt
s
j=1 πj = 1
On myopic case:sijt+1 = Mi (p + qsijt)(1 − sijt)
On Multiple Period, high price regret:
αp(Pt − 1
(Ti −t)
T
τ=t i aiτ Piτ )
On Multiple Period, stock out regret:βr (1 + τ)θ
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 19 / 33

20. Estimation:Delta Method to Identify Stock Out Regret
Coefficient
Basic Model
Ui1 = α + (0.5di1 + ri di2)θ + βppi1
+αpai2(pi1 − pi2) + ξi1 + i1
it ∼ EV 1(0, π2
6 ) , ξit ∼ N(0, σ2
ξ )
Ui2 =
ri (ai2(α + 0.5di2θ + βpPi2)
+(1 − ai2)βr (0.5di1 + ri di2)θ) + ξi2 + i2
Ui0 = i0
Estimation
η = θβr
ˆV
θ
η
=
ˆσ11 0
0 ˆσ22
ˆβr = ˆη
ˆθ
ˆµ =
1
ˆθ
− ˆη
ˆθ2
V (βr ) = ˆµ ˆV
θ
η
ˆµ
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 20 / 33

21. Sources of Variation for Identification
High Price Regret: Availablity of the product (exogenous), and the
markdown amount (endogenous) → Best effort: control for remaining
inventory?
Stock out Regret: Availablity of the product (exogenous), Time of
the markdown (endogenous)→ Best effort: control for the ratio of
remain.Inv
remain.Period ?
Price sensitivity: Variation in price (endogenous) → Best effort:
control for remaining amount in inventory
Consumption utility: Length of the season (exogenous), Time of the
markdown (endogenous)→ Best effort: control for the ratio of
remain.Inv
remain.Period ?
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 21 / 33

22. Sources of Variation for Identification
Ownership utility: Accounts for product heterogeneity (product
quality)
Rejoice: Availablity of the product (exogenous), and the markdown
amount (endogenous) → Best effort: control for remaining inventory?
Unobservables: Control with market time dummy (Unobserved
demand shocks)
Consumer heterogeneity: Static, dynamic, and myopic decision
makers with different price sensitivity through BLP model
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 22 / 33

23. BLP estimation with sample size of 270K (Duration 1-2
days)
First Simulation
α θ βp αp βr
1st segment Real Parameter 2.9410 0.4640 -2.0130 -5.1110 -0.5600
Estimate (BLP) 2.8150 0.4400 -1.9230 -4.8900 -0.5710
Estimate (OLS) 2.4350 0.4060 -1.9200 -4.7700 -0.9720
2nd segment Real Parameter 2.9410 0.4640 -2.3700 -5.4220 -1.5220
Estimate (BLP) 2.8150 0.4390 -2.2740 -5.0670 -1.5270
Estimate (OLS) 2.4350 0.4060 -1.9200 -4.7700 -0.9720
Seg. size (π) Real Parameter 0.2000
Estimate 0.1960
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 23 / 33

24. BLP estimation with sample size of 270K (Duration 1-2
days)
Second Simulation
α θ βp αp βr
1st segment Real Parameter 0.5213 0.6268 -0.5472 -4.0147 -0.6571
Estimate (BLP) 0.5007 0.5904 -0.5263 -3.7821 -0.6755
Estimate (OLS) 0.2461 0.3892 -0.3849 -3.0047 -1.1299
2nd segment Real Parameter 0.5213 0.6268 -1.767 -6.1362 -1.9427
Estimate (BLP) 0.5007 0.5904 -1.7134 -5.7501 -1.9546
Estimate (OLS) 0.2461 0.3892 -0.3849 -3.0047 -1.1299
Seg. size (π) Real Parameter 0.7
Estimate 0.7004
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 24 / 33

25. BLP estimation with sample size of 270K (Duration 1-2
days)
Third Simulation
α θ βp αp βr
1st segment Real Parameter 2.197 0.7844 -2.786 -0.2877 -0.2874
Estimate (BLP) 2.1431 0.7569 -2.6749 -0.1304 -0.2357
Estimate (OLS) 1.7223 0.4753 -2.9623 -1.0273 -0.5069
2nd segment Real Parameter 2.197 0.7844 -4.358 -2.9293 -0.7278
Estimate (BLP) 2.1431 0.7569 -4.2771 -2.7287 -0.7327
Estimate (OLS) 1.7223 0.4753 -2.9623 -1.0273 -0.5069
Seg. size (π) Real Parameter 0.001
Estimate 0.001
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 25 / 33

26. MPEC estimation with sample size of 105 (Less than 5
minues)
First Simulation
α θ βp αp βr
1st segment Real Parameter 2.331 2.9486 -2.1827 -0.6108 -1.4496
Estimate (BLP) 2.3271 2.9281 -2.178 -0.58 -1.4251
2nd segment (Hetrog) Real Parameter -0.9165 -4.293 -2.6808
Estimate (BLP) -0.9165 -4.293 -2.6995
Seg. size (π) Real Parameter 0.99
Estimate 0.99
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 26 / 33

27. MPEC estimation with sample size of 105 (Less than 5
minues)
Second Simulation
α θ βp αp βr
1st segment Real Parameter 0.6739 0.167 -1.8778 -0.1797 -0.0342
Estimate (BLP) 0.6732 0.161 -1.8502 -0.1011 -0.0128
2nd segment (Hetrog) Real Parameter -2.9594 -4.0098 -0.6048
Estimate (BLP) -3.0008 -4.0051 -0.777
Seg. size (π) Real Parameter 0.8
Estimate 0.8054
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 27 / 33

28. MPEC estimation with sample size of 105 (Less than 5
minues)
Third Simulation
α θ βp αp βr
1st segment Real Parameter 1.7661 2.147 -2.2958 -1.9087 -0.8951
Estimate (BLP) 1.759 2.1164 -2.2923 -1.8109 -0.8614
2nd segment (Hetrog) Real Parameter -3.9743 -5.1279 -2.4387
Estimate (BLP) -3.9743 -5.1279 -2.474
Seg. size (π) Real Parameter 0.5
Estimate 0.5
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 28 / 33

29. MPEC estimation with sample size of 105 (Less than 5
minues)
Fourth Simulation
α θ βp αp βr
1st segment Real Parameter 2.00E+00 1.82E+00 -7.78E-02 -1.95E+00 -0.1109
Estimate (BLP) 2.0563 1.789 -0.0904 -1.8997 -0.0938
2nd segment (Hetrog) Real Parameter -3.0642 -5.2117 -2.652
Estimate (BLP) -3.0642 -5.2117 -2.6929
Seg. size (π) Real Parameter 0.3
Estimate 0.3
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 29 / 33

30. Model EStimation Performance
Model Estimation LL AIC BIC
BLP (Dynamic) 151.95 −293.91 −280.64
BLP (Static) 300.14 −590.27 −577.004
OLS -631.9 1273.8 1287.1
MPEC (Static) 7712 −1541 −1540
MPEC algorithm of estimation outperforms BLP Model estimation
algorithms
Static Model fits the data better than dynamic model (so far)
No heterogeneity model performs worse than static and dynamic
model with heterogeneity
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 30 / 33

31. Parameter Estimate
α θ βp αp βr
First segment Dynamic
(BLP)
parameter esti-
mate
-0.0077 0.025 -1.5963 0.1563 -174.4532
t-stat -1.0551 1.9099 -562.8402 21.857 -2.8659
Static Model
(BLP)
parameter esti-
mate
-0.0003 0.0742 -1.5955 0.1658 -56.5262
t-stat -0.037 5.9756 -592.4332 24.4085 -9.0029
Static Model
(MPEC)
parameter esti-
mate
-0.0581 -0.0428 0.0101 0.0078 -3.4788
t-stat -0.4943 -0.2029 0.2212 0.0674 -0.3275
Second segment Dynamic
(BLP)
parameter esti-
mate
-0.0077 0.025 0.0086 -0.0412 1.1854
t-stat -1.0551 ”1.9099 ” 15513i -816 -232i
Static Model
(BLP)
parameter esti-
mate
-0.0003 0.0742 0.0094 -0.0317 2.5927
t-stat -0.037 5.9756 0.0004 0 0
Static Model
(MPEC)
parameter esti-
mate
-0.0581 -0.0428 -0.4958 -0.4411 56.2505
t-stat -0.4943 -0.2029 -73.8071 -0.3552 6.0364
Segment size (π) Dynamic
(BLP)
parameter esti-
mate
0.1779
t-stat 588
Static Model
(BLP)
parameter esti-
mate
0.1789
t-stat 19.119
Static Model
(MPEC)
parameter esti-
mate
0
t-stat 0
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 31 / 33

32. Still A lot to do ....
Estimate model of fixed effects, rejoice, dynamic multiperiod using MPEC
estimation
Add 3 types of misperception (Markdown time and depth and availablity)
Allow model to decide on size of segment of static, dynamic and myopic
consumers
Counterfactual analysis on pricing policy when consumers are regretful,
higher regretful consumer (signaling of firm), and availablity misperception
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 32 / 33

33. Still A lot to do ....
Counterfactual analysis on pricing policy when consumers are regretful,
higher regretful consumer (signaling of firm), and availablity misperception
Robustness check on the markdet size coefficient (1.25), more segments,
only one type of regret, reduced model
Test hypothesis on product category level regret (cold vs hot apparels,
men vs women apparels, simple vs. sophisticated items)
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 33 / 33

34. Thank You
Meisam Hejazi Nia (UTD) Demand Dynamics Under Consumer Regret April 23, 2014 34 / 33