SAMPLE 2 FORM RDC/1A THE HONG KONG POLYTECHNIC UNIVERSITY RESEARCH DEGREE PROPOSAL

Transcription

SAMPLE 2
FORM RDC/1A
(Revised Oct 2006)
THE HONG KONG POLYTECHNIC UNIVERSITY
RESEARCH DEGREE PROPOSAL
Doris Wu
Note: Please note that the information given in this form will only be used for processing this application.
Layout and length
A typical PhD proposal will be somewhere between three and four thousand words (excluding
references and appendices). While we do not insist on a definite layout, applicants are
encouraged to keep the following in mind: i.e. about 13 pages with 1.5 spacing and leaving
enough margin for comments.
1.
Project Title:
Econometric Analysis of Tourist Expenditures in Hong Kong
2.
Project Objectives: (Purpose of proposed investigation)
The theory of consumer demand has been a central topic in Economics for many
years (Edgerton et al., 1996). Much effort has been exerted in bridging the gap
between the pure theory of consumer behaviour and its empirical implementations.
The formalised theory of consumer demand has stimulated the developments and
applications of econometric methodologies. Not only has the rapid expansion of
international tourism led to increasing contribution to national and global economies,
it has also motivated growing interests in tourism demand analysis among academics
and business practitioners. Tourism demand studies are of particularly importance for
the countries/regions where tourism plays a key role, such as Hong Kong. However,
those latest developments in econometric methodologies have been rarely applied to
the tourism context. The limitations of the traditional methodologies employed in
previous tourism demand analyses may have biased the empirical results, and more
seriously, led to misleading policies. This study, therefore, aims to overcome this
problem by introducing a theoretically sound and technically effective framework in
tourism demand analysis.
© No part of this proposal could be reproduced in any form without the permission of the author.
1
The empirical analysis of this study will be based on the data related to the inbound
tourism in Hong Kong. Tourism has become the second largest foreign currency
earner in Hong Kong since 1995 and the income generated from tourism has
contributed around 6% to Hong Kong’s gross domestic products (GDP) over the last
decade (Zhang et al., 2001). The service sector in Hong Kong, particularly, retailing,
accommodation, catering, arts and entertainment businesses, has either directly or
indirectly benefited from international tourism in Hong Kong (Heimstra and Wong
2003). Although the tourism industry has played an ever-increasing role in the
generation of wealth and employment in Hong Kong, it also faces critical challenges
of creating and maintaining a sustainable competitive advantage in such an
environment that other Asian destinations particularly Singapore, Taiwan, Thailand
and Mainland China are gaining a growing popularity. In order to adopt effective
tourism policies and strategies in respond to this challenge, the public agencies and
tourism businesses in Hong Kong need to have accurate knowledge about the
characteristics of its key source markets, such as their spending and seasonal
patterns, and about the determinants of these patterns. The main objectives of this
study are as follows:
i.
To model tourists’ expenditure allocation to different categories of goods and
services including shopping, hotel bills, meals outside hotels, entertainment,
tours and others, using a series of LAIDS approach.
ii.
To quantify the effects of budget (income) and price changes on tourists’
expenditures of each source market by computing expenditure and own-price
elasticities, based on the estimated TVP-EC-LAIDS models. The source
markets include Mainland China, Taiwan, Japan, USA, Singapore, UK, South
Korea and Australia.
iii.
To identify interrelationships between different categories of goods/services,
in terms of substitutability and complementarity, in each of the eight demand
system models.
iv.
To examine the effects of seasonality on tourists’ expenditures by source
market.
v.
To compare the impacts of income, price and seasonality on tourism demand
between different origin countries/regions.
vi.
To recommend appropriate tourism suggestions, which will be useful to
tourism industry stakeholders and government.
2
3.
Scope and Background of Research:
(Please identify key issues/problems to be addressed)
Decision-makers in tourist destinations, especially those destinations where tourism
is one of the major sources of foreign exchange, have put much effort in trying to
understand the key determinants of demand for their tourism products and services
in order to formulate and implement the most effective tourism policies and
strategies. Many econometric studies on tourism demand have been published over
the last three decades and they have undoubtedly made significant contributions to
the tourism demand literature (Witt and Witt, 1995). Most of those studies, however,
are based on the single-equation approach, which suffers from specific limitations.
Eadington and Redman (1991) noted that this approach is incapable of analyzing the
interdependence of budget allocations to different consumer goods/services. For
example, a decrease in hotel room rate may stimulate tourists’ spending on shopping
and entertainment. However, lacking an explicit basis in consumer demand theory,
the single-equation approach cannot adequately capture the influence of price
changes in one tourism good/service (or in a particular destination) on the demand
for other products/services (or destinations). Therefore, the demand elasticities,
specifically the cross-price elasticity, derived from the single equation approach do
not reflect the true substitute effects, which may result in unreliable policy
recommendations. Another limitation of the single-equation approach is that it cannot
be used to test for symmetry and adding-up hypotheses suggested by demand
theory.
The system of equations approach can overcome these limitations. By including a
group of equations (one for each consumer good) in the system and estimating them
simultaneously allows one to examine how consumers choose bundles of goods in
order to maximize their preference or utility given budget constraints. Although there
are a number of system modelling approaches available, the almost ideal demand
system (AIDS), introduced by Deaton and Muellbauer (1980), has been the most
commonly used method for analyzing consumers’ spending behaviour, as it has
considerable advantages over the others.
3
Although the AIDS model has received considerable attention in the economic
literature in terms of analyzing households’ demand for nondurable goods
(particularly food categories), the application of this approach to tourism demand
studies is still rare. A thorough literature search has identified the following
publications. O’Hagan and Harrison (1984), White (1985), Syriopoulos and Sinclair
(1993), Papatheodorou (1999), Papanikos and Sakellariou (1997), De Mello et al.
(2002) and Divisekera (2003) employed the static AIDS in its linearly approximated
form (LAIDS) to examine tourist expenditures in a group of destinations by a
particular origin country. Lyssiotou (2001) specified a non-linear AIDS model to study
the spending behaviour of the UK the outbound tourists. Unlike all the other studies
mentioned above, Durbarry and Sinclair (2003) estimated an error correction LAIDS
(EC-LAIDS) to analyze the demand for tourism to Italy, Spain and UK by French
residents. However, the error correction AIDS models in their study took a reduced
form as all the short-run explanatory variables were omitted due to their statistical
insignificance. Instead of focusing on tourists’ expenditure allocation to different
destinations, Fujii et al. (1985) investigated tourists’ expenditure on different
consumer goods in one particular destination. Six broad categories of goods
consumed by tourists were investigated in their study. They are (1) food and drink;
(2) lodging; (3) clothing, accessories and jewellery; (4) local transport; (5) recreation
and entertainment and (6) other.
As the authors argued, such an analysis is
particularly important in assessing the effects of public policies on the pricing of
goods/services at the tourist destinations. Moreover, the analysis of the
interrelationships between the demands for different categories of goods/services will
shed new light on identifying the comparative advantages of the various sectors,
such as retailing and lodging within the tourism industry. The AIDS model adopted by
Fujii et al. (1985) was static in its model specification, which does not reflect the
dynamic nature of tourists’ decision making and the reliability of the empirical findings
is questionable. Hitherto, there is no known published research focusing on tourists’
expenditure allocation to different product categories. The improvements of the AIDS
specification, in terms of the inclusion of error correction mechanism to capture the
dynamics of consumers’ decision making, have not benefited the tourism studies in
real sense. To bridge this gap, this project will introduce a time varying parameter
error correction model representation of the LAIDS (TVP-EC-LAIDS) to investigate
the expenditure patterns of tourists using the data related to Hong Kong inbound
tourism.
4
It is anticipated that this research will further advance the tourism demand analysis
techniques and shed more light in the understanding of tourists’ consumption
behaviour not only in Hong Kong, but also in other parts of the world. Based on the
TVP-EC-LAIDS framework, eight leading contributors to Hong Kong tourism receipts
will be included in the investigation. These contributors include Mainland China,
Taiwan, Japan, USA, Singapore, UK, South Korea and Australia. The spending of
tourists from these eight origin countries/regions accounted for about 81% of the total
tourist expenditures in Hong Kong (Hong Kong Tourism Board, HKTB, 2001). A
preliminary research has found that tourists from these origins have different
spending patterns. For example, the 2003 figures show that American tourists spent
43.2% and 31.3% of their total budget on hotel bills and shopping, respectively. On
the other hand, tourists from Mainland China spent 68.5% on shopping and only
12.3% on hotel bills. Demand from different source markets also displays different
seasonal patterns. For instance, the most popular season to visit for Chinese tourists
is December, while UK visitors peaked in March. Clearly, the spending differentials of
tourists from different countries/regions will have important influences on tourism
product provision in the different sectors of the economy. This will be systematically
analyzed in the study using the proposed modelling methodology. Consistent with the
main data source - Statistical Review of Hong Kong Tourism (Hong Kong Tourism
Board, 2003), this study will examine tourists’ consumption behaviour divided into six
spending categories. They are (1) shopping; (2) hotel bills; (3) meals outside hotels;
(4) entertainment; (5) tours and (6) others. These six categories form a complete
demand system, and the TVP-EC-LAIDS will be estimated for each of the origin
countries/regions. The expenditure and own-price elasticities will be calculated for
each system in order to examine the effects of price and budget changes on the
demand for each category of products. In addition, the cross-price elasticities will
also be estimated and this will allow the identification of the interrelationships
between the demands for different goods/services by tourists. Seasonality is also
readily accommodated in the TVP-EC-LAIDS, and its effects on tourist expenditure
will be examined in the study.
5
This study is the first attempt to introduce the TVP-EC-LAIDS to the tourism
expenditure research. Given its advantages over other LAIDS approach, the TVPEC-LAIDS will produce robust demand estimates and provide reliable information for
tourism practitioners. The research findings are expected to provide useful
information for policy makers in both the public and private sectors. For example, if
different consumption patterns are identified, a diversified marketing strategy for
different market segments would be appropriate; if different price elasticities are
observed, a flexible pricing policy for different products should be adopted. The
identification of a complementary effect between two different products suggests that
a joint marketing campaign by the two parties would be necessary.
4.
Research Methodology:
The AIDS model stems from the neoclassical theory of consumer demand. It has
been the most commonly used method for analyzing consumer behaviour as it has
considerable advantages over the others. For example, it gives an arbitrary first-order
approximation to any demand system; it has a flexible functional form and does not
impose any a priori restrictions on elasticities; it is easy to estimate and largely
avoids the need for non-linear estimation; the restrictions of homogeneity and
symmetry can be tested through linear restrictions on the parameters in the model; it
is derived from the consumer cost function corresponding to price-independent
generalized logarithmic (PIGLOG) consumer preferences, which permits an exact
aggregation over consumers without imposing identical preferences. As far as
aggregate data are concerned, a rational representative consumer is assumed to
make the budgeting allocation. Therefore, although the AIDS model is developed on
the basis of microeconomic theory, it can readily be generalized to the aggregate
level (Edgerton et al. 1996).
6
4.1 Static LAIDS
The static AIDS can be viewed as an extension of the Working-Leser model, in which
the budget share for good i is related to the logarithms of prices and total real
expenditure in the following manner:
wi = ai + ∑ γ ij log p j + bi log( x / P ) + vi
(1)
j
where wi is the budget share of the ith good, pj is the price of the jth good, x is total
expenditure on all goods in the system, P is the aggregate price index, x/P is real
total expenditure, and ai , bi and γ ij are the parameters that need to be estimated. vi
is the normal disturbance term.
The aggregate price index P in Equation (1) is defined as:
log P = a0 + ∑ α i log pi +
i
1
∑∑ γ ij log pi log p j + vi
2 i j
(2)
where a0 and α i are the parameters to be estimated. It shows that the relationship
between the price index P and the prices of individual goods is non-linear, which
results in a complicated non-linear estimation of the system. To linearise the
relationship, Deaton and Muellbauer (1980) suggested to replace the price index P
with Stone’s price index (P*) which takes the form log P* =
∑ w log p
i
i
. The linear
i
approximation of the AIDS model using this Stone’s price index is termed the LAIDS,
and is commonly used in most demand studies.
To comply with the theoretical properties of demand theory, i.e. the budget constraint
and utility maximisation, the following restrictions are imposed on the parameters in
the AIDS model:
Adding-up restrictions:
∑a
i
i
= 1,
∑γ
i
ij
= 1 , and
∑b
i
= 0 which allows for all budget
i
shares to sum to unity. Due to these restrictions, the residuals variance-covariance
matrix Ω is singular. The usual solution is to delete an equation from the system and
estimate the remaining equations, and then calculate the parameters in the deleted
equation in accordance with the adding-up restrictions.
Homogeneity:
∑γ
ij
= 0 , which is based on the assumption that a proportional
j
change in all prices and expenditure does not affect the quantities purchased. In
other words, the consumer does not exhibit money illusion.
Symmetry: γ ij = γ ji , which takes consistency of consumers’ choices into account.
7
Negativity: this requires the matrix of substitution effects to be negative semidefinite.
One subset of the negativity restriction implies that all the compensated own-price
elasticities must be negative.
Due to the flexible functional form of the LAIDS model, the elasticity analysis can be
easily carried out.
The demand elasticities are calculated as functions of the
estimated parameters, and they have standard implications. The expenditure
elasticity ( ε ix ), which measures the sensitivity of demand in response to changes in
expenditure, is calculated using ε ix = 1 + bi / wi . The uncompensated own-price
elasticity ( ε ii ) and cross-price elasticity ( ε ij ) measure how a change in the price of
one product affects the demand for this product and other products with the total
expenditure and other prices held constant. They are given by ε ii = λii / wi − bi − 1
and ε ij = λij / wi − bi w j / wi , respectively. In the same way, the compensated price
elasticities ( ε ii* and ε ij* ), which measure the price effects on the demand assuming
the real expenditure ( x / P ) is constant, are calculated as ε ii* = γ ii / wi + wi − 1
and ε ij* = γ ij / wi + w j . In particular, the sign of the calculated ε ij*
indicates the
substitutability or complementarity between the destinations under consideration
(Edgerton et al., 1996).
In the static LAIDS, it is implicitly assumed that there is no difference between
consumers’ short-run and long-run behaviour, i.e. the consumers’ behaviour is
always in “equilibrium”. However, in reality, habit persistence, adjustment costs,
imperfect information, incorrect expectations and misinterpreted real price changes
often prevent consumers from adjusting their expenditure instantly to price and
income changes (Anderson and Blundell, 1983). Therefore, until full adjustment
takes place consumers are “out of equilibrium”. This is one of the reasons why most
static LAIDS models cannot satisfy the theoretical restrictions (Duffy, 2002). It is
therefore necessary to augment the long-run equilibrium relationship with a short-run
adjustment mechanism. Moreover, the static LAIDS pays no attention to the
statistical properties of the data and the dynamic specification arising from time
series analysis. It is well known that most economic data are non-stationary, and the
presence of unit roots may invalidate the asymptotic distribution of the estimators.
Therefore traditional statistics such as t, F and R2 are unreliable, and least squares
estimation of the static LAIDS tends to be spurious (Chambers, 1993).
4.2 Two stage EC-LAIDS
8
The concepts of cointegration (CI) and the error correction model (ECM) were first
proposed by Engle and Granger (1987), and have been widely used by researchers
and practitioners in modelling and forecasting macroeconomic activities over the last
decade. Engle and Granger (1987) show that the long-run equilibrium relationship
can be conveniently examined using the CI technique, and the ECM describes the
short-run dynamic characteristics of the economic activities. If the variables in the
regression are cointegrated, the spurious regression problem will not occur.
Before examining the CI relationship, all variables concerned need to be tested for
unit roots (or orders of integration). If seasonal data are used, seasonal unit roots
should be tested. The Augmented Dickey-Fuller (ADF) statistic and the HEGY
procedure (Hylleberg et al., 1990) can be used for these tests. Once the orders of
integration of the variables have been identified, either the Engle and Granger (1987)
two-stage approach or the Johansen (1988) maximum likelihood approach can be
used to test for the CI relationship among the variables in the models (Song and Witt,
2000).
Once the CI relationship between the dependent variables and the linear combination
of independent variables in the long-run LAIDS is confirmed, an ECM presentation of
the LAIDS can be achieved and econometrically estimated by appropriate algorithms.
In this study two stages EC-LAIDS (see Chambers and Nowman, 1997; Duffy, 2002)
will be employed to examine the short-run dynamics equilibrium and it is given by
A( L)Δwi = B( L)Δzt + φ ( L)Δdumt + Γ( wt −1 − Πzt −1 − ϕdumt −1 ) + vt
where
A( L) = I + ∑li =1 Ai Li ,
B( L) = ∑im= 0 Bi Li
and
(3)
φ ( L) = ∑ is=0 φi Li
polynomials in the lag operator L. zt is a intercept vector.
are matrix
Π , ϕ and Γ are
parameter matrices, respectively. l, m and s can be determined by using order
selection techniques.
Applications of the two stages EC-LAIDS can be seen in the studies of demand for
non-durable goods and food products, such as Attfield (1997), Skjerpen and
Swensen (2000), and Fanelli and Mazzocchi (2002). Durbarry and Sinclair (2003)
and Li., et al. (2004) also introduced this method to tourism demand analysis.
9
4.3 TVP-LAIDS
The specification of the above fixed-parameter EC-LAIDS model implies the speed of
short-run adjustment is constant over time. However, in practice this assumption
seems to be too strict. Therefore, it is more easily understandable to specify models
with time varying parameter (TVP) form to analyse the long-run equilibrium and
short-run dynamics of economics phenomenon. TVP technique relaxes the fixed
parameter restriction and its long-run form has been successfully applied to
economic studies. Recently TVP-EC model receives more attention. However, up to
now there are rarely attempt to combine TVP WITH LAIDS model. The exceptions
include that Leybourne (1993a, b) who estimate a TVP version of AIDS on an
equation-by-equation version. Mazzocchi (2003) explores the TVP ALDS model with
full implementation of the time varying homogeneity and symmetry constraints. Li et
al. (2006) specifically advanced the forms of TVP long-run LAIDS (TVP-LR-LAIDS)
and TVP-EC-LAIDS and originally applied them in the context of tourism forecasting
study. At the same time, Mazzocchi (2006) also employed EM (expectation and
maximisation) algorithm for the construction of long-run and short-run TVP AIDS
model in the study of UK demand for alcohol and tobacco.
4.3.1 TVP-LR-LAIDS
The Equation (1) can be represented as a time varying parameter form and each
equation of the system can be written in the following one-dimension state space
form:
wit = zt′π it + ϑi dumt + uit
(4)
uit ~ N (0, H t ), t = 1,..., T
(5)
π it + 1 = π it + ξit
(6)
π 1 ~ N (c1 , P1 ), ξit ~ N (0, Qt )
(7)
where wit and uit are the ith elements of wt and ut respectively; z′ is the transpose
of matrix z. ϑi is a q-vector of disturbance terms, π it is an unobserved state vector
following a multivariate random walk. H t and Qt are initially assumed to be known.
10
Correspondingly, the whole system can be written as:
wit = zt∗πΠ ∗t + ϑdumt + uit
(8)
Π ∗t + 1 = Π ∗t + ξt∗
(9)
where
zt∗ = I n ⊗ zt′
Π ∗t = (π 1t ,π 2 t ,...,π nt )′
ξt∗ = (ξ1t , ξ 2 t ,..., ξ nt )′ . Combine with the
restrictions that M = GΠ ∗t where G is the coefficient matrix of the restriction, the
equation (8) can be rewritten as follow:
Wt = zt∗Π ∗t + Dt + U t
(10)
where Wt = ( wt M )′; Z t∗ = ( zt∗ G )′; Dt = (ϑdumt ); U t= (ut 0 )′ .
This TVP-LR-LAIDS model broke the limitations generated from fixed-parameter
modelling. Therefore it seems easier to reflect the real economics phenomenon.
However, its assumption that consumers’ behaviour is always “in equilibrium” is not
much realistic because many factors such as habit persistence and imperfect
information always cause consumers not to be an equilibrium place until full
adjustments take place. As a result, there is the feasibility to construct TVP short-run
dynamics model, specifically TVP-EC-LAIDS model in this study, within the long-run
equilibrium framework.
4.3.2 TVP-EC-LAIDS
In the estimation of the TVP-LR-LAIDS and TVP-EC-LAIDS models, the Kalman filter
algorithm (Kalman, 1960) is employed.
Each equation of the unrestricted TVP-EC-LARDS can be described as:
Δwit = ( ztΔ )'π itΔ + θ i dumt + u Δit
(11)
πΔ =πΔ +ξΔ
(12)
it +1
it
it
where ztΔ = (Δzt , wt −1 − Πzt −1 )' ; π itΔ is the corresponding parameter vector; θ i is the
ith row of θ ; u Δit is the ith item of u Δt , the disturbance vector of the measurement
equation; and ξ itΔ is the disturbance vector of the state equation.
11
The state space from of the whole unrestricted TVP-EC-LAIDS is specified as
follows:
Δwt = ( ztΔ ∗ )'π Δit + θ i dumt + u Δit
(13)
Π tΔ+ 1 = Π tΔ + ξt∗Δ
(14)
where ztΔ ∗ = I q + 1 ⊗ ( ztΔ )′; Π tΔ = (π 1Δt , π 2Δt ,..., π ntΔ )' ; ξ tΔ ∗ = (ξ 1tΔ , ξ 2tΔ ,..., ξ ntΔ )′
Different from the fixed-parameter models, the TVP-EC-LAIDS model recursively
refines the parameters through Kalman filter algorithm and allows parameters in
econometric models to vary over time. As a result, in TVP-EC-LAIDS model the
dynamics of changing economic regimes can be readily accommodated and it is
likely to generate more accurate forecasts. However, in Li et al. (2006) only
unrestricted TVP-LR-LAIDS and TVP-EC-LAIDS equation by equation are estimated
using Kalman filter algorithm due to the insufficient observations. This study will
extend the empirical field by estimating the whole system using homogeneity-andsymmetry-restricted TVP-EC-LAIDS model in the context of tourism expenditure. At
the same time, fixed-parameter static LAIDS and two stages EC-LAIDS models will
also be employed for the comparison of estimating accuracy.
4.4 Data Description
In this study, quarterly data will be collected from official sources. The number of
observations is subject to data availability, and a period of 20 years (80 quarters) is
expected to be covered in this study. Eight leading tourist spenders (Mainland China,
Taiwan, Japan, USA, Singapore, UK, South Korea and Australia) will be examined.
The data of their spending on shopping, hotel bills, meals outside hotels,
entertainment tours and other items will be collected from the Hong Kong tourism
Board publications. With regard to the price variables, the aggregation of detailed
categories of tourist expenditures is necessary to form the price indices for the six
broader classes. For aggregating the component price indices, Tornqvist (1936) price
index will be used. The advantage of this method is that it allows for moving weights.
It has been applied by Fujii et al. (1985). The raw data of individual prices will be
collected from Hong Kong Census and Statistics Department.
12
4.5 Research Plan
This project is planned for completion in two and half years. Specifically, the tasks for
this period are divided into five sub-periods:
1
○
2
○
Six Months
Six Months
4
○
Eight Months
3
○
5
○
Four
Months
Ten Months
①.
Literature review
②.
Data collection and preliminary data analysis
③.
Construction of the models
④.
Model estimation and analysis of the results
⑤.
Summarize and write up research finding
13
5.
Project Significance and Value:
Econometric analysis of tourism demand has been overwhelmingly dominated by the
single-equation approach. This approach, however, suffers from various theoretical
and technical problems which often lead to results which are less than accurate and
imperceptibly defensible. This study aims to overcome some of the basic limitations
of single equation models by employing a dynamics-enhanced version of the Almost
Ideal Demand System (AIDS) model, more specifically known as the time varying
parameter error correction linear almost ideal demand system (TVP-EC-LAIDS) to
investigate the effects of various influencing factors on tourist expenditure in Hong
Kong. This theoretically superior alternative approach, developed by Li, et al. (2006),
has rarely been applied to the empirical studies with the only one exception
(Mazzocchi, 2006) and has never been used to the tourism expenditure study. This
study will be the first attempt to fill in the gap in the tourism literature.
This study attempts to apply this technique into the context of tourist expenditure
allocation amongst different product categories. Particular focus will be specified with
regard to the key source markets’ expenditure allocation to different categories of
goods/services such as shopping, hotel bills, meals outside hotels, entertainments
and tours. This study aims to quantify the effects of price and budget changes on
tourists’ expenditures of each source market and identify the interrelationships
between different categories of goods/services, using TVP demand system models.
Comparisons of the impacts of income, price and seasonality effects on tourism
demand between different countries of origin will also be studied. The empirical
results will provide useful information for key tourism players and public agencies in
their formulation as well as evaluation of effectiveness of their tourism policies.
14
Reference
The research proposal should include a reference list containing all the reference cited in the
proposal using a consistent and appropriate format such as indicated in the APA style guide.
6.
References
Anderson, G. and Mizon, G. (1983). Parameter constancy tests: Old and new.
Southampton University Discussion Paper, no. 8325.
Attfield, C. L. F. (1997). Estimating a cointegrating demand system. European
Economic Review, 41, 61-73.
Blanciforti, L., Green, R., & King, G. (1986). U.S. consumer behavior over the postwar period: An almost ideal demand system analysis. Monograph 40
(Giannini Foundation of Agricultural Economics, University of California).
Chambers, M. J. (1993). Consumers’ demand in the long run: Some evidence from
UK data. Applied Economics, 25, 727-733.
De Mello, M., Pack, A., & Sinclair, M. T. (2002). A system of equations model of UK
tourism demand in neighbouring countries. Applied Economics, 34, 509-521.
Deaton, A., & Muellbauer, J. (1980). An almost ideal demand system. American
Economic Review, 70, 312-326.
Divisekera, S. (2003). A model of demand for international tourism. Annals of
Tourism Research, 30, 31-49.
Duffy, M. (2002). Advertising and food, drink and tobacco consumption in the United
Kingdom: A dynamic demand system. Agricultural Economics, 1637, 1-20.
Durbarry, R. & Sinclair, M. T. (2003). Market shares analysis: The case of French
tourism demand. Annals of Tourism Research, 30, 927-941.
Edgerton, D. L., Assarsson, B., Hummelmose, A., Laurila, I. P., Rickertsen, K., &
Vale, P. H. (1996). The econometrics of demand systems with applications to
food demand in the Nordic countries. London: Kluwer Academic Publishers.
Engle, R. F., & Granger , C. W. J. (1987). Cointegration and error correction:
Representation, estimation and testing. Econometrica, 55, 251-276.
Hiemstra, S., & Wong, K. F. (2002). Factors affecting demand for tourism in Hong
Kong. Journal of Travel and Tourism Marketing, 13, 43-62.
Hylleberg, S., Engle, R. F., Granger, G. W. J., & Yoo, B. S. (1990). Seasonal
integration and cointegration. Journal of Econometrics, 44, 215-238.
Johansen, S. (1988). A statistical analysis of cointegration vectors. Journal of
Economic Dynamics and Control, 12, 231-254.
15
Li, G., Song, H., & Witt, S. F. (2004). Modeling tourism demand: A dynamic linear
AIDS approach. Journal of Travel Research, 43, 141-150.
Li, G., Song, H., & Witt, S. F. (2005). Time varying parameter and fixed parameter
linear AIDS: An application to tourism demand forecasting. Journal of Travel
Research, 22, 57-71.
Lyssiotou, P. (2001). Dynamic Analysis of British Demand for Tourism Abroad.
Empirical Economics, 15, 421-436.
Mazzocchi, M. (2006). Time patterns in UK demand for alcohol and tobacco: An
application of the EM algorithm. Computational Statistics & Data Analysis, 50,
2191-2205.
Mazzocchi, M., & Fanelli, L. (2002). A cointegrated VECM demand system for meat
in Italy. Applied Economics, 34, 1593-1605.
Mazzocchi, M., & Lobb, A. E. (2005). A latent-variable approach to modeling multiple
and resurgent meat scares in Italy. The 11th International Conference of the
EAAE. Copenaghen, Denmark.
O’Hagan, J. W., & M. Harrison, J. (1984). Market shares of US tourism expenditure in
Europe: An econometric analysis. Applied Economics, 16, 919-931.
Papanikos, G. T., & Sakellariou. C. (1997). An Econometric application of the almost
ideal demand system model to Japan’s tourist demand for ASEAN
destinations, Journal of Applied Recreation Research, 22, 157-172.
Papatheodorou,
A.
(1999).
The
demand
for
international
tourism
in
the
Mediterranean region. Applied Economics, 31, 619-630.
Phillips, P. C. B. (1991). Optimal inference in cointegrated systems. Econometrica,
59, 283-306.
Phillips, P. C. B. (1994). Some exact distribution theory for maximum likelihood
estimators
of
cointegrating
coefficients
in
error
correction
models.
Econometrica, 62, 73-93.
Ray, R. (1985). Specification and time series estimation of dynamic Gorman polar
form demand systems. European Economic Review, 27, 357-374.
Skjerpen, T., & Swensen, A. R. (2000). Testing for long-run homogeneity in the linear
almost ideal demand system: An application on Norwegian quarterly data for
nondurables. Discussion Paper 289, Statistics Norway.
Song, H., & Witt, S. F. (2000). Tourism demand modelling and forecasting: Modern
econometric approaches. Pergamon: Oxford.
Song, H., Liu, X., & Romilly, P. (1997). A comparative study of modelling the demand
for food in the United States and the Netherlands, Journal of Applied
Econometrics, 12, 593-611.
16
Song, H., Romilly, P., & Liu, X. (1999), An empirical study of outbound tourism
demand in the UK. Applied Economics, 32, 611-624.
Song, H., & Witt, S. F. (2000). Tourism demand modeling and forecasting - Modern
econometric approaches. Pergamon: Oxford.
Song, H., & Witt, S. F. (2003), General-to-specific modelling to international tourism
demand forecasting. Journal of Travel Research, 42, 65-74.
Song, H., Witt, S. F., & Jensen, T. C. (2003a). Tourism forecasting: Accuracy of
alternative econometric models. International Journal of Forecasting, 19, 123141.
Song, H., Witt, S. F., & Li, G. (2003b). Modelling and forecasting demand for Thai
tourism. Tourism Economics, 9, 363-387.
Song, H., & Wong, K. F. (2003). Tourism demand modeling: A time-varying
parameter approach. Journal of Travel Research, 42, 57-64.
Song, H., Wong, K. F. & Chon, K. S. (2003c). Modelling and forecasting the demand
for Hong Kong tourism. International Journal of Hospitality Management, 22,
435-51.
Syripopoulos, T., & Sinclair, T. (1993). A dynamic model of demand for
Mediterranean countries. Applied Economics, 25, 1541-1552.
Tornqvist, L. (1936). The bank of Finland’s consumption price index. Bank of Finland
Monthly Bulletin, 10, 1-8.
White, K. (1985). An international travel demand model: US travel to western Europe.
Annals of Tourism Research, 12, 529-545.
Wong, K. F. (1997a). An investigation of the time-series behaviour of international
tourist arrivals, Tourism Economics, 3, 185-199.
Wong, K. F. (1997b). The relevance of business cycles in forecasting international
tourist arrivals, Tourism Management, 18, 581-586.
Zhang, Q. H., Wong, K. F., & Or, Y. S. (2001). An analysis of historical tourism
development and its implications to the tourism industry in Hong Kong. Pacific
Tourism Review, 5, 15-21.
17
Other useful information
References
Cantor, J. (1993). A Guide to Academic Writing. London: Greenwood Press.
Huff, A. (1999). Writing for Scholarly Publication. London: Sage Publications.
Luey, B. (1990). Handbook for Academic Authors. Cambridge: Cambridge University Press.
Rountree, K. & Laing, T. (1996). Writing by Degrees. A Practical Guide to Writing
Theses and Research Papers. Auckland: Longman
Rudestam, K., & Newton, R. (1992). Surviving Your Dissertation: A Comprehensive Guide to
Content and Process. Newbury Park: SAGE Publications.
Thomas, L. (1985). Completing Dissertations in the Behavioral Sciences and Education: A
Systematic Guide for Graduate Students. San Francisco: Jossey-Bass
(Above references were retrieved from the Department of English
http://www.engl.polyu.edu.hk/eeprs/Ref_theses%20and%20dissertations.htm#Books_T&D)
18
7.
Details of Any External Collaboration:
In these circumstances, are there likely to be any complications associated with the
publication of your thesis? Give details.
8.
Research Ethics/ Safety Approval
(Please read the policy and procedures for safety approval available at the Health,
Safety & Environment Office Homepage. Please attach approval letter where
appropriate.)
I/We confirm that approval:
* has been
obtained
* is not required * will be obtained
before the start
of the project
Human Research Ethics
Animal Research Ethics
Biological Safety
Ionizing Radiation Safety
Non-ionizing Radiation Safety
Chemical Safety
(* Please tick as appropriate)
19
9.
Statement of the Applicant
I wish to register for a research degree on the basis of the proposal given in this
Form (RDC/1A).
I understand that, during the period of my registration with the University, I may not
be a candidate for any other degree or award.
I understand that, except with the specific permission of the Research Committee, I
must prepare and defend my thesis in English. (You are required to seek permission
if another language, which is considered more appropriate to the subject, is to be
used in the presentation of the thesis. Please submit the justification together with
this application)
I undertake to abide by the general regulations of the University.
Signature
Date
(Student Applicant)
Name of Applicant
10.
Endorsement by the Proposed Chief Supervisor
Signature of Chief Supervisor
Department/School
Name of Chief Supervisor
Date
[Please send this form, application form (for admission to the Degree of
MPhil/PhD), and Form RC/1 to the Head of Department/Director of School for
completion of Section 10.]
11.
Recommendation of Head of Affiliated Department in the University
I support this application, and confirm to the best of my knowledge that adequate
facilities as requested in Form RC/1 will be provided to enable the student to conduct
and complete the research programme in an efficient and safe manner. I also agree
to provide adequate research space for the applicant during the research
programme.
Signature
_____
Date
(Head of Department)
[After completing all sections, please return this form to the Research Office]
RDC/1A
20

SAMPLE 2 FORM RDC/1A THE HONG KONG POLYTECHNIC UNIVERSITY RESEARCH DEGREE PROPOSAL

Transcription

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