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In this thesis, we concentrate our research on the Paris region, a vibrant and innovative region with over 5,6 million jobs, 37 percent of French executives, and 40 percent of national workforce in research and development. It is the first R&D hub in Europe and the third worldwide. 11.7 million people or over 19 percent of the country's population reside in the area which occupies only 2.2 percent of the surface of France. The Paris region is the third World touristic destination (in 2013) (Global Destination Cities Index 2015) with 16 millions of visitors from abroad. The GDP of the region amounts to 29 percent of total French GDP (IAU IdF, 2014) %31 percent of total French GDP (http://www.grand-paris.jll.fr/fr/paris/chiffres-cles/) that is 612 milliards euros (2012). It is 1st European city considering the number of firms classified in Fortune 500 (July 2014).   Yet, the Paris region’s economy is spatially unbalanced (Combes et al., 2011). The region is highly heterogeneous, especially regarding economic activity. While few municipalities host a large number of new establishments, others struggle to be chosen by any, and a large group of municipalities is left with no new creation.Depending on the analyzed sector, the percentage of municipalities left with no new establishment creation ranges from 34 percent up to 69 percent.  When the observed data display a higher fraction of zeros than would be typically explained by the standard count data models, two types of models can be suggested: the hurdle model (Mullahy, 1986) or the zero-inflated model (Lambert, 1992). The hurdle model, also called the two-part model, model  reflects a two-part decision making process. It relaxes the assumption that the zero observations and the positive observations come from the same data generating process.The two-step decision-making process is reflected through the hurdle model interpretation.  Much work has been done in the domain of location choice models, however, several issues arise when analyzing involved phenomena, which scholars have yet to fully explore: inquired:  1) addressing the excess of zeros problem in the location choice model in highly heterogeneous geographic areas and 2) determining an appropriate way to accommodate spatial effects for in  location decisions. We respond to the complaint voiced by Liviano-Solis and Arauzo-Carod (2013) and Bhat et al. (2014)who notice  that heretofore the hurdle model technique has not been fully explored well investigated  when analyzing location phenomena. patterns.  These are the first challenges that we face in the first chapter of this thesis. 2)   %When selecting the appropriate location in which to set up in the market, an establishment may consider not only the characteristics of a particular area, but also the characteristics of neighboring zones.   The second chapter extends the research on the hurdle model and the study presented in the first part of this thesis. The final decision of an establishment seems to be related to the surrounding economic landscape. When accounting for the linkage between neighboring observations, the decision on the spatial weight matrix specification should be made. Yet, since there exist no solitary claim on the concept of space, the form of the weight matrix is largely debated. One of the problems hides in the definition of distance usually based on the straight-line segment connecting two locations. Euclidean distance is typically used and has been utilized in the first chapter to account for spatial spillovers in location choice model. However, Euclidean distance is believed to be only one simplistic possibility out of an infinite number of shortest path relations. Other alternative distance metrics may be proposed when building the spatial distance weight matrices.  When accounting for the linkage between neighboring observations since the final decision of an establishment should be related Geographic factors such as terrain, land cover, infrastructure, and traffic congestion may cause agents not  to follow pure Euclidean relations. The Euclidean distance might thus not always be  the surrounding economic landscape, we need to decide most relevant one depending  on a given problem. Interest in this question dates at least to  the spatial weight matrix specification. Yet, since there exist no solitary claim 1960s and research  on network models in geography (Haggett 1967). There are insights to be gained by mindfully reconsidering and measuring distance. The second chapter investigates establishments location decisions in  the concept of space, Paris region where high congestion, speed limits, or physical uncrossable barriers, such as rivers or industrial corridors can diminish or totally eliminate  the form linkage between neighboring areas. Rather than imposing a restrictive structure  of the weight matrix matrix, this research proposes a flexible toolkit to point which distance metric  is largely debated. One of the problems hides in more appropriate to correctly account for  the definition surrounding economic landscape. A probabilistic mixture  ofdistance usually based on the straight-line segment connecting  two locations. Extending the research presented in the first chapter of this thesis, where Euclidean ”mono-distance” hurdle-Poisson models is developed. Each model’s latent class uses a different  distance was used representation  to account for spatial spillovers incorporate spillover effects  in location choice model, other alternative choices of establishments from several activity sectors. Seven  distance metrics are considered: Euclidean distance, two road distances (with and without congestion), public transit distance, and the corresponding travel times. This methodology allows  to be proposed when building capture  the spatial distance weight matrices. diversity of agents’ behavior, i.e., to distinguish establishments which are more time- or more distance-oriented given location.  Geographic factors such as terrain, land cover, infrastructure, and traffic congestion may cause agents not to follow pure Euclidean relations. Euclidean distance is believed to be only one simplistic possibility out of an infinite number of shortest path relations. The Euclidean distance might thus not always be the most relevant one depending on the problem considered. Interest in this question dates at least to the 1960s and research on network models in geography (Haggett 1967). There are insights to be gained by mindfully reconsidering and measuring distance depending on a given problem. The second chapter investigates establishments location decisions in the Paris region where high congestion, speed limits, or physical uncrossable barriers, such as rivers or industrial corridors can diminish or totally eliminate the linkage between neighboring areas. Rather than imposing a restrictive structure of the weight matrix, this research proposes a flexible toolkit to point which distance metric is more appropriate to correctly account for the surrounding economic landscape. A probabilistic mixture of two ”mono-distance” hurdle-Poisson models was developed. Each model’s latent class uses a different distance representation to incorporate spillover effects in location choices of establishments from several activity sectors. Seven distance metrics were considered: Euclidean distance, two road distances (with and without congestion), public transit distance, and the corresponding travel times. This methodology allows to capture the diversity of agents’ behavior, i.e., to distinguish establishments which are more time- or more distance-oriented given location.   3)   In the third chapter we further enhance the literature on the location choices, this time incorporating strategic interactions among establishments.   %In spite of recognizing the importance of incorporating spatial effects in establishments location decision processes, the literature is still scarce on previous attempts.   3)   In the third chapter we further enhance the literature on the location choices, this time incorporating strategic interactions among establishments.  We shed light on strategic interactions, fundamental in establishments’ location choices, yet largely unheeded in the empirical literature. If establishments acted in isolation, it would be a relatively simple matter task  to adapt existing discrete-choice models. Yet, being non-strategic means that a firm an establishment  ignores other players’ decisions. Less is known about how to correctly adapt location choice models to study establishments’ discrete choices when they are interrelated. In very sparse empirical applications, when locational choice models are developed for several activity sectors, each of the model is typically run independently. What makes these discrete choices particularly interesting and challenging to analyze is that decisions of a particular establishment are interrelated with choices of the others because an establishment accounts for the actions of other agents when making its own decisions (Draganska et al., 2008). These thorny problems posed by the interdependence of decisions generally cannot be assumed away, without altering the realism of the model of establishment decision making (Berry and Reiss, 2007). The conventional approaches to location selection, i.e., traditional theory and methods, fail (Thill, 1997) by providing only a set of systematic steps for problem-solving without considering strategic interactions between the establishments in the market. Being non-strategic would mean that an establishment ignores other players’ decisions (Toivanen and Waterson, 2005). A properly appropriately  specified model of simultaneous entry or location decisions needs to recognize this interdependence of profits (Berry and Reiss, 2007).  There is a need for more realistic studies of complex establishment’s decision-making processes. Even though the computational burden imposed by these models considering strategic interactions is relatively high, it seems that the costs imposed are more than offset by the benefits that accumulate/accrue (Draganska et al., 2008).  Strategic interactions have been largely unsung in the empirical analyses since the year 1929 when Hotelling (1929) brought the discussion in the industrial organization literature. Most of the papers are less than a decade old (Bajari et al., 2013). This literature is in its infancy, in part, due to the complexity of expressions for the probabilities used in the models which increases45  along with the number of locations and establishment types (Draganska et al., 2008). We estimate a static discrete game of incomplete information to obtain a Bayesian Nash Equilibrium at the group level using data at the aggregate level. We permit asymmetries across establishment types in the impact of interaction effects and exogenous market characteristics. We develop one location choice model which embraces seven individual models for seven establishment types run simultaneously to account for interactions from all the types on each other.  
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4) The motivation of the last chapter comes from the fact that most previous discussion on locational decisions has one common feature of making unrealistic and restrictive assumptions and perceives the industry in terms of independent stores. The analysis of multi-store competition has started already with the trailbraking work of Teitz (1968) who introduced the idea that a firm can open multiple facilities in the context of Hotelling’s linear city model and serves the market from a number of locations. Yet, the subject of location of competing firms with multiple component units seems to have been largely unsung/unheeded in the spatial location literature (Peng and Tabuchi, 2007). This gap is inquisitive/inquiring for the systems which dominate in the market (Karamychev and van Reeven, 2009; Iida and Matsubayashi, 2011 ; Janssen et al., 2005 ; Pal and Sarkar, 2002 ; Peng and Tabu- chi, 2007). The conventional single-store location theory may not apply to situations wherein individual stores are part of larger organizations under common strategy, intuition, and control, where a centralization is applied to reach global goals and consider the interest of a firm as a whole (Thill, 1997). Conceptually, a firm selects a distribution of locations instead of choosing a point location (Chu and Lu, 1998).  The analysis of multi-store competition has started already with the trailbraking work of Teitz (1968) who introduced the idea that a firm can open several facilities in the context of Hotelling’s linear city model and serves the market from multiple locations. Yet, the subject of location of competing firms with multiple component units seems to have been largely unsung/unheeded in the spatial location literature (Peng and Tabuchi, 2007). This gap is inquisitive/inquiring for the systems which dominate in the market (Karamychev and van Reeven, 2009; Iida and Matsubayashi, 2011 ; Janssen et al., 2005 ; Pal and Sarkar, 2002 ; Peng and Tabu- chi, 2007).   Our main motivation for this chapter is to combine a number of novel solutions into one model and to rectify several mathematical and methodological misconceptions made in numerous existing store-location papers. We incorporate strategic interactions between stores within the same firm and stores that belong to different chains. We consider spatial competition, business stealing and learning effects. We pay a particular attention to correctly capture market segments and to select potential customer groups by observing their characteristics, their mobility patterns, their trip chaining behavior, and activities’ purposes during the day and during the week. A clear distinction between a daytime and nighttime population present in a particular area is needed, and therefore a more appropriate distance measure to a store traveled by a potential customer is carefully proposed and applied in a more realistic manner than it has been done in the existing literature. Further, we consider the markets as being interdependent. A combination of all these elements can create a more authentic/more realistic and original model.  The conventional single-store location theory may not apply to situations wherein individual stores are part of larger organizations under common strategy, intuition, and control, where a centralization is applied to reach global goals and consider the interest of a firm as a whole (Thill, 1997). Conceptually, a firm selects a distribution of locations instead of choosing a point location (Chu and Lu, 1998).  Our main motivation for this chapter is to combine a number of novel solutions into one model and to rectify several mathematical and methodological misconceptions made in numerous existing store-location papers. We incorporate strategic interactions between stores within the same firm and stores that belong to different chains. We consider spatial competition, business stealing and learning effects. We pay a particular attention to correctly capture market segments and to select potential customer groups by observing their characteristics, their mobility patterns, their trip chaining behavior, and activities’ purposes during the day and during the week. A clear distinction between a daytime and nighttime population present in a particular area is needed, and therefore a more appropriate distance measure to a store traveled by a potential customer is carefully proposed and applied in a more realistic manner than it has been done in the existing literature. Further, we consider the markets as being interdependent. A combination of all these elements can create a more authentic/more realistic and original model. 
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