The Model. 7.1.1 RCC and the MOD will operate within a Public / Public partnership. The overriding objective is to ensure that the site is developed in such a way as to deliver the agreed vision for the site. Hence whatever vehicle is adopted it must ensure that RCC and MOD retain control over exactly what gets delivered.
The Model. While the historical record shows that the Federal Reserve attempted to use the nonrate terms of access to control discount window borrowing in the 1920s and early 1930s it is not clear the attempts were successful. Assessing whether changes in credit policy had a material effect on bank borrowing requires empirical tests using a model that can measure the administrative pressure applied at the discount window. A useful starting point in developing such a model is in specifying the costs to banks in meeting a reserve need. Cost-minimizing banks would weigh the cost of borrowing from the discount window against the cost of liquidating assets or borrowing from an alternative source. The cost of borrowing from the Federal Reserve is the interest paid at the discount rate, Rd, plus the implicit costs of supervisory surveillance. These costs are those resulting from the administrative pressure supervisors apply to discourage banks from engaging in arbitrage when discount rates were below market rates of interest, as they were for most of the 1920s and many months in the early 1930s. In this model the implicit costs are represented by a borrowing function in which the marginal surveillance cost of access to the discount window, c(B/K), rises with the amount borrowed, B, relative to bank capital, K. Capital is the appropriate scale variable given that Federal Reserve banks had from the start focused on borrowing relative to capital when seeking to restrain bank borrowing (Xxxxxxxxxxx, 1932, p. 44). The implicit costs may be thought of as the opportunity costs of providing the collateral for the borrowings or the capital adjustments required by supervisors whose attention is drawn to the bank by the borrowing. The cost of liquidating the assets is the foregone interest on the assets, RA, plus the transactions costs incurred in selling the assets, tc, measured as a portion of the value of the assets. Although a small market for federal funds developed in several cities in the 1920s in which banks could borrow or lend reserves, most banks met reserve drains by liquidating assets — particularly their holdings of call loans and short- term marketable securities (Xxxxxx, 1938, 93-97 and Xxxxxx, 1964) 3-13). The total cost, C, to banks of meeting their reserve need, RN, can be expressed as: where
The Model. As mentioned in the Introduction, we follow Xxxxxxxx and Xxxxxx (2000) and estimate the following two specifications of an equation designed to account for changes in employment in 3-digit ISIC (Rev. 2) manufacturing industries: LDEMPLit = β0 + β1 LDCONSit + β2 LDPRODit + x0 XXXXXxx + x0 XXXxx + uit (5) and LDEMPLit = β0 + β1 LDCONSit + β2 LDPRODit + x0 XXXXXxx + x0 XXXxx (6) + x0 (XXXxXXXXX)xx + uit where uit = μi + xxx xxx xxx ~xxx(0, x0). We assume the cross-section component μi to be fixed since the 3- digit industries that make-up the panel have not been chosen at random. Hence, both specifications are estimated using a fixed effects estimator that is, basically, OLS with cross-section dummies. The variables used may be defined as follows: LDEMPL = The natural log of the absolute value of the change in employment (L) between t and t-n. LDCONS = The natural log of the absolute value of the change in aparent consumption (C = Q + M - X) between t, t-n, Q being output. LDPROD = The natural log of the absolute value of the change in labour productivity, measured as output per worker, between t and t-n. LTREX = The natural log of trade exposure [(X+M)/Q]. IIT = May be GL, ΔGL or A. IITxLTREX = The interaction between IIT and trade exposure. LDEMPL is a proxy for the costs of adjustment in the labour market. The assumption is that the costs of moving labour across industries is proportional to the size of net changes in wage payments and, furthermore, that this proportion is the same for all industries and over time. The expected sign for the coefficient of LDCONS is positive. One would expect the coefficient of LDPROD to be negative since increases in productivity would tend to reduce the labour requirement to produce the same level of output. This expectation is supported by evidence from the accounting measure of employment change found in, e.g., Xxxxxxxx and Xxxxxx (1999) for Belgium, Xxxxxx et al. (1999) for Greece and Erlat (2000) for Turkey. The prior expectation for the coefficient of LTREX is that it should be positive since trade exposure is expected to increase inter-industry specialization pressures (Xxxxxxxx and Xxxxxx, 2000: 730). Finally, the coefficients of both IIT and IITxLTREX are expected to be negative given the smooth adjustment hypothesis. The reason for the introduction of IITxLTREX in the second specification is the expectation that IIT should matter more in sectors where the level of trade is high.
The Model. The model presented in this section is a global emission game defined by a triple G = {I, A, Πi}. The set I = {1, 2, , n} is the set of the n players, each of them representing a country. This set I is split into two subset, denoted by I1 and I2, that contain the developed countries and the developing countries, respectively. Even if more asymmetry would be more realistic, the division in two homogeneous groups is suitable to take differences into account and largely used in literature (see e.g., Xxxxxxx and de Zeeuw, 2013). An environmental coalition is then a subset C = (C1 ∪ C2) ⊆ I, where C1 ⊆ I1 is the set of developed countries in coalition and C2 ⊆ I2 is the set of developing countries in coalition. The second element of the triple G is the set of strategies A. Also this set can be written by the union of two disjoint sets A = A1 ∪ A2, where A1 and A2 contain the strategies of developed and developing countries, respectively. The strategies contained in each set Ai are given by the emissions functions of player i, that are the functions of time ei(t) such that ei(t) ≥ 0 ∀t ∈ [0, +∞). The third element of the triple G is the payoff (or welfare) function Πi, i = {1, 2}, that is a map that, for every possible strategies profile, determines the gain of each player. The production of goods and services generates benefits to the citizens of a country and, as by-product, emissions of pollution too. Calling by xx(t) the total production of goods and services for country i at time t, is it possible to write the emission of the country i as function of its own production: ei(t) = 6 Altruistic behavior and International Environmental Agreements h(yi(t)), where h is an increasing function that satisfy h(0) = 0. If the function h is also smooth, than it is possible to express the relation between production and benefit in terms of emissions directly. A very well known form for this benefit in literature (see e.g., xx Xxxxxx and Xxxxxx-Xxxxxx, 2018), expressed by Xx(ei(t)) for player i, is the quadratic and concave function Bi(ei(t)) = αiei(t) − 2 ei (t), where αi is a strictly positive parameter. The assumption of two homoge- neous kinds of players means that there are only two different values for the parameter α; α1 for each i ∈ I1 and α2 for each i ∈ I2. Moreover, the usual presumption on these parameters is that α1 > α2. The simple idea is that developed countries are able to produce more good and services for unity of pollution respect to developing countri...
The Model. Consider a population of size one of patients with a specific disease. Patients are indexed with a parameter θ that represents their personal characteristics such as age, co-morbidities, or even some analytical parameter (cholesterol level, blood pressure, biomarker, etc.). We assume that θ is distributed uni- formly within the interval [0, 1]. A pharmaceutical firm has developed a new drug whose therapeutic value has been previously proved in a clinical trial. A clinical trial is defined by {θt , q(θt )} where θt ∈(0,1) represents the characteristics of the patients above which the new drug is tested and q(θ t )∈ (0,1] is the probability that the drug is effective (that is to say, the drug cures, meaning in this setting that it restores completely the quality of life pre- vious to the disease). In other words, patients with θ ≥ θt participate in the clinical trial, and they are cured with probability q(θt ). For the sake of simplicity, we will assume that the probability that the drug cures in the clinical trial is one: q(θt ) = 1. For patients with θ < θt , the clinical trial does not provide any information about the drug efficacy. In real clinical practice, the drug can be administered to patients with θ < θt but its effectiveness is uncertain. We assume that Pr (cure θ < θ ) = θ . Thus, the drug cures with a low probability if it is ad- t
The Model. We assume a supply chain consisting of a single retailer (he) and a single supplier (she). The supplier can ship to the retailer in any period but produces only once every T periods. This is appropriate for a setting in which a supplier may manufacture different goods on a set production schedule so that she can only produce material for a given retailer once every T periods. We assume no bound on the amount produced. Alternatively, one could imagine a supplier which, because of limited availability of necessary raw materials, can produce for a given retailer only once every T periods. There is a large body of literature that discusses the benefits of sequencing multiple jobs that must use common resources over a fixed time interval. These types of problems are collectively referred to as economic lot scheduling problems or lot-sizing problems. Xxxxxxxxxx (1978) examines both analytical and heuristic techniques for finding policies under no capacity restrictions. Xxxxxx (1999a) provides additional reasons for using policies with fixed-time-review intervals as well as an excellent review of papers that use this assumption. Other references can be found in Xxxxxx (1981), which provides a review of production scheduling literature. We further assume that the retailer (under RMI) and the supplier (under VMI) follow a periodic-review inventory policy so that they examine the retailer’s inventory level at the beginning of each period, and that end-user demand occurs only at the retailer. Demand is independently and identically distributed (i.i.d.) according to the distribution function Φ(·) and density function φ(·). Excess demand at the retailer is backlogged. We allow the supplier to outsource in order to obtain material to ship to the retailer in a period in which the supplier cannot produce. In this context outsourcing could represent a form of expediting such as working overtime, producing using less efficient technology, transshipping from another location, or procuring from an outside source. Outsourcing results in an additional cost to the supplier of b0 per unit. When salvage value corresponds to production costs, both salvage value and production costs can be ignored. Therefore, we do not include them in the model. Instead, we model only the premium for outsourced goods, b0. Under both RMI and VMI, as soon as inventory arrives at the retailer, ownership of the inventory is transferred to the retailer. Thus, our situation does not represent a consignmen...
The Model. In this section we refine the formal security model which has been widely used in the litera- ture [12, 8–10, 23, 6] to analyze group key agreement protocols. In particular, we incorporate strong corruption [4] into the security model in a different way than the previous approaches by allowing an adversary to ask one additional query, Dump, and we modify the definition of freshness according to the refined model. Section 5 shows that our approach leads to much simpler security proof of the compiler presented by Xxxx and Yung [23]. U { } Participants. Let = U1, . . . , Un be a set of n users who wish to participate in a group key agreement protocol P . The number of users, n, is polynomially bounded in the security parameter k. Users may execute the protocol multiple times concurrently and thus each user can have many instances called oracles. We use Πs to denote instance s of user Ui. In initialization phase, each user Ui ∈ U obtains a long-term public/private key pair (PKi, SKi) by running a key generation algorithm G(1k). The set of public keys of all users is assumed to be known a priori to all parties including the adversary A.
The Model. The Model combines four basic elements together in an integrated agreement: basic procedural and other standard provisions needed for an agreement of limited partnership formed under the Revised Uniform Limited Partnership Act; basic business, tax and regulatory provisions commonly used in privately held limited partnerships which make long term debt and equity investments; the specific provisions that SBA requires be included in limited partnership agreements for SBICs; and the suggested wording for additional provisions that are often incorporated into a limited partnership agreement. The specific features of the Model are described briefly below: Bold and Regular Type. The Model uses two different typefaces, brackets and underscoring to assist readers in identifying (i) required provisions, (ii) suggested possible provisions if the terms are used, (iii) the general formatting of the agreement, and (iv) places where additional provisions can or may be added by a user: Bold, Arial type indicates provisions that are required. Times New Roman type indicates where optional provisions are expected to appear in the agreement, if used, and the suggested language of a particular provision. Applicants may determine the specific wording of these provisions, but SBA will not accept language that conflicts with the Small Business Investment Act of 1958, as amended, and the rules and regulations thereunder and interpretations thereof promulgated by SBA.
The Model. 2.1. The Network Model by Goyal and Joshi (2006) Formally, an international agreement between countries i and j is described by a link, given by a binary variable gij {0,1} with gij = 1 if an agreement exists between countries i and j and gij = 0 otherwise. A network gij = {( gij)ijN } is a description of the international agreements that exist among a set N = {1,…,N} of identical countries, where N is the total number of countries. Networks gc and ge are the complete network (i.e. gij = 1 for all i, j N) and the empty network (i.e. gij = 0 for all i, j N). Let G denote the set of all possible networks, g + gij denote the network obtained by replacing gij = 0 in network g by gij = 1, and g − gij denote the network obtained by replacing gij = 1 in network g by gij = 0. Let Ni(g) = {j N: gij = 1} be the set of countries with whom country i has an international trade agreement in network g. Assume that i Ni(g) so that gii = 1. The cardinality of Ni(g) is denoted i(g). In this model i(g) is also the number of active firms in country i because of the assumption that each country has only one firm (note that the domestic firm in country i is included in i(g)). Let Li(g) = {(gij)ijN : j Ni(g)} be the set of links existing in country i in network g. Note that gii Li(g). Let hi Li(g) – {gii} be a link subset, and let i be the cardinality of hi. This latter notation is used in the definition of the alternative stability concept adopted in this research. Let (g) be a subset of countries in network g. (g) is said to be a complete component if: (i) gij = 1 for all i,j (g); and (ii) gik = 0 for all i (g) and all k (g). On the other hand, (g) is said to be an incomplete component if there exists at least two countries i,j (g) such that gij = 0.
The Model. Consider a two-period world with a zero discount rate and three dates: date -1, date 0, and date 1. There are two units, A and B. Each unit's utility is a linear function of its resources at date 1 (so that there are no wealth e ects).
