An Empirical Investigation of Composite Product Choice

Prior ingredient branding research has examined the influence of “stated” factors such as fit between partner brands on composite product (e.g., Tide with Downy fabric softener) attitudes. This research focuses on choice of composite products, and addresses three managerially relevant questions: Which consumer segments are more likely to adopt the composite product? Will the choice of the composite product have positive or negative reciprocal effects on partner brands? Will the introduction of the composite product benefit the primary or the secondary brand more? The authors use a brand choice model to investigate the “revealed” choice of complements-based composite products. Study results indicate that (i) despite high fit between the composite product and the primary brand, consumer segments may have different choice likelihoods for these products, whereas prior research suggests equal likelihood; (ii) the choice of a composite product may not provide a positive reciprocal effect to the secondary brand; and (iii) the introduction of a composite product may benefit the primary brand more than the secondary brand, whereas prior research suggests a symmetrical benefit for the partner brands. Finally, the finding that introducing a composite product may not cannibalize the sale of the primary brand extends the ingredient branding literature, which has been silent on this issue.     

经济学视角下自然保护区有效管理行为研究——以甘肃白水江自然保护区李子坝社区为例

以甘肃白水江自然保护区李子坝社区巡护监测为例,从经济学的视角,运用比较优势和竞合模型分析该社区巡护监测与专业巡护监测的两种行为。模型分析显示两者各有优势,在专业巡护监测的基础上,如果辅之以社区巡护监测能更有效地促进环境与生物的多样性的保护,既可以提升有效管理的水平,又可以达到社区保护利益的最大化。     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?     

Oil risk contracts,business conduct and performance patterns: Data panel analysis

Using the economic and financial performance data of international companies for the exploration, and exploration–extraction (E&E) of oil, as well as the patterns of institutional situation and orientation with the government market and national oil companies or NOCs that receive project offers, we analyze the institutional development and behavior patterns by type of E&E contract, following the strategic actor approach, or the so-called agency theory. Additionally, in light of Mexico's energy reform being implemented between 2015 and 2019, we analyzed the types of license contracts compared to those for production and shared profit. Subsequently, it was determined-through panel data methods in the analysis of 17 companies between 2005 and 2015-that global companies present bigger yields and commitments compared to specialized companies, confirmed by their net income and returns on equity or ROE.     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.     

Econometrics and equilibrium models

In Rational Econometric Man, Edward Nell and Karim Errouaki present a welcome and timely case for the view that econometrics and econometric model-building may not be the magic tools to solve all empirical questions despite what many seem to have thought they were in the 1960s. Here I examine some possible problems with econometric models that have to do with their usually taking the form of equilibrium models. Some of these problems were recognized by Trygve Haavelmo decades ago. And as Aris Spanos has recently discussed, the problems are often the result of what we say in our textbooks. Some problems have to do with what we mean by econometric parameters and others with how we use probabilities.     

DEMAND,CUSTOMER BASE‐LINE AND DEMAND RESPONSE IN THE ELECTRICITY MARKET: A SURVEY

The increasing use of demand‐side management, as a tool to reliably meet electricity demands at peak time, has stimulated interest among researchers, consumers and producer organiza‐tions, managers, regulators and policymakers. This research reviews the growing literature on models which are used to study demand, customer base‐line (CBL) and demand response in the electricity market. After characterizing the general demand models, the CBL, based on which the demand response models are studied, is reviewed. Given the experience gained from the review and existing conditions, the study combines an appropriate model for each case for a possible application to the electricity market; moreover, it discusses the implications of the results. In the literature, these aspects are studied independently. The main contribution of this survey is attributed to the treatment of the three issues as sequentially interdependent. The review is expected to enhance the understanding of the demand, CBL and demand response in the electricity market and their relationships. The objective is conducted through a combination of demand and supply side managements in order to reduce demand through different demand response programs during peak times. This enables electricity suppliers to save costly electricity generation and at the same time reduce energy vulnerability.     

Contagion risk in the interbank market: a probabilistic approach to cope with incomplete structural information

One lesson of the financial crisis erupting in 2008 has been that domino effects constitute a serious threat to the stability of the financial sector, i.e. the failure of one node in the interbank network might entail the danger of contagion to large parts of the entire system. How important this effect is, depends on the exact topology of the network on which the supervisory authorities have typically very incomplete knowledge. In order to explore the extent of contagion effects and to analyse the effectiveness of macroprudential measures to contain such effects, a reconstruction of the quantitative features of the empirical network would be needed. We propose a probabilistic approach to such a reconstruction: we propose to combine some important known quantities (like the size of the banks) with a realistic stochastic representation of the remaining structural elements. Our approach allows us to evaluate relevant measures for the contagion risk after default of one unit (i.e. the number of expected subsequent defaults, or their probabilities). For some quantities we are able to derive closed form solutions, others can be obtained via computational mean-field approximations.     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

再思社会科学方法论的形式主义与实质主义之争——以经济学的新进展为例

笔者从社会科学方法论中的形式主义与实质主义之争这一古老论题出发,阐明了经济学中的形式主义思想在最近半个世纪的发展是建立细节完全刻画的"全模型",并指出其背后的理念不在于建立普遍规律,而在于形成可以用以启发人们理解经验世界的辅助性认知手段。在澄清全模型方法认识论价值的同时,本文也指出其应用重点不在于形成理想化与意识形态化的叙事,而在于针对局域的经验事实,建立具有确定经验限定意涵的模型化推论,以阐明经验事实背后不易被单纯质性研究阐发的机制。当代经济学方法论显示的这一发展,可以被称为"形式主义经济学的实质主义化",对这一趋势的把握有助于揭示主流经济学独特的认识论价值。