Comparison Between Nelson-Olson Method and Two-Stage Limited Dependent Variables (2SLDV ) Method for the Estimation of a Simultaneous Equations System (Tobit Model)

This study relates to the estimation of a simultaneous equations system for the Tobit model where the dependent variables ( ) are limited, and this will affect the method to choose the good estimator. So, we will use new estimations methods different from the classical methods, which if used in such a case, will produce biased and inconsistent estimators which is (Nelson-Olson) method and Two- Stage limited dependent variables(2SLDV) method to get of estimators that hold characteristics the good estimator . That is , parameters will be estimated for the limited variables and find the variance-covariance matrix for extracted estimators by the aforementioned two methods and then compare between the results of the two methods and find any better method by estimation and then finding the estimation efficiency, and this is what the study aims to . A simultaneous equations system will be imposed for the limited model defined by two equations containing two endogenous variables one of complete observations and the other censored at zero. The two methods were used to analyze the relationship between income and family expenditure on durable consumer goods , where the results showed that the performance of (Nelson-Olson) method is better than performing the Two-Stage limited dependent variables (2SLDV) method in obtain the lower values and all comparison measures as well as the results showed that income and expenditure one affects the other and the and the price affects the income and expenditure

Military Coups d’État and Their Causes

There have been three waves of scholarship on military coups d’état (or simply “coups”)—the unconstitutional replacement of chief executives by military officers—since the 1960s. The first used case studies to explore why the military overthrows governments. One of its central findings was that military uprisings were an integral part of political succession in many countries. A second wave produced the “aggregate studies” that were the first to deploy cross-national databases to identify the measurable features that distinguished more from less coup-prone political systems. These studies revealed, among other things, that coups proliferated in places with a history of instability. The third and current wave of scholarship takes advantage of the development of statistical software for limited dependent variables—then unavailable, now commonplace—to recast the quantitative research on coups. Two core findings have survived disconfirmation since the start of the third wave. First, higher income countries have fewer coups, though the effects are small (and become even weaker when models only contain developing countries). Second, “political legacy effects” mean that the probability of a coup declines with time since the last military uprising. Much of the latest wave of research pinpoints factors—like coup proofing, less inequality, or the end of the Cold War—that reduce the probability of a coup. The development of ever more sophisticated statistical techniques to divine the causes of instability, nevertheless, relies on off-the-shelf data sets and coup catalogs whose validity—properly understood as accuracy—is questionable. Only a greater attention to accuracy and complementary methods promise to produce a comprehensive account of why the military topples governments in some, but not in other, places.

Limited Dependent Variables and Discrete Choice Modelling

Limited dependent variables considers regression models where the dependent variable takes limited values like zero and one for binary choice mowedels, or a multinomial model where there is a few choices like modes of transportation, for example, bus, train, or a car. Binary choice examples in economics include a woman’s decision to participate in the labor force, or a worker’s decision to join a union. Other examples include whether a consumer defaults on a loan or a credit card, or whether they purchase a house or a car. This qualitative variable is recoded as one if the female participates in the labor force (or the consumer defaults on a loan) and zero if she does not participate (or the consumer does not default on the loan). Least squares using a binary choice model is inferior to logit or probit regressions. When the dependent variable is a fraction or proportion, inverse logit regressions are appropriate as well as fractional logit quasi-maximum likelihood. An example of the inverse logit regression is the effect of beer tax on reducing motor vehicle fatality rates from drunken driving. The fractional logit quasi-maximum likelihood is illustrated using an equation explaining the proportion of participants in a pension plan using firm data. The probit regression is illustrated with a fertility empirical example, showing that parental preferences for a mixed sibling-sex composition in developed countries has a significant and positive effect on the probability of having an additional child. Multinomial choice models where the number of choices is more than 2, like, bond ratings in Finance, may have a natural ordering. Another example is the response to an opinion survey which could vary from strongly agree to strongly disagree. Alternatively, this choice may not have a natural ordering like the choice of occupation or modes of transportation. The Censored regression model is motivated with estimating the expenditures on cars or estimating the amount of mortgage lending. In this case, the observations are censored because we observe the expenditures on a car (or the mortgage amount) only if the car is bought or the mortgage approved. In studying poverty, we exclude the rich from our sample. In this case, the sample is not random. Applying least squares to the truncated sample leads to biased and inconsistent results. This differs from censoring. In the latter case, no data is excluded. In fact, we observe the characteristics of all mortgage applicants even those that do not actually get their mortgage approved. Selection bias occurs when the sample is not randomly drawn. This is illustrated with a labor participating equation (the selection equation) and an earnings equation, where earnings are observed only if the worker participates in the labor force, otherwise it is zero. Extensions to panel data limited dependent variable models are also discussed and empirical examples given.

Limited Dependent Variables in Management Research

A limited dependent variable (LDV) is an outcome or response variable whose value is either restricted to a small number of (usually discrete) values or limited in its range of values. The first type of LDV is commonly called a categorical variable; its value indicates the group or category to which an observation belongs (e.g., male or female). Such categories often represent different choice outcomes, where interest centers on modeling the probability each outcome is selected. An LDV of the second type arises when observations are drawn about a variable whose distribution is truncated, or when some values of a variable are censored, implying that some values are wholly or partially unobserved. Methods such as linear regression are inadequate for obtaining statistically valid inferences in models that involve an LDV. Instead, different methods are needed that can account for the unique statistical characteristics of a given LDV.