Implementation-Neutral Causation in Structural Models

The assessment of businesses' credit risk is a difficult and important process in the area of financial risk management. In a classical multivariate model, financial ratios are combined in order to achieve a credit risk score, which signals if a loan application is approved or discarded. Despite their good performance, the developed multivariate models using statistical methods have been widely criticized. They are based on models that use accounting data, which have the disadvantage of being static and so often fail to follow the changes in the economic and business environment. In recent years, market models (structural and reduced form models) have become popular among banks and financial institutions, because of their theoretical background and the use of updated information. The aim of this article is to present an overview of basic market models (structural models, reduced form models and market models used from credit institutions) together with their characteristics in order to outline their development throughout the last decades.

Microfoundations for Performance, Competition and Econometric Implications

The VAR and cointegrated VAR model considered in the previous chapters are reduced form models. As argued before, the dynamic properties of the VAR system are often investigated within an impulse response analysis. Because of the reduced form nature of the models considered in Chapters 2 and 3 economic interpretation is often not possible within this framework. In recent years structural VAR models have become a popular modeling tool in empirical macroeconometrics and macroeconomics. Similar to reduced form VAR models, structural VARs have the same problems related to the large number of parameters. Therefore, in this chapter we discuss possibilities to combine the structural restrictions needed to identify the economic shocks and the subset restrictions discussed in Chapter 2. This chapter is structured as follows: Section 4.1 reviews the concept of structural VAR models and provides an overview of the structural models considered. In Section 4.2 we describe the estimation of SVAR models and suggest some estimation algorithms that can be used if subset restrictions are present and, moreover, provide an illustrative example. In Section 4.3 we compare different estimation methods for subset SVAR models and investigate the effects of data based model reduction on the precision of structural parameter estimates, impulse response point and interval estimates. Section 4.4 summarizes the main results.KeywordsImpulse ResponseModel ReductionGeneralize Methodof MomentStructural ShockGeneralize Methodof Moment EstimateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This chapter summarizes the overall tendering and contracting process in the pharmaceutical industry by providing an overview of the first-sealed price auction theory, auction rules, and drug pricing mechanism of different countries. Comparing procurement systems across Asia, Africa, Europe, and Latin America, the review casts light on various pharmaceutical bidding systems across the world and their impact on drug prices. Then, this review focuses on the empirical estimation of first-price auction models. In terms of model specification, we compare the two most commonly used empirical methods for bidding price estimation: structural models and reduced form approaches to test the auction theory. Maximum likelihood estimation is the most frequently used method for structural estimation in literature and selection bias correction is widely adopted using reduced form models. In addition to parametric model construction, we also provide an extensive introduction of non-parametric testing methodologies, including non-parametric estimation and quantile-based estimation to reduce the computation complexity and further illustrate how auction theory could be validated by real-world applications. Additional thoughts and adjustments on non-parametric testing are brought up based on a real-world tendering use case from a large multi-national pharmaceutical company.

The authors provide a comprehensive and useful review of structural models in marketing (Chintagunta et al. 2006). They evaluate the strengths and limitations of a structural approach and present examples of recent advancements in the development and application of structural models. Their discussion on the directions for future studies is very insightful. Despite the growing trend in the literature to claim contribution by offering structural treatments to research questions, it remains arbitrary and sometimes mysterious as to what constitutes a “structural” model. I agree with the authors that artificial boundaries are commonly imposed in our field between structural and reduced-form models. I believe it is more constructive to classify research studies along a structural/reduced-form continuum than to draw a distinctive line between, or even develop different research paradigms around, them. My objective in this discussion is to provide further insights into the structural/reduced-form continuum. I first argue that this continuum is usually multidimensional, and one can move up the continuum in two directions. Following the argument are some examples of how the studies in the literature can be positioned along the continuum. I also discuss the role of data in developing more structural models. I argue that limited data quality can be a “double-edged sword” for the use of more structural models and that data integration and researcher creativity can be substitutes for limited data quality. Directions for future research on moving up the structural/reduced-form continuum are also identified. The discussion intends to support the complementarity between “structural” and “reduced-form” models, and the need to break the boundary between them.

A Comprehensive Unified Model of Structural and Reduced Form Models for Defaultable Fixed Income Bonds

PRM85 - Analysis of Causal Relations in Stroke Registry Data

Chapter 5 - Restrictions on the reduced form and its computerized parametrization

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] In this study, I examine effects of financial incentives and non-financial incentives on public school teachers' retirement in Missouri. For financial incentives, existing studies commonly model the retirement in two types, static reduced-form and structural-form. There are two main types of the reduced-form models, linear probability model (e.g., Costrell and McGee, 2010; Brown, 2013), probit or logit model (e.g., Furgeson et al., 2006; Asch et al., 2005; Coile and Gruber, 2007). A prominent structural form model is the Stock and Wise (1990) option value model. In addition, some researchers (Brown and Laschever, 2012; Gustman and Steinmeier, 2000; Dwyer and Mitchell, 1999) suggest that non-financial incentives, such as colleagues, health status, and family, are also important factors to explain retirement decisions. Among these non-financial incentives, peer effects are the focus of my study. The first chapter analyzes peer effects on public school teachers' retirement timing decisions. I use an administrative dataset of the full population of late career Missouri public school teachers during academic years 1994-2007 to construct a model incorporating both financial incentives and non-financial incentives when teachers make retirement decisions. My study mainly differs from previous literature in the controls of financial incentives and the use of data. For controls of financial incentives, instead of using pension wealth and peak value, I propose the simulated "financial benefit of postponing retirement" based on Stock-Wise (hereafter, SW) option value model as a new financial incentive proxy. Besides, I use a much richer dataset so that more detailed information about teachers are available and the schools are more diverse in poverty and urbanicity. Empirically, when the retirement rate of peers in previous year increases 1%, the probability of retirement increases approximately 0.128 percentage points. In addition, I find evidence of heterogeneous peer effects. For example, teachers with different education levels respond to peers retirement differently. Because the reduced-form parameters generally depend on the pension rules, the reduced-form models are unreliable to predict the effects of changes in the pension rules. In contrast, the structural model parameters are independent of pension rules, which makes them useful for policy analysis. The second chapter uses an economic structural model to fit late career Missouri public school teachers' data and study the effects of changing pension rules on the timing of retirement. Ni and Podgursky (2016) show that the SW option value model produces good fit for the Missouri public school teachers during 2002-2008. There are three limitations in their study and other applications of the SW model. First, the SW option value model has only been applied to retirement data when the pension rules are fixed. Second, there is a selection bias in the sample of senior teachers: among the retirement-eligible teachers only the "stayers" are in the sample, while the "early leavers" are absent. This bias has not been adjusted in the previous studies. Third, the SW model is estimated based on the likelihood of panel data of individuals (teachers or salesmen). The cost of computing the likelihood increases in the number of teachers and the length of the sample period. For a larger state, such as Texas, and/or a longer sample period, the computation cost may be prohibitively high. The second chapter solves these problems. First, I model expectations of pension rule changes. Second, I adjust the sample selection bias by using distribution of initial preference errors. Third, I group individual teachers into a fixed number of (age, experience) cells to reduce the time of evaluating the likelihood. I estimate the model under different expectation assumptions and compare their performance by simulation. The estimated SW model exhibits good in-sample and out-of-sample fits. Counterfactual analysis shows that pension enhancements reduce the retirement age by around 0.4 years for the 1994 cohort teachers and by more than one year for teachers in the steady state. Finally, I compare SW option value model with an alternative widely applied structural model of retirement, dynamic programming model. Compared to dynamic programming model, the SW model tends to predict early retirement.

Credit risk findings for commercial real estate loans using the reduced form

Microscopic traffic simulation tools are becoming increasingly popular in evaluating transport options. Driving behaviour models (e.g. route choice models, lane-changing models, etc.) are essential components of these tools. The state-of-the-art driving behaviour models assume that drivers make instantaneous decisions. However, in reality, many of the driving decisions are based on a specific plan. The plan is however unobserved or latent and only the manifestations of the plan through actions are observed. Examples include selection of a target lane before execution of the lane change, choice of a merging tactic before execution of the merge. Ignoring the effect of plans in the decision framework can lead to incorrect representation of congestion in traffic simulation tools. In this article, we present a modelling methodology to address the effects of unobserved plans in the decisions of the drivers. The actions of the driver are conditional on the current plan and can be influenced by anticipation of downstream traffic conditions. The heterogeneity in decision making and planning capabilities of drivers are explicitly addressed. The methodology has been applied in developing lane-changing behaviour models with disaggregate trajectory data extracted from video recordings of an urban road using the maximum likelihood technique. Estimation results show that the latent plan models have a significantly better goodness-of-fit compared to the ‘reduced form’ models where the latent plans are ignored. The latent plan models were also found to outperform the reduced form models in validation case studies within the microscopic traffic simulator MITSIMLab.

Using the Environmental Kuznets Curve to evaluate energy policy: Some practical considerations

In this paper, we study the credit default swap (CDS) pricing with counterparty risk in a reduced form model. The default jump intensities of the reference firm and counterparty are both assumed to follow the mean-reverting CIR processes with independent jumps respectively and a common jump. The approximate closed-form solutions of the joint survival probability density and the probability density of the first default can be obtained by using the PDE method. Then with the expressions of the probability densities, we can get the formula for the CDS price with counterparty risk in a reduced form model with a common jump. In the numerical analysis part, we find that the default of the reference asset has a greater impact on the CDS price than that of the default of counterparty after introducing the common jump process.

Dynamic Modeling using Advanced Time-series Techniques: Energy-GNP and Energy-Employment Interactions

We propose a general strategy to estimate semi‐parametrically simultaneous equations with limited dependent variables. First, each reduced form (RF) is estimated with various semi‐parametric methods. Second, the specification of each RF is tested to select an appropriate method. Third, the structural form (SF) equations are estimated using minimum distance methods and the restrictions among the SF and RF parameters. A case study of female labour supply is presented.