Likelihood approximations and discrete models for tied survival data

Ties among event times are often recorded in survival studies. For example, in a two week laboratory study where event times are measured in days, ties are very likely to occur. The proportional hazards model might be used in this setting using an approximated partial likelihood function. This approximation works well when the number of ties is small. on the other hand, discrete regression models are suggested when the data are heavily tied. However, in many situations it is not clear which approach should be used in practice. In this work, empirical guidelines based on Monte Carlo simulations are provided. These recommendations are based on a measure of the amount of tied data present and the mean square error. An example illustrates the proposed criterion.

Essays on the Empirical Analysis of Economic and Political Development in Sub-Saharan Africa

In Sub-Saharan Africa, non-democratic events, like civil wars and coup d'etat, destroy economic development. This study investigates both domestic and spatial effects on the likelihood of civil wars and coup d'etat. To civil wars, an increase of income growth is one of common research conclusions to stop wars. This study adds a concern on ethnic fractionalization. IV-2SLS is applied to overcome causality problem. The findings document that income growth is significant to reduce number and degree of violence in high ethnic fractionalized countries, otherwise they are trade-off. Income growth reduces amount of wars, but increases its violent level, in the countries with few large ethnic groups. Promoting growth should consider ethnic composition. This study also investigates the clustering and contagion of civil wars using spatial panel data models. Onset, incidence and end of civil conflicts spread across the network of neighboring countries while peace, the end of conflicts, diffuse only with the nearest neighbor. There is an evidence of indirect links from neighboring income growth, without too much inequality, to reduce the likelihood of civil wars. To coup d'etat, this study revisits its diffusion for both all types of coups and only successful ones. The results find an existence of both domestic and spatial determinants in different periods. Domestic income growth plays major role to reduce the likelihood of coup before cold war ends, while spatial effects do negative afterward. Results on probability to succeed coup are similar. After cold war ends, international organisations seriously promote democracy with pressure against coup d'etat, and it seems to be effective. In sum, this study indicates the role of domestic ethnic fractionalization and the spread of neighboring effects to the likelihood of non-democratic events in a country. Policy implementation should concern these factors.

Essays in Macroecometrics: methodological aspects and empirical applications

In the first chapter, I develop a panel no-cointegration test which extends Pesaran, Shin and Smith (2001)'s bounds test to the panel framework by considering the individual regressions in a Seemingly Unrelated Regression (SUR) system. This allows to take into account unobserved common factors that contemporaneously affect all the units of the panel and provides, at the same time, unit-specific test statistics. Moreover, the approach is particularly suited when the number of individuals of the panel is small relatively to the number of time series observations. I develop the algorithm to implement the test and I use Monte Carlo simulation to analyze the properties of the test. The small sample properties of the test are remarkable, compared to its single equation counterpart. I illustrate the use of the test through a test of Purchasing Power Parity in a panel of EU15 countries. In the second chapter of my PhD thesis, I verify the Expectation Hypothesis of the Term Structure in the repurchasing agreements (repo) market with a new testing approach. I consider an "inexact" formulation of the EHTS, which models a time-varying component in the risk premia and I treat the interest rates as a non-stationary cointegrated system. The effect of the heteroskedasticity is controlled by means of testing procedures (bootstrap and heteroskedasticity correction) which are robust to variance and covariance shifts over time. I fi#nd that the long-run implications of EHTS are verified. A rolling window analysis clarifies that the EHTS is only rejected in periods of turbulence of #financial markets. The third chapter introduces the Stata command "bootrank" which implements the bootstrap likelihood ratio rank test algorithm developed by Cavaliere et al. (2012). The command is illustrated through an empirical application on the term structure of interest rates in the US.

An empirical comparison of methods for meta-analysis of diagnostic accuracy showed hierarchical models are necessary

OBJECTIVE: Meta-analysis of studies of the accuracy of diagnostic tests currently uses a variety of methods. Statistically rigorous hierarchical models require expertise and sophisticated software. We assessed whether any of the simpler methods can in practice give adequately accurate and reliable results. STUDY DESIGN AND SETTING: We reviewed six methods for meta-analysis of diagnostic accuracy: four simple commonly used methods (simple pooling, separate random-effects meta-analyses of sensitivity and specificity, separate meta-analyses of positive and negative likelihood ratios, and the Littenberg-Moses summary receiver operating characteristic [ROC] curve) and two more statistically rigorous approaches using hierarchical models (bivariate random-effects meta-analysis and hierarchical summary ROC curve analysis). We applied the methods to data from a sample of eight systematic reviews chosen to illustrate a variety of patterns of results. RESULTS: In each meta-analysis, there was substantial heterogeneity between the results of different studies. Simple pooling of results gave misleading summary estimates of sensitivity and specificity in some meta-analyses, and the Littenberg-Moses method produced summary ROC curves that diverged from those produced by more rigorous methods in some situations. CONCLUSION: The closely related hierarchical summary ROC curve or bivariate models should be used as the standard method for meta-analysis of diagnostic accuracy.

Australia's National Mental Health Strategy and deinstitutionalization: some empirical results

Objective: To determine the role of the National Mental Health Strategy in the deinstitutionalization of patients in psychiatric hospitals in Queensland. Method: Regression analysis (using the maximum likelihood method) has been applied to relevant time-series datasets on public psychiatric institutions in Queensland. In particular, data on both patients and admissions per 10 000 population are analysed in detail from 1953-54 to the present, although data are presented from 1883-84. Results: These Queensland data indicate that deinstitutionalization was a continuing process from the 1950s to the present. However, it is clear that the experience varied from period to period. For example, the fastest change (in both patients and admissions) took place in the period 1953-54 to 1973-74, followed by the period 1974-75 to 1984-85. Conclusions: In large part, the two policies associated with deinstitutionalization, namely a discharge policy ('opening the back door') and an admission policy ('closing the front door') had been implemented before the advent of the National Mental Health Strategy in January 1993. Deinstitutionalization was most rapid in the 30-year period to the early 1980s: the process continued in the 1990s, but at a much slower rate. Deinstitutionalization was, in large part, over before the Strategy was developed and implemented.

Real options and demographic decisions:empirical evidence from East and West Germany

Iyer and Velu (2006) have convincingly argued that contemporary analyses of fertility behaviour fail to explain why a woman (or a couple) will choose to postpone childbirth, and in particular to consider the role of uncertainty in this regard. They have addressed this lacuna in the literature by using a real options approach to model fertility decisions by relating uncertainty experienced by individuals to the likelihood of childbirth. However, they did not present empirical evidence. Since the theory implies the existence of two offsetting effects of uncertainty on fertility decisions, a positive insurance effect and a negative option value effect, it is not easy to reject the theory on the basis of empirical analysis, when one of these effects offsets the other. We construct such a test for East (and also West) Germany during that country's reunification, which takes advantage of the fact that because of the country's strong welfare system, the insurance effect should be dominated by the option value effect, thereby suggesting that the net relationship should be negative. The results provide rather strong support for the real options link, especially for Eastern Germany.

Test for Independence of the Variables with Missing Elements in One and the Same Column of the Empirical Correlation Matrix

2000 Mathematics Subject Classification: 62H15, 62H12.

An empirical investigation on firm-level performance of cross border mergers and acquisitions

This empirical study investigates the performance of cross border M&A. The first stage is to identify the determinants of making cross border M&A complete. One focus here is to extend the existing empirical evidence in the field of cross border M&A and exploit the likelihood of M&A from a different perspective. Given the determinants of cross border M&A completions, the second stage is to investigate the effects of cross border M&A on post-acquisition firm performance for both targets and acquirers. The thesis exploits a hitherto unused data base, which consists of those firms that are rumoured to be undertaking M&A, and then follow the deal to completion or abandonment. This approach highlights a number of limitations to the previous literature, which relies on statistical methodology to identify potential but non-existent mergers. This thesis changes some conventional understanding for M&A activity. Cross border M&A activity is underpinned by various motives such as synergy, management discipline, and acquisition of complementary resources. Traditionally, it is believed that these motives will boost the international M&A activity and improve firm performance after takeovers. However, this thesis shows that such factors based on these motives as acquirer’s profitability and liquidity and target’s intangible resource actually deter the completion of cross border M&A in the period of 2002-2011. The overall finding suggests that the cross border M&A is the efficiency-seeking activity rather than the resource-seeking activity. Furthermore, compared with firms in takeover rumours, the completion of M&A lowers firm performance. More specifically, the difficulties in transfer of competitive advantages and integration of strategic assets lead to low firm performance in terms of productivity. Besides, firms cannot realise the synergistic effect and managerial disciplinary effect once a cross border M&A is completed, which suggests a low post-acquisition profitability level.

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Three empirical essays on startups' survival using the Kauffman Firm Survey

This dissertation studies newly founded U.S. firms' survival using three different releases of the Kauffman Firm Survey. I study firms' survival from a different perspective in each chapter. ^ The first essay studies firms' survival through an analysis of their initial state at startup and the current state of the firms as they gain maturity. The probability of survival is determined using three probit models, using both firm-specific variables and an industry scale variable to control for the environment of operation. The firm's specific variables include size, experience and leverage as a debt-to-value ratio. The results indicate that size and relevant experience are both positive predictors for the initial and current states. Debt appears to be a predictor of exit if not justified wisely by acquiring assets. As suggested previously in the literature, entering a smaller-scale industry is a positive predictor of survival from birth. Finally, a smaller-scale industry diminishes the negative effects of debt. ^ The second essay makes use of a hazard model to confirm that new service-providing (SP) firms are more likely to survive than new product providers (PPs). I investigate the possible explanations for the higher survival rate of SPs using a Cox proportional hazard model. I examine six hypotheses (variations in capital per worker, expenses per worker, owners' experience, industry wages, assets and size), none of which appear to explain why SPs are more likely than PPs to survive. Two other possibilities are discussed: tax evasion and human/social relations, but these could not be tested due to lack of data. ^ The third essay investigates women-owned firms' higher failure rates using a Cox proportional hazard on two models. I make use of a never-before used variable that proxies for owners' confidence. This variable represents the owners' self-evaluated competitive advantage. ^ The first empirical model allows me to compare women's and men's hazard rates for each variable. In the second model I successively add the variables that could potentially explain why women have a higher failure rate. Unfortunately, I am not able to fully explain the gender effect on the firms' survival. Nonetheless, the second empirical approach allows me to confirm that social and psychological differences among genders are important in explaining the higher likelihood to fail in women-owned firms.^

The Influence of Age and Size on Family-Owned Firms’ Financing Decisions: Empirical Evidence Using Panel Data

Based on four samples of Portuguese family-owned firmsdi) 185 young, low-sized family-owned firms; ii) 167 young, high-sized familyowned firms; iii) 301 old, low-sized family-owned firms; and iv) 353 old, high-sized family-owned firms d we show that age and size are fundamental characteristics in family-owned firms’ financing decisions. The multiple empirical evidence obtained allows us to conclude that the financing decisions of young, low-sized family-owned firms are quite close to the assumptions of Pecking Order Theory, whereas those of old, high-sized family-owned firms are quite close to what is forecast by Trade-Off Theory. The lesser information asymmetry associated with greater age, the lesser likelihood of bankruptcy associated with greater size, as well as the lesser concentration of ownership and management consequence of greater age and size, may be especially important in the financing decisions of family-owned firms. In addition, we find that GDP, interest rate and periods of crisis have a greater effect on the debt of young, low-sized family-owned firms than on that of family-owned firms of the remainder research samples.