Renewable estimation and incremental inference in generalized linear models with streaming data sets

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153655/1/rssb12352_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153655/2/rssb12352-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153655/3/rssb12352.pd

Essays on panel data and sample selection methods

The availability of panel data allows researchers to control for unobserved heterogeneity in economic models, but raises important computational and statistical challenges. For instance, fixed effects estimators suffer from the incidental parameter problem and lead to high-dimensional estimation problems. In this dissertation, I aim to address both theoretical and practical issues in the estimation of panel data models. Sample selection is one of the most common forms of endogeneity in empirical economics. It arises when the main dependent variable is selected into the sample through a nonrandom process. The classical solution to account for sample selection is the Heckman selection model (HSM). In this dissertation, I extend the HSM in two dimensions: (1) I relax the homogeneity restrictions that the HSM imposes; and (2) I develop a panel data version of the model that accounts for unobserved heterogeneity. In Chapter 1, I develop a distribution regression model with sample selection for panel and network data. The model is a semiparametric generalization of the HSM that accommodates much richer patterns of heterogeneity in the selection process, covariates and unobserved effects. I provide a computationally attractive two-step fixed-effect estimation procedure, a bias correction method and a multiplier bootstrap algorithm to conduct uniform inference on the function-valued model parameters. I apply this model to the gravity equation of international trade network accounting for possibly endogenous zero trade decisions and unobserved country heterogeneity. Chapter 2 focuses on the distribution regression model with sample selection for cross-sectional data. In this chapter, I study the identification of the model and apply the model to wage decompositions in the UK accounting for possibly endogeneous selection into employment. Here I decompose the difference between the male and female wage distributions into four effects: composition, wage structure, selection structure and selection sorting. In Chapter 3, I propose a novel estimation algorithm for panel data models with multiple high-dimensional fixed effects and missing data. The algorithm absorbs the fixed effects iteratively until they are eventually eliminated. Applying this algorithm to a large-scale US employer-based health insurance data, I conclude that narrow network plans reduce health care utilization

Econometric Modeling and Evaluation of Fiscal-Monetary Policy Interactions

Thesis (Ph.D.) - Indiana University, Economics, 2015How do fiscal and monetary policies interact to determine inflation? The conventional view rests on the Taylor principle, that central banks can control inflation by raising nominal interest rate more than one-for-one with inflation. This principle embeds an implicit assumption that the government always adjusts taxes or spending to assure fiscal solvency. But when the required fiscal adjustments are not assured, as may occur during periods of fiscal stress, monetary policy may no longer be able to determine inflation. Under this alternative view, policy roles are reversed, with fiscal policy determining the price level and monetary policy acting to stabilize debt. Because these two policy regimes imply starkly different policy advice, identifying the prevailing regime is a prerequisite to understanding the macro economy and to making good policy choices. This dissertation employs econometric modeling and evaluation techniques to examine the empirical implications of the dynamic interactions between post-war U.S. fiscal and monetary policies. Chapter One compares two econometric interpretations of a dynamic macro model designed to study U.S. policy interactions. Two main findings emerge. First, the data overwhelmingly support the conventional view of inflation determination under the prevailing, "strong" econometric interpretation that takes literally all of the model's implications for the data. But this result is susceptible to any potential model misspecification. Second, according to the alternative, "minimal" econometric interpretation that is immune to the difficulties with the strong interpretation, the two views of inflation determination can explain the data about equally well. These findings imply that the apparent statistical support in favor of the conventional view over the alternative in the literature stems largely from the strong interpretation rather than from compelling empirical evidence. Therefore, a prudent policymaker should broaden her perspective beyond any single view on the inflation process. Chapter Two, joint with Todd B. Walker, develops an analytic function approach to solving generalized multivariate linear rational expectations models. This solution method is shown to provide important insights into equilibrium dynamics of well-known models. Chapter Three further demonstrates the usefulness of this method via a conventional new Keynesian model

What drives housing consumption in China? Based on a dynamic optimal general equilibrium model and spatial panel data analysis

Abstract. This paper examines the housing sales in China from 2004 to 2015 utilizing an optimal dynamic general equilibrium theoretical framework combined with a macroeconomic model. The spatial panel econometric empirical results suggest that housing prices and economic growth have increased housing sales in China. However, since house is considered as a special commodity in China, and unemployment show negative impacts on housing sales.Keywords. Energy use, Housing values, Optimal dynamic general equilibrium, Spatial panel econometrics, China.JEL. Q41, R31, E10

Essays on the macroeconomic management of foreign aid flows in Africa

The main motivation of this thesis is to contribute to the literature on the macroeconomic effects of foreign aid flows. It consists of four empirical papers, investigating the two main channels through which aid flows impact the recipient economy: (i) the fiscal sector, and (ii) the real exchange rate. The first paper is concerned with the impact of aid on government expenditure, domestic revenues and borrowing. It uses a traditional fiscal response framework with annual data for Ethiopia. The second paper also focuses on the fiscal sector but uses a recently compiled quarterly fiscal dataset and the cointegrated vector autoregression methodology. The main result arising from both papers is the strong correlation between aid inflows and domestic borrowing, possibly as a strategy to smooth unpredictable and volatile aid inflows. Aid is positively correlated with government expenditures, but there is little evidence of tax displacement. There is also evidence of aid heterogeneity, as grants and loans induce different effects. The third paper assesses the impact of foreign aid on the Ethiopian real exchange rate, which is a common measure of external competitiveness. It uses a quarterly macroeconomic dataset and applies two distinct methodologies: (i) single-equation cointegration models, and (ii) an unobserved components model. The results do not provide support for the ‘Dutch disease’ hypothesis. The fourth paper investigates the extent to which foreign aid is ‘absorbed’ and ‘spent’. The empirical analysis uses a panel of 25 African low-income countries and applies recently developed panel cointegration techniques. The findings suggest that aid is fully spent while absorption is higher than previously estimated

Share Ownership Distribution, Non-renewable Resources Extraction Rate and Pollution Intensity

There is increasing concern for scarcity of natural resources and deterioration of the environment due to economic activity. Although theoretically the Hotelling rule not only provides an optimal extraction for the resource owner's profit maximization problem but also provides the optimal solution for society as a whole, the rule fails to fit the facts and only applies to the idealised world for which it was constructed. In particularly, when the resource firm realises it can affect its price depending on extraction, shareholders will disagree on the extraction rate. Thus, how to deal with the shareholders' interests and make decisions for resource firms is of central importance. Endogenizing firms' objectives through shareholder voting via majority rule is considered as the solution. This thesis analyzes the behaviour of resources firms in shareholder voting equilibrium when the firms' decisions are taken through shareholder voting. Firstly, theoretical models are formulated for the extraction rate and pollution intensity of resources firms respectively. We show that the share ownership owned by the largest shareholder is an important determinant of extraction rate and pollution intensity. Moreover empirical studies using panel data are conducted to test the hypothesis. We find strong evidence supporting our theoretical implications. As for the extraction rate in resource firms, the results indicate a significant and negative relation between extraction rate and the share owned by the largest shareholder. However, a significantly positive relation is found using oil fields level data. As for the pollution emissions in firms, we find the firm where the largest shareholder holds a larger share will have lower pollution intensity

classCleaner: A Quantitative Method for Validating Peptide Identification in LC-MS/MS Workflows

Indiana University-Purdue University Indianapolis (IUPUI)Because label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics infers the peptide sequence of each measurement, there is inherent uncertainty in the identity of each peptide and its originating protein. Removing misidentified peptides can improve the accuracy and power of downstream analyses when differences between proteins are of primary interest. In this dissertation I present classCleaner, a novel algorithm designed to identify misidentified peptides from each protein using the available quantitative data. The algorithm is based on the idea that distances between peptides belonging to the same protein are stochastically smaller than those between peptides in different proteins. The method first determines a threshold based on the estimated distribution of these two groups of distances. This is used to create a decision rule for each peptide based on counting the number of within-protein distances smaller than the threshold. Using simulated data, I show that classCleaner always reduces the proportion of misidentified peptides, with better results for larger proteins (by number of constituent peptides), smaller inherent misidentification rates, and larger sample sizes. ClassCleaner is also applied to a LC-MS/MS proteomics data set and the Congressional Voting Records data set from the UCI machine learning repository. The later is used to demonstrate that the algorithm is not specific to proteomics

Multivariate expectile-based distribution: properties, Bayesian inference, and applications

International audienceExpectiles form a family of risk measures that have recently gained interest over the more common value-at-risk or return levels, primarily due to their capability to be determined by the probabilities of tail values and magnitudes of realisations at once. However, a prevalent and ongoing challenge of expectile inference is the problem of uncertainty quantification, which is especially critical in sensitive applications, such as in medical, environmental or engineering tasks. We address this issue by developing a novel distribution, termed the multivariate expectilebased distribution (MED), that possesses an expectile as a closed-form parameter. Desirable properties of the distribution, such as log-concavity, make it an excellent fitting distribution in multivariate applications. Maximum likelihood estimation and Bayesian inference algorithms are described. Simulated examples and applications to expectile and mode estimation illustrate the usefulness of the MED for uncertainty quantification

Aspects of econometric modelling

This thesis is concerned with some of the problems of econometric modelling with especial emphasis on the use of linear approximations, nonlinear estimation and specification tests. The motivation for this approach is given in the introductory chapter. In Chapter 2, the validity of linear approximations and their effects on estimation and hypothesis testing are discussed and a test for linearity is suggested. In respect of estimation, a new algorithm (based on variable linearisation) for estimating non-linear single equation functions is developed in Chapter 3, and the technique is subsequently extended to non-linear simultaneous equation systems in Chapter 9. With regard to model specification tests, emphasis is placed on the use of the Lagrange multiplier (LM) principle. In Chapter 4, a comparative study of different forms of the LM test statistic is conducted and some of its properties are discussed. Applications of the LM test are made in Chapters 5, 6 and 10. In Chapter 5, several tests for univariate normality are proposed, and one of the tests is generalized to the multivariate case in Chapter 10. Since most of the available model specification tests are one-directional and are not valid in the presence of more than one misspecification, a simultaneous approach to testing model specification is considered in Chapter 6. Tests developed for classical regression model are not applicable to limited dependent variable (LDV) models, so that specification tests for LDV models are discussed separately in Chapter 7. The test procedures mentioned above are suitable for testing nested hypotheses. In Chapter 8, test procedures for non-nested models are discussed and an attempt is made to test nested and non-nested hypotheses jointly. In the last chapter of the thesis suggestions are made to unify model estimation and testing by using robust estimates to calculate test statistics in order to increase their efficiency