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Fiscal management in resource-rich countries : essentials for economists, public finance professionals, and policy makers
The extractive industries sector (EI) occupies an outsize space in the economies of many developing countries. Policy makers, economists, and public finance professionals working in such countries are frequently confronted with issues that require an in-depth understanding of the sector, its economics, governance, and policy challenges, as well as the implications of natural resource wealth for fiscal and public financial management. The objective of the two-volume Essentials for Economists, Public Finance Professionals, and Policy Makers, published in the World Bank Studies series, is to provide a concise overview of the EI-related topics these professionals are likely to encounter. This second volume, Fiscal Management in Resource-Rich Countries, addresses critical fiscal challenges typically associated with large revenue flows from the EI sector. The volume discusses fiscal policy across four related dimensions: short-run stabilization, the management of fiscal risks and vulnerabilities, the promotion of long-term sustainability, and the importance of good public financial management and public investment management systems. The volume subsequently examines several institutional mechanisms used to aid fiscal management, including medium-term expenditure frameworks, resource funds, fiscal rules, and fiscal councils. The volume also discusses the earmarking of revenue, resource revenue projections as applied to the government budget, and fiscal transparency, and outlines several fiscal indicators used to assess the fiscal stance of resource-rich countries. The authors hope that economists, public finance professionals, and policy makers working in resource-rich countries-- including decision makers in ministries of finance, international organizations, and other relevant entities-- will find the volume useful to their understanding and analysis of fiscal management in resource-rich countries.
Book
Target rotation rate estimation via ISAR frame processing
A method is proposed for estimating the target rotation rate for ISAR imaging based on the frame processing technique. The method utilises the intrinsic structures of the frame which span the Hilbert space of radar return from which the rotational information of the target is extracted. For a prominent point, the intensity of the projection is maximum when the frame component corresponding to the prominent point is built with the target rotation rate. [PUBLICATION ABSTRACT]
Journal Article
What Are We Weighting For?
When estimating population descriptive statistics, weighting is called for if needed to make the analysis sample representative of the target population. With regard to research directed instead at estimating causal effects, we discuss three distinct weighting motives: (1) to achieve precise estimates by correcting for heteroskedasticity; (2) to achieve consistent estimates by correcting for endogenous sampling; and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does.
Journal Article
Sample Size Determination for GEE Analyses of Stepped Wedge Cluster Randomized Trials
In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Setting the latter two correlation parameters to be equal accommodates cross-sectional designs. We propose sample size procedures for continuous and binary responses within the framework of generalized estimating equations that employ a block exchangeable within-cluster correlation structure defined from the distinct correlation types. For continuous responses, we show that the intraclass correlations affect power only through two eigenvalues of the correlation matrix. We demonstrate that analytical power agrees well with simulated power for as few as eight clusters, when data are analyzed using bias-corrected estimating equations for the correlation parameters concurrently with a bias-corrected sandwich variance estimator.
Journal Article
A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices
We develop a bid-ask spread estimator from daily high and low prices. Daily high (low) prices are almost always buy (sell) trades. Hence, the high-low ratio reflects both the stock's variance and its bid-ask spread. Although the variance component of the high-low ratio is proportional to the return interval, the spread component is not. This allows us to derive a spread estimator as a function of high-low ratios over 1-day and 2-day intervals. The estimator is easy to calculate, can be applied in a variety of research areas, and generally outperforms other low-frequency estimators.
Journal Article
Online Updating of Statistical Inference in the Big Data Setting
We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness of fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting. Supplementary materials for this article are available online.
Journal Article
Common Errors: How to (and Not to) Control for Unobserved Heterogeneity
Controlling for unobserved heterogeneity (or \"common errors\"), such as industry-specific shocks, is a fundamental challenge in empirical research. This paper discusses the limitations of two approaches widely used in corporate finance and asset pricing research: demeaning the dependent variable with respect to the group (e.g., \"industry-adjusting\") and adding the mean of the group's dependent variable as a control. We show that these methods produce inconsistent estimates and can distort inference. In contrast, the fixed effects estimator is consistent and should be used instead. We also explain how to estimate the fixed effects model when traditional methods are computationally infeasible.
Journal Article
SEMIPARAMETRIC OPTIMAL ESTIMATION WITH NONIGNORABLE NONRESPONSE DATA
When the response mechanism is believed to be not missing at random (NMAR), a valid analysis requires stronger assumptions on the response mechanism than standard statistical methods would otherwise require. Semiparametric estimators have been developed under the parametric model assumptions on the response mechanism. In this paper, a new statistical test is proposed to guarantee model identifiability without using instrumental variable assumption. Furthermore, we develop optimal semiparametric estimation for parameters such as the population mean. Specifically, we propose two semiparametric optimal estimators that do not require any model assumptions other than the response mechanism. Asymptotic properties of the proposed estimators are discussed. An extensive simulation study is presented to compare with some existing methods. We present an application of our method using Korean labor and income panel survey data.
Journal Article
Doubly Robust Policy Evaluation and Optimization
We study sequential decision making in environments where rewards are only partially observed, but can be modeled as a function of observed contexts and the chosen action by the decision maker. This setting, known as contextual bandits, encompasses a wide variety of applications such as health care, content recommendation and Internet advertising. A central task is evaluation of a new policy given historic data consisting of contexts, actions and received rewards. The key challenge is that the past data typically does not faithfully represent proportions of actions taken by a new policy. Previous approaches rely either on models of rewards or models of the past policy. The former are plagued by a large bias whereas the latter have a large variance. In this work, we leverage the strengths and overcome the weaknesses of the two approaches by applying the doubly robust estimation technique to the problems of policy evaluation and optimization. We prove that this approach yields accurate value estimates when we have either a good (but not necessarily consistent) model of rewards or a good (but not necessarily consistent) model of past policy. Extensive empirical comparison demonstrates that the doubly robust estimation uniformly improves over existing techniques, achieving both lower variance in value estimation and better policies. As such, we expect the doubly robust approach to become common practice in policy evaluation and optimization.
Journal Article
Comparison of Two Bias-Corrected Covariance Estimators for Generalized Estimating Equations
Mancl and DeRouen (2001, Biometrics57, 126-134) and Kauermann and Carroll (2001, JASA96, 1387-1398) proposed alternative bias-corrected covariance estimators for generalized estimating equations parameter estimates of regression models for marginal means. The finite sample properties of these estimators are compared to those of the uncorrected sandwich estimator that underestimates variances in small samples. Although the formula of Mancl and DeRouen generally overestimates variances, it often leads to coverage of 95% confidence intervals near the nominal level even in some situations with as few as 10 clusters. An explanation for these seemingly contradictory results is that the tendency to undercoverage resulting from the substantial variability of sandwich estimators counteracts the impact of overcorrecting the bias. However, these positive results do not generally hold; for small cluster sizes (e.g., <10) their estimator often results in overcoverage, and the bias-corrected covariance estimator of Kauermann and Carroll may be preferred. The methods are illustrated using data from a nested cross-sectional cluster intervention trial on reducing underage drinking.
Journal Article