Automatic smoothing parameter selection: A survey

This is a survey of recent developments in smoothing parameter selection for curve estimation. The first goal of this paper is to provide an introduction to the methods available, with discussion at both a practical and also a nontechnical level, including comparison of methods. The second goal is to provide access to the literature, especially on smoothing parameter selection, but also on curve estimation in general. The two main settings considered here are nonparametric regression and probability density estimation, although the points made apply to other settings as well. These points also apply to many different estimators, although the focus is on kernel estimators, because they are the most easily understood and motivated, and have been at the heart of the development in the field.     

Deriving values for the ecological support function of wildlife: An indirect valuation approach

We describe a method that combines economic willingness-to-pay estimates for higher trophic-level species with basic information available about ecosystem relationships to derive estimates of partial willingness-to-pay for lower level species that might be of direct policy interest. This method is intended as a quasi-benefit transfer method for use in benefit–cost analysis. Our method makes it possible to establish partial willingness-to-pay estimates for the large number of species of immediate or potential policy interest using only data available in non-market valuation and biology and ecology literature. We provide a partial estimation of indirect values for the predator–prey relationships that support golden eagles in the Snake River Bird of Prey area as an example of how to operationalize our approach.     

Individual needs and social pressure: Evidence on the Easterlin hypothesis using repeated cross-section surveys of Canadian households

This paper provides additional evidence, using time-series and cross-sectional Canadian survey data, for the Easterlin hypothesis of an important income elasticity of individual needs. Our analysis is based on the regression of a minimum income to satisfy needs equation derived from a simple utility maximization framework. Moreover, our specification allows computing the Arrow-Pratt relative risk-aversion index and the Intertemporal Rate of Substitution. Our results are robust to different estimation methods dealing with the endogenous nature of income. We also compute poverty rates using our estimated equation parameters and standard OECD measures of poverty and find that some subjective measures are relatively close to the OECD measures.     

Estimating the causal effect of fertility on economic wellbeing: data requirements, identifying assumptions and estimation methods

This article aims to answer to what extent fertility has a causal effect on households’ economic wellbeing—an issue that has received considerable interest in development studies and policy analysis. However, only recently has this literature begun to give importance to adequate modelling for estimation of causal effects. We discuss several strategies for causal inference, stressing that their validity must be judged on the assumptions we can plausibly formulate in a given application, which in turn depends on the richness of available data. We contrast methods relying on the unconfoundedness assumption, which include regressions and propensity score matching, with instrumental variable methods. This discussion has a general importance, representing a set of guidelines that are useful for choosing an appropriate strategy of analysis. The discussion is valid for both cross-sectional or panel data.     

Government size,democracy, and corruption: An empirical investigation

Previous studies on the effect of government size on corruption have produced mixed results. In an attempt to explain these ambiguous results, this study investigates the effect of government size on corruption by taking into account the role of the democracy in each country. Using annual data of 82 countries between 1995 and 2008, the estimation results indicate that an increase in government size can lead to a decrease in corruption if the democracy level is sufficiently high and, in contrast, can lead to an increase in corruption if it is too low. As robustness checks, the estimations using a different index of corruption and a different proxy for government size are also conducted. The results reveal that our main findings are robust. Furthermore, to address endogeneity problems, we conduct the instrumental variables estimation and the system generalized method of moments estimation, the results of which also support our primary findings. These results provide some important implications for policymakers seeking to perform government interventions without aggravating corruption.     

Forecasting REIT volatility with high-frequency data: a comparison of alternative methods

Volatility is a crucial input for many financial applications, including asset allocation, risk management and option pricing. Over the last two decades the use of high-frequency data has greatly advanced the research on volatility modelling. This article makes the first attempt in the real estate literature to employ intraday data for volatility forecasting. We examine a wide range of commonly used methods and apply them to several major global REIT markets. Our findings suggest that the group of reduced form methods deliver the most accurate one-step-ahead forecast for daily REIT volatility. They outperform their GARCH-model-based counterparts and two methods using low-frequency data. We also show that exploiting intraday information through GARCH does not necessarily yield incremental precision for forecasting REIT volatility. Our results are relatively robust to the choice of realized measure of volatility and the length of evaluation period.     

Analysis of High Frequency Data in Finance: A Survey

This study examines the use of high frequency data in finance, including volatility estimation and jump tests. High frequency data allows the construction of model-free volatility measures for asset returns. Realized variance is a consistent estimator of quadratic variation under mild regularity conditions. Other variation concepts, such as power variation and bipower variation, are useful and important for analyzing high frequency data when jumps are present. High frequency data can also be used to test jumps in asset prices. We discuss three jump tests: bipower variation test, power variation test, and variance swap test in this study. The presence of market microstructure noise complicates the analysis of high frequency data. The survey introduces several robust methods of volatility estimation and jump tests in the presence of market microstructure noise. Finally, some applications of jump tests in asset pricing are discussed in this article.     

A META‐ANALYSIS ON THE RELATIONSHIP BETWEEN INCOME INEQUALITY AND ECONOMIC GROWTH

In recent years there is a growing interest in determining the impact of inequality on economic growth. Theoretical papers as well as empirical applications have, however, produced controversial results. Although there is a considerable part of the literature that considers inequality detrimental to growth, more recent studies have challenged this result and found a positive effect of inequality on growth. In this paper, we provide a contribution to the empirical puzzle by using meta‐analysis to systematically describe, identify and analyse the variation in outcomes of empirical studies. We find that estimation methods, data quality and sample coverage systematically affect the results. The results point out that it will be particularly useful to increasingly focus research on determining the impact of income inequality on economic growth using single‐country data at the regional level, or a relatively homogeneous set of countries with adequate controls for country‐wide differences in economic, social and institutional characteristics.     

The long-run determinants of fertility: one century of demographic change 1900–1999

We examine the long-run relationship between fertility, mortality, and income using panel cointegration techniques and the available data for the last century. Our main result is that mortality changes and growth of income contributed to the fertility transition. The fertility reduction triggered by falling mortality, however, is not enough to overcompensate the positive effect of falling mortality on population growth. This means that growth of income per capita is essential to explain the observed secular decline of population growth. These results are robust to alternative estimation methods, potential outliers, sample selection, different measures of mortality, the sample period, the inclusion of education as an explanatory variable, and the use of different data sets. In addition, our causality tests suggest that fertility changes are both cause and consequence of economic development.     

Aggregate infrastructure capital stock and long-run growth: Evidence from Finnish data

In this paper, Bayesian methods and the Finnish aggregate infrastructure capital series from 1860 to 2003 are used to explore how government infrastructure policy affects long-run output growth. We use Finnish data, since to the best of our knowledge the Finnish land and water construction investments series is the best available sufficiently long time series on aggregate infrastructure investments. The Finnish data provide strong and robust evidence indicating that permanent changes in government infrastructure policy have permanent effects on the growth rate of output.     

A systemic approach to modelling and estimating demand for money(ies)

This article uses a consumer theory-based systemic approach to model the demand for monetary liquid asset holdings in Chile. We implement the suggestions and caveats of aggregation theory for the estimation of a demand system for liquid assets (monies) in static, dynamic and time-varying parameters setups. Our results are robust and theoretically consistent with consumer theory restrictions, as a system derived from a utility maximizing framework and a quasi concave utility function. In our estimations, we find stability of interest rate elasticities, in contrast to previous related literature. We also document evidence that long (short) maturity rates are associated to less (more) liquid assets.     

Headlights on tobacco road to low birthweight outcomes

Low birthweight outcomes are associated with considerable social and economic costs, and therefore the possible determinants of low birthweight are of great interest. One such determinant which has received considerable attention is maternal smoking. From an economic perspective this is in part due to the possibility that smoking habits can be influenced through policy conduct. It is widely believed that maternal smoking reduces birthweight; however, the crucial difficulty in estimating such effects is the unobserved heterogeneity among mothers and the fact that estimation of conditional mean effects seems potentially inappropriate. We provide a unified view on the estimation of relationships between prenatal smoking and birthweight outcomes with quantile regression approaches for panel data and emphasize their differences. This paper contributes to the literature in three ways: (i) we focus not only on one technique, but provide evidence from several approaches and highlight a variety of statistical issues; (ii) the performance of the methods are thoroughly tested in a simulated environment, and recommendations are given on their appropriate use; (iii) our results are based on a detailed data set, which includes many relevant control variables for socio-economic, wealth, and personal characteristics.     

Big data analytics in economics: What have we learned so far,and where should we go from here?

Research into predictive accuracy testing remains at the forefront of the forecasting field. One reason for this is that rankings of predictive accuracy across alternative models, which under misspecification are loss function dependent, are universally utilized to assess the usefulness of econometric models. A second reason, which corresponds to the objective of this paper, is that researchers are currently focusing considerable attention on so‐called big data and on new (and old) tools that are available for the analysis of this data. One of the objectives in this field is the assessment of whether big data leads to improvement in forecast accuracy. In this survey paper, we discuss some of the latest (and most interesting) methods currently available for analyzing and utilizing big data when the objective is improved prediction. Our discussion includes a summary of various so‐called dimension reduction, shrinkage and machine learning methods as well as a summary of recent tools that are useful for ranking prediction models associated with the implementation of these methods. We also provide a brief empirical illustration of big data in action, in which we show that big data are indeed useful when predicting the term structure of interest rates.     

Embracing Model Uncertainty: Strategies for Response Pooling and Model Averaging

Econometricians modeling Stated Preference (SP) data, and most other types of data, are confronted with the uncomfortable reality that our knowledge of the “true” model is limited, with only certain variables suggested by the application at hand and general classes of functional forms and error structures suggested by the literature. Accepting our limited knowledge, we pursue strategies for analyzing SP data that are more robust to uncertainties in our knowledge of the true model. These include non-parametric and parametric likelihood-based tests of pooling responses from different elicitation formats, and a frequentist-based model averaging approach for estimating willingness to pay functions. We argue that these strategies lead to increased econometric integrity and empower the ultimate users of models, such as policy decision-makers and even juries, to better assess the robustness of the results. We apply these approaches to an SP survey of saltwater angling in Alaska which utilized split-sample rankings and ratings elicitation methods. While an important goal of our paper is to develop practicable modeling strategies that will ultimately lead to more robust conclusions and more confidence by the users of SP results, an equally important goal is to engender a critical discussion of how we can make the analysis of SP data more robust.     

The Washington consensus works: Causal effects of reform, 1970-2015

Traditional policy reforms of the type embodied in the Washington Consensus have been out of academic fashion for decades. However, we are not aware of a paper that convincingly rejects the efficacy of these reforms. In this paper, we define generalized reform as a discrete, sustained jump in an index of economic freedom, whose components map well onto the points of the old consensus. We identify 49 cases of generalized reform in our dataset that spans 141 countries from 1970 to 2015. The average treatment effect associated with these reforms is positive, sizeable, and significant over 5- and 10- year windows. The result is robust to different thresholds for defining reform and different estimation methods. We argue that the policy reform baby was prematurely thrown out with the neoliberal bathwater.     

On the income dependence of equivalence scales

Household consumption exhibits economies of scale as the number of household members increases. We collect survey data from two countries, Germany and France, in order to obtain direct subjective estimates of household consumption economies of scale, and, in particular, to examine an additional dimension: whether household consumption economies of scale change as living standards go up. Our data from both countries indicate strongly that household economies of scale increase as the living standard goes up. We discuss the robustness of our survey method and compare our results to these of alternative estimation methods in the literature.     

How effective is the Free Trade Agreement in South Asia? An empirical investigation

We investigate the efficacy of preferential trade liberalization in changing the observed trade pattern among the South Asian countries that have entered into the South Asian Free Trade Agreement (SAFTA). Although in its nascent stage, some data are now available to provide an ex-post evaluation of the performance of this bloc. Using these data, we find no empirical evidence of trade creation among SAFTA members, which is not surprising given that tariff concessions in SAFTA are small and are offset by complicated rules of origin procedure. However, a substantial and statistically significant increase in exports from SAFTA members to the rest of the world is found. Several panel strategies are used to check the sensitivity of the results against the assumptions of the estimation strategies. As some key coefficient estimates are found to differ across estimation methods, policymakers in South Asia need to use care in relying on the results from empirical studies, including our own, in formulating their trade policies.     

Do better institutions offset the adverse effect of a financial crisis on investment? Evidence from East Asia

We extend the neoclassical investment theory to capture the deviation of current institutional quality from its ideal level. We show that an improvement in institutions increases the speed of adjustment towards desired capital stock and the marginal product of capital and hence enhances the investment demand. Next, we apply our theoretical model to East Asia. Using Generalized Method of Moments, we estimate a dynamic panel data model to investigate the effect of alternate measures of institutional quality on investment dynamics in this region. Our findings underline the role of institutions as a deep determinant of investment. We also conclude that the East Asian countries with better institutional quality are less prone to the adverse effects of financial crises on their fixed capital formation. Our results are robust to the estimation methods and changes in institutional measures and hence might have important policy implication for policymakers and investors in East Asia.     

Je t'aime, moi non plus: Bilateral opinions and international trade

This paper studies the relationship between bilateral trade patterns and opinions. It uses the Eurobarometer public opinion surveys published by the European Commission, which provide data on the share of the population in each EU15 member country in favour of each CEEC joining the EU. Our results first suggest that bilateral opinions have a statistically robust and relatively large effect on imports, even when standard and new covariates capturing proximity between countries are controlled for. We interpret this effect as reflecting a positive impact of “bilateral affinity” on trade patterns. We also show that it is possible to go some way towards explaining the variance in bilateral opinions among our sample. Last we provide some preliminary attempt to determine causality between bilateral opinions and imports.     

TESTING THE STRUCTURE OF CONDITIONAL CORRELATIONS IN MULTIVARIATE GARCH MODELS: A GENERALIZED CROSS‐SPECTRUM APPROACH*

We introduce a class of generally applicable specification tests for constant and dynamic structures of conditional correlations in multivariate GARCH models. The tests are robust to the presence of time‐varying higher‐order conditional moments of unknown form and are pure significance tests. The tests can identify linear and nonlinear misspecifications in conditional correlations. Our approach does not necessitate a particular parameter estimation method and distributional assumption on the error process. The asymptotic distribution of the tests is invariant to the uncertainty in parameter estimation. We assess the finite sample performance of our tests using simulated and real data.