A visualization tool for exploratory analysis of cyclic multivariate data

This paper presents an interactive visualization tool for the qualitative exploration of multivariate data that may exhibit cyclic or periodic behavior. Glyphs are used to encode each multivariate data point, and linear, stacked, and spiral glyph layouts are employed to help convey both intra-cycle and inter-cycle relationships within the data. Users may interactively select glyph and layout types, modify cycle lengths and the number of cycles to display, and select the specific data dimensions to be included. We validate the usefulness of the system with case studies and describe our future plans for expanding the system's capabilities.     

Hypothesis tests using data envelopment analysis

A substantial body of recent work has opened the way to exploring the statistical properties of DEA estimators of production frontiers and related efficiency measures. The purpose of this paper is to survey several possibilities that have been pursued, and to present them in a unified framework. These include the development of statistics to test hypotheses about the characteristics of the production frontier, such as returns to scale, input substitutability, and model specification, and also about variation in efficiencies relative to the production frontier.     

A multivariate unobserved component analysis of US housing market

This study explores price dynamics and price relationships in the US housing market with a focus on four regions: Northeast, Midwest, South, and West. It applies a multivariate state-space model to identify the common trends and common cycles in US regional markets. The study finds that the principal source of secular price variability in the Northeast and West markets is due to two common stochastic trends, while a large share of transitional price variability in the Northeast, West and Midwest originates from three common stochastic cycles. The study estimates the relationships between the common unobserved components and economic variables and finds that unemployment, federal funds rate, corporate default risk, economic expansion, unanticipated inflation in the construction market are significant underlying economic phenomena that impact the evolution of the common movements in both the short run and the long run housing dynamics. Authorship is equally shared between the authors.     

An analysis of stock market transactions data

Many studies have argued against the strict form of the efficient markets hypothesis (EMH) by concluding that a lagged relationship exists between volume and the absolute value of a price change. These studies have denied a priori the possibility of a contemporaneous relationship. If a simultaneous relationship exists then least squares with only lagged variables suffers from omitted variable bias, and least squares with contemporaneous variables may suffer from simultaneous equations bias. Investigating these possibilities, this study demonstrates that simultaneity exists and that previous findings of a lagged relationship between the variables are therefore due to specification error. System estimation techniques suggest that the price-volume relationship is recursive, with the absolute value of a price change causing volume contemporaneously, but not conversely.     

Stability and integrity tests in data envelopment analysis

Studies that apply data envelopment analysis often neglect testing the stability of the efficient frontier to data perturbations, and, to a lesser extent, the ability of efficiency scores to correctly discriminate between units on performance (integrity). Our primary motivation is to demonstrate methods that can help reduce the number of managerial decisions based on results that may be unreliable. To this end, we illustrate multiple tests of stability and integrity in an environment of fully units-invariant efficiency measurement. This application of tests of stability and integrity using a slacks-based measure of efficiency is the first in a peer-reviewed journal.     

Independent component analysis for realized volatility: Analysis of the stock market crash of 2008

This paper investigates the factors that drove the U.S. equity market returns from 2007 to early 2010. The period was highlighted by volatile energy and commodity prices, the collapse of insurance and banking firms, extreme implied volatility and a subsequent rally in the overall market. To extract the driving factors, we decompose the returns of the S&P500 sector ETFs into statistically independent signals using independent component analysis. We find that the generated factors have interesting financial interpretations and are consistent with the major economic themes of the period. We find that there are two sets of general market betas during the period along with a dominant factor for energy and materials sector. In addition, we find that the EGARCH model which accommodates asymmetric responses between returns and volatility can plausibly fit the high levels of variance during the crash. Finally, estimated correlations dropped when commodity prices moved higher, but then spiked when the S&P500 crashed in late 2008.     

Technology and market structure in the analysis of exchange

Economists treat the ‘market’ as a black box in which prices rise and fall to equate supplies with demands. Obviously, however, markets are usually collections of institutions. Using conventional methods of economic analysis, it is argued that certain physical characteristics of commodities, the technologies of their production and use, and buyer and seller concentration lead to the development of particular institutional arrangements composing markets. These institutional arrangements are efficient in the sense that they require the least use of resources in effecting transactions.     

Emergence of a core-periphery structure in a simple dynamic model of the interbank market

This paper studies a simple dynamic model of interbank credit relationships. Starting from a given balance sheet structure of a banking system with a realistic distribution of bank sizes, the necessity of establishing interbank credit connections emerges from idiosyncratic liquidity shocks. Banks initially choose potential trading partners randomly, but over time form preferential relationships via an elementary reinforcement learning algorithm. As it turns out, the dynamic evolution of this system displays a formation of a core-periphery structure with mainly the largest banks assuming the roles of money center banks mediating between the liquidity needs of many smaller banks. Statistical analysis shows that this evolving interbank market shares the majority of the salient characteristics of interbank credit relationship that have been put forth in recent literature. Preferential interest rates for borrowers with strong attachment to a lender may prevent the system from becoming extortionary and guarantee the survival of the small peripherical banks.     

Forecasting crude oil futures market returns: A principal component analysis combination approach

To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can not only guard against over-fitting by employing the PCA technique but also reduce forecast variance due to extensive forecast combinations, thus benefiting from both the combination of information and the combination of forecasts. Showing impressive out-of-sample forecasting performance, the PCA combination approach outperforms a benchmark model and many related competing models. Furthermore, a mean–variance investor can realize sizeable utility gains by using the PCA combination forecasts relative to the competing forecasts from an asset allocation perspective.     

Capital market integration in ASEAN: A non-stationary panel data analysis

Motivated by the establishment of ASEAN Economic Community (AEC) at the end of 2015, we examine saving-investment relationship in various subgroups of ASEAN to assess their capital market integration. The results from second generation panel unit-root and cointegration tests that account for cross-sectional dependence as well as estimates of long-run saving-retention rate provide some evidence of market integration in ASEAN. The analysis of short-run dynamics suggests that capital mobility in ASEAN during 1980–2014 appears similar to that in OECD countries during 1970–1999. More importantly, across different panel estimators and subgroups of membership, there is considerable heterogeneity among the member countries. The saving-investment association is very weak, thereby implying very high capital mobility, in more developed members such as Singapore, Malaysia, and Brunei; the association is very strong, implying very low capital mobility, for much less developed members such as Laos, Myanmar, and Cambodia. The results call for renewed effort to develop capital markets in less developed nations and integrate them with the rest of the membership in ASEAN. In this paper, we also address several major shortcomings of the original Feldstein-Horioka framework.     

Some model-restricted shrinkage estimators for contingency tables with missing data

For contingency tables with extensive missing data, the unrestricted MLE under the saturated model, computed by the EM algorithm, is generally unsatisfactory. In this case, it may be better to fit a simpler model by imposing some restrictions on the parameter space. Perlman and Wu (1999) propose lattice conditional independence (LCI) models for contingency tables with arbitrary missing data patterns. When this LCI model fits well, the restricted MLE under the LCI model is more accurate than the unrestricted MLE under the saturated model, but not in general. Here we propose certain empirical Bayes (EB) estimators that adaptively combine the best features of the restricted and unrestricted MLEs. These EB estimators appear to be especially useful when the observed data is sparse, even in cases where the suitability of the LCI model is uncertain. We also study a restricted EM algorithm (called the ER algorithm) with similar desirable features. Received: July 1999     

Locally minimax tests for a multinormal data problem

LetX be ap-normal random vector with unknown mean and unknown covariance matrix and letX be partitioned asX=(X ₍₁₎ ,X ₍₂₎ , ...,X _(r) ) whereX _(j) is a subvector of dimensionp _j such that _j=1 ^r p _j =p. We show that the tests, obtained by Dahel (1988), are locally minimax. These tests have been derived to confront H_o: =0 versusH ₁: 0 on the basis of sample of sizeN, X ₁, ..., X_N, drawn fromX andr additional samples of sizeN _j, U _i ^(j) , i=1, ..., N_j, drawn fromX ₍₁₎, ...X _(r) respectively. We assume that the (r+1) samples are independent and thatN _j>p _j forj=0, 1, ..., r (N _oN andp _op). Whenr=2 andp=2, a Monte Carlo study is performed to compare these tests with the likelihood ratio test (LRT) given by Srivastava (1985). We also show that no locally most powerful invariant test exists for this problem.     

On the suitability of income inequality measures for regional analysis: Some evidence from simulation analysis and bootstrapping tests

The paper looks at the sensitivity of commonly used income inequality measures to changes in the ranking, size and number of regions into which a country is divided. During the analysis, several test distributions of populations and incomes are compared with a ‘reference’ distribution, characterized by an even distribution of population across regional subdivisions. Random permutation tests are also run to determine whether inequality measures commonly used in regional analysis produce meaningful estimates when applied to regions of different population size. The results show that only the population weighted coefficient of variation (Williamson’s index) and population-weighted Gini coefficient may be considered sufficiently reliable inequality measures, when applied to countries with a small number of regions and with varying population sizes.     

Developed stock market reaction to political change: a panel data analysis

This paper utilized panel data to examine the effects of political change in developed stock market. According to Hausman test, we capture the stock return by the fixed-effect model to fit the stock market. Political change was originally intended as an incumbent party impetus to create opportunities for progress. However, this has caused great political party distress, creating political change with an inverse stock return relationship in developed countries.     

Outside and inside the regression “black box” from exploratory to interior data analysis

Good data analysis consists of three phases: (1) preliminary analysis, (2) confirmatory analysis (model testing), and (3) interior analysis (model checking). Social scientists doing quantitative research usually concentrate on only one of the three: confirmatory analysis. I argue that there is much to be learned from careful preliminary and interior analyses. I present an extensive example of data analysis for each of the three phases using the same data set in each phase. Rather than surveying all the possible tools available in each phase of data analysis, I concentrate on Exploratory Data Analysis techniques (stem-and-leaf plot, letter-value display, box plot, and power transformations) for the preliminary phase, on OLS for the confirmatory phase, and on residuals, leverage and single-case influence measures for interior analysis.     

Some tests for the constancy of regressions under heteroscedasticity

For testing the equality of coefficients of a linear regression model under heteroscedasticity, we suggest an F criterion conditioned on the posterior mean of the ratio of standard deviations of error terms in two subsamples. For pairable subsamples, and exact F test is derived. Sampling experiments show that the Chow test differs substantially from the nominal significance level when the two subsample sizes are unequal, and that the F test conditioned on the posterior mean is superior to other tests when sample sizes are small.     

Labor market analysis for developing countries

This paper is about analyzing labor markets in developing countries, searching for both improved understanding and greater policy relevance. Following a five-part policy evaluation framework, the highlights of labor markets in developing countries are presented. Theoretical models with multiple sectors and segments and empirical analysis using different kinds of data are then reviewed. A brief concluding section addresses some priority research needs.