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The contribution of this study is to disclose the main determinants of the duration of the U.S. antitrust federal court decisions, an issue that has been nearly overlooked by most related studies. As an indirect measure for the time needed to dispose cases, this study uses the duration of the case from the filing of the complaint to the date of the opinion. Using this metric, we employ parametric and nonparametric panel data techniques on a sample of 613 appellate court proceedings on U.S. antitrust cases during the period 1995–2018. The empirical research reveals spatial heterogeneity in terms of case duration among the circuits of the appellate U.S. courts. The econometric analysis supports that the duration of appellate antitrust court decisions depends on administrative-related factors including the way the case was filed in the circuit, the jurisdiction in the case, the " pro se " representation of the undertakings, and the nature of the final judgment. Lastly, based on the flexible semi-parametric analysis, we argue that the impact of these parameters on case duration is linear, regardless of the specification of the parametric part of the model. [ABSTRACT FROM AUTHOR]

This study provides an econometric methodology to test a linear structural relationship among economic variables. We propose the so-called distance-difference (DD) test and show that it has omnibus power against arbitrary nonlinear structural relationships. If the DD-test rejects the linear model hypothesis, a sequential testing procedure assisted by the DD-test can consistently estimate the degree of a polynomial function that arbitrarily approximates the nonlinear structural equation. Using extensive Monte Carlo simulations, we confirm the DD-test's finite sample properties and compare its performance with the sequential testing procedure assisted by the J-test and moment selection criteria. Finally, through investigation, we empirically illustrate the relationship between the value-added and its production factors using firm-level data from the United States. We demonstrate that the production function has exhibited a factor-biased technological change instead of Hicks-neutral technology presumed by the Cobb–Douglas production function. [ABSTRACT FROM AUTHOR]

We analyse the spatial diffusion of new COVID-19 cases and the countrywide impact of state-specific containment policies during the early months of the COVID-19 pandemic in the United States. We first use spatial econometric techniques to document direct and indirect spillovers of new infections across county and state lines, as well as the impact of individual states' lockdown policies on infections in neighbouring states. We find consistent statistical evidence that new cases diffuse across county lines, holding county-level factors constant, and that the diffusion across counties was affected by the closure policies of adjacent states. We then develop a spatial version of the epidemiological susceptible–infected–recovered (SIR) model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighbouring states. We incorporate lockdown policies into our model and calibrate the model to match both the cumulative and the new infections across the 48 contiguous US states and DC. Our results suggest that had the states with the less restrictive social distancing measures tightened them by one level, the cumulative infections in other states would be about 5% smaller. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run. [ABSTRACT FROM AUTHOR]

Problem definition: Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, including the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews. To our knowledge, this is the first paper that empirically studies how the review dispersion affects a seller's activity level and the number of matches in an online marketplace with active sellers. Distinct from literature, we examine the relationship between the review dispersion and supply-side activities in an online service marketplace. Methodology/results: We collaborated with one of the largest online solar marketplaces in the United States that connects potential solar panel adopters with installers. We obtained a unique data set from the marketplace for 2013 − 2018. We complement this with public data sets. Our analysis uses traditional econometrics methods, a clustering method, and the deep-learning-based natural-language-processing model BERT developed by Google AI. We find that the dispersion in customer reviews has a significant and inverted U-shaped relationship with an installer's marketplace activity level. Intuitively, a marketplace operator would favor having more sellers with perfect ratings. In contrast, we identify a significant and inverted U-shaped relationship between the market-level review dispersion and transactions. Managerial implications: Our paper provides key insights to marketplace operators and sellers. We find that in contrast to general belief, an operator can improve its market transactions by keeping/promoting sellers with low ratings or avoiding (negative) review filtering. Furthermore, sellers' implementation of "rating gating" to avoid negative reviews may backfire for them by reducing their matches. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0104. [ABSTRACT FROM AUTHOR]

Thus far, the data envelopment analysis (DEA) model has not been used to examine the lagged effects of advertising goodwill stock on promotional efficiency. Thus, this study develops a dynamic DEA framework to illustrate the lagged effect of advertising goodwill stock on the dynamic promotional efficiency of major automobile manufacturers in the United States. The advertising goodwill stock in five automobile producers was measured using the econometric model. Subsequently, the advertising goodwill stock was treated as a lagged effect in a dynamic DEA framework. We analyse the empirical results and formulate the following two important findings: First, the results demonstrate that the lagged effect of advertising goodwill stock can lead to changes in the promotional efficiency of advertising. Second, the paper discusses ways that inefficient automobile manufacturers can improve marketing strategy implementation. [ABSTRACT FROM AUTHOR]

Objective: To analyze the competitiveness of the Mexican tomato in the United States market, Methodology: The normalized revealed comparative advantage index was used, regressions with robust estimators, the Hodrick-Prescott filter was applied to extract the trend and guarantee non-seasonality. Results: Mexico maintains the comparative advantage revealed in the US market, with a growing and consistent competitiveness throughout the period analyzed. This competitiveness is strengthened by belonging to the Trade Agreement, by reinforcing the export process. Limitations: A bilateral Mexico-United States analysis is considerate, which, although it responds to the proposed research objective, favors continuing to investigate specifically the dynamics of the United States market because decrease in the proportion of the market is identified despite the substantial increase in exports. Conclusions: The VCRN estimate shows that, for the study period, the Mexican tomato has positioned itself in the US market, with a growing trend; The Hodrick-Prescott filter was applied to extract the trend and ensure the non-seasonality of the VCRN, which in turn allowed a better fit in the model, the F test determined that the model was globally accepted to explain the phenomenon. [ABSTRACT FROM AUTHOR]

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is a complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) and a multi-layered perceptron artificial neural network (mlp-NN), over the Southwestern United States (SWUS), based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations on monthly and seasonal timescales from 1990–2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from the North American Regional Reanalysis (NARR) dataset. The model's performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BIAS). The ANFIS model generally performs better than the mlp-NN model in predicting regional dustiness over the SWUS region, with r = 0.77 and 0.83 for monthly and seasonal fine dust, respectively. AI models perform better in predicting regional dustiness on a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust on both monthly and seasonal timescales. Compared to precipitation, air temperature is the more important predictor of regional dustiness on both monthly and seasonal timescales. The relative importance of air temperature is higher on the monthly timescale than the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models can predict monthly and seasonal fine and coarse dust, based on the limited climatic data, with good accuracy and with potential implications for research in data sparse regions. [ABSTRACT FROM AUTHOR]

Investments in the architectural, engineering, and construction (AEC) sector can be used by governments to introduce desired changes in the economy, which gives an indication of the future trends of the economy. In addition, the stock market—as reflected by the Standard and Poor 500 (S&P 500)—creates a sense of confidence about the direction of the US economy. Many studies have shown that stock prices in multiple international industries could be considered as leading economic indicators. Nevertheless, this was not researched within the AEC sector in the United States. This paper addresses this knowledge gap by investigating whether the stock prices of AEC companies in the United States could be used to inform strategic, proactive decision making. First, time series data were collected for the stock prices of 16 large AEC companies as well as for the S&P 500 index. Second, multivariate time series analysis was conducted to examine the type of relationship between the response variable—S&P 500—and all other input variables. Two econometrics models were developed: a linear, statistical vector autoregression model and a computational, deep learning model based on long–short-term memory (LSTM) networks. Third, the two developed econometrics models were tested on unseen data. Fourth, the best econometrics model was verified using another leading economic indicator which is the Purchasing Managers' Index. The results reflected that the computational LSTM model outperformed the linear statistical model. The findings showed that the stock prices of AEC companies could be considered as an additional and complementing leading economic indicator to the American Institute of Architects' Architecture Billings Index to provide a broader perspective of the business ecosystem. This paper adds to the body of knowledge by providing an econometrics model for helping in optimal investment opportunities, taking informed strategic management and business judgments, and predicting changes in the economy. [ABSTRACT FROM AUTHOR]

We examine the manner in which the population prevalence of disordered gambling has usually been estimated, on the basis of surveys that suffer from a potential sample selection bias. General population surveys screen respondents using seemingly innocuous "trigger," "gateway" or "diagnostic stem" questions, applied before they ask the actual questions about gambling behavior and attitudes. Modeling the latent sample selection behavior generated by these trigger questions using up-to-date econometrics for sample selection bias correction leads to dramatically different inferences about population prevalence and comorbidities with other psychiatric disorders. The population prevalence of problem or pathological gambling in the United States is inferred to be 7.7%, rather than 1.3% when this behavioral response is ignored. Comorbidities are inferred to be much smaller than the received wisdom, particularly when considering the marginal association with other mental health problems rather than the total association. The issues identified here apply, in principle, to every psychiatric disorder covered by standard mental health surveys, and not just gambling disorder. We discuss ways in which these behavioral biases can be mitigated in future surveys. [ABSTRACT FROM AUTHOR]

Beginning in the 1950s, a group of scholars primarily associated with the University of Chicago began to challenge many of the fundamental tenants of antitrust law. This movement, which became known as the Chicago School of Antitrust Analysis, profoundly altered the course of American antitrust scholarship, regulation, and enforcement. What is not known, however,isthe degree to whichChicago School ideas influenced the antitrust regimes of other countries. By leveraging new datasets on antitrust laws and enforcement around the world, we empirically explore whether ideas embraced by the Chicago School diffused internationally. Our analysis illustrates that many ideas explicitly rejected by the Chicago School--such as using antitrustlaw to promote goals beyond efficiency or regulate unilateral conduct--are common features of antitrust regimes in other countries. We also provide suggestive evidence that the influence of the antitrust revolution launched by the Chicago School has been more limited outside of the United States. [ABSTRACT FROM AUTHOR]

In this paper, we describe mating patterns in the USA from 1964 to 2017 and measure the impact of changes in marital preferences on between-household income inequality. We rely on the recent literature on the econometrics of matching models to estimate complementarity parameters of the household production function. Our structural approach allows us to measure sorting along multiple dimensions and to effectively disentangle changes in marital preferences and in demographics, addressing concerns that affect results from existing literature. We answer the following questions: Has assortativeness increased over time? Along which dimensions? To what extent can the shifts in marital preferences explain inequality trends? We find that, after controlling for other observables, assortative mating in education has become stronger. Moreover, if mating patterns had not changed since 1971, the 2017 Gini coefficient between married households would be 6% lower. We conclude that about 25% of the increase in between-household inequality is due to changes in marital preferences. Increased assortativeness in education positively contributes to the rise in inequality, but only modestly. [ABSTRACT FROM AUTHOR]

The author uses a spatial econometric framework to examine the determinants of teacher salaries in the United States, including union activity in the teachers' own and in neighboring districts, teacher salaries in nearby districts, and other school district characteristics such as size and student-teacher ratios.Using the 1999-2000 Schools and Staffing Survey as well as the School District Demographic System and Bureau of Labor Statistics data sets, he finds that union activity (measured by the legal status of collective bargaining and teacher union membership density) increases salaries for experienced teachers by as much as 18 to 28%; it increases salaries for beginning teachers, however, by considerably less. Results also confirm that salaries for experienced and beginning teachers are positively affected by the salaries of teachers in nearby districts. A one-percent increase in the distance-weighted average of teachers salaries in nearby districts increases salaries in a given district by 0.52% for experienced teachers and 0.66% for beginning teachers. Studies that ignore such spatial dependence are likely to be mis-specified and may lead to misleading conclusions. [ABSTRACT FROM AUTHOR]

This paper presents a small-sample study of the three-equation-three variable New-Keynesian macro model. While the point estimates imply that the Fed has been stabilizing inflation fluctuations since 1980, our econometric analysis suggests considerable uncertainty regarding the stance of the Fed against inflation. The canonical New-Keynesian macro model is strongly rejected by the likelihood ratio test, but we propose the direction in which it needs to be modified in order to fit the data. [ABSTRACT FROM AUTHOR]

Using new econometric methods, we separately estimate average transaction costs for over 167,000 bonds from a 1-year sample of all U.S. municipal bond trades. Municipal bond transaction costs decrease with trade size and do not depend significantly on trade frequency. Also, municipal bond trades are substantially more expensive than similar-sized equity trades. We attribute these results to the lack of bond market price transparency. Additional cross-sectional analyses show that bond trading costs increase with credit risk, instrument complexity, time to maturity, and time since issuance. Investors, and perhaps ultimately issuers, might benefit if issuers issued simpler bonds. [ABSTRACT FROM AUTHOR]

Empirical estimates of monetary policy rules suggest that the behavior of U.S. monetary policymakers changed during the past few decades. However, for that same time period, statistical analyses of lagged representations of the economy, such as VARs, often have not rejected the null of structural stability. These two sets of empirical results appear to contradict the Lucas critique. This paper reconciles these results with the Lucas critique by showing that the apparent policy invariance of reduced forms is consistent with the magnitude of historical policy shifts and the relative insensitivity of the reduced forms of plausible forward-looking macroeconomic specifications to policy shifts. [ABSTRACT FROM AUTHOR]

In this article the researchers use Lumsdaine and Papell tests for a unit root in the presence of unrestricted structural change and Papell and Prodan test for a unit root in the presence of restricted structural change to study the long-term real gross domestic product (GDP) in the U.S. The unit root null is rejected at the 1% level with both tests. Using the Murray and Nelson method to evaluate the rejections, the researchers discover that, unlike the Augmented-Dickey-Fuller (ADF) tests, the unit root null is not rejected too often if the data generating process is a unit root with transitory fluctuations.

Real equipment investment in the United States boomed in the 1990s, led by soaring investment in computers. We find that traditional aggregate econometric models completely fail to capture the magnitude of this growth—mainly because these models neglect to address two features that were crucial (and unique) to the 1990s' investment boom. First, the pace at which firms replace depreciated capital increased. Second, investment was more sensitive to the cost of capital. We document that these two features stem from the special behavior of investment in computers and therefore propose a disaggregated approach. This produces an econometric model that successfully explains the 1990s' equipment investment boom. [ABSTRACT FROM AUTHOR]

The persistence of shocks to aggregate output has been the subject of continuing investigation since Nelson and Plosser (1982) suggested they are largely permanent. Recent literature reaches mixed conclusions, largely due to disagreement about how to treat the Great Depression. We estimate output persistence based on a parametric bootstrap of a Markov-switching model for GDP 1870-1994 in which the economy can switch in and out of a turbulent state. Our results suggest that real shocks persist indefinitely if we drop the maintained assumption of homoskedasticity in favor of a Markov-switching representation of the Great Depression. [ABSTRACT FROM AUTHOR]

We attempt to translate Neo-Austrian ideas about the workings of financial markets, as originally advanced by F. A. Hayek, into the standard probabilistic language of modern finance. We focus on an apparent paradox, namely the insistence of Neo-Austrians on order (i.e., stationarity) together with ever-reemerging inefficiencies. The paper's findings have implications beyond Neo-Austrian theory: They demonstrate how easy it is to reject market efficiency, but how much more difficult it is to discern the nature of the inefficiency. We illustrate our findings with price data from the U.S. Treasury bill market over the period 1962 to 1999. There is ample evidence that the price of a three-month Treasury bill is not a random walk, yet the sign of the average price change is erratic, so that inference about the nature of the inefficiency is unreliable. [ABSTRACT FROM AUTHOR]

This paper presents econometric evidence on whether the founding of the Federal Reserve in 1914 caused a structural change from level stationarity to difference stationarity in U.S. and U.K. short-term nominal interest rates. We develop new econometric tests that allow for parameter transitions to test for a break of this kind and under- take a grid search analysis of dates and speeds for the change. We find that U.S. nominal interest rales most likely evolved rapidly to difference stationarity in June 1917. For the United Kingdom we fail to reject the null that U.K. interest rate series follow a difference stationary process over the entire period 1890-1934. Our analysis differs from previous research on this topic in that we take care to explore statistical uncertainty around parameter estimates, and incorporate higher-order dynamics into our econometric analysis. [ABSTRACT FROM AUTHOR]

We investigate the demand for money and the degree of substitutability among monetary assets in the United States using the generalized Leontief and the Minflex Laurent (ML) models as suggested by Serletis and Shahmoradi (2007). In doing so, we merge the demand systems literature with the recent financial econometrics literature, relaxing the homoskedasticity assumption and instead assuming that the covariance matrix of the errors of flexible demand systems is time-varying. We also pay explicit attention to theoretical regularity, treating the curvature property as a maintained hypothesis. Our findings indicate that only the curvature constrained ML model with a Baba, Engle, Kraft, and Kroner (BEKK) specification for the conditional covariance matrix is able to generate inference consistent with theoretical regularity. [ABSTRACT FROM AUTHOR]

I discuss the econometrics and the economics of past research on the effects of minimum wages on employment in the United States. My intent is to try to identify key questions raised in the recent literature, and some from the earlier literature, which I think hold the most promise for understanding the conflicting evidence and arriving at a more definitive answer about the employment effects of minimum wages. My secondary goal is to discuss how we can narrow the range of uncertainty about the likely effects of the large minimum wage increases becoming more prevalent in the United States. I discuss some insights from both theory and past evidence that may be informative about the effects of high minimum wages, and try to emphasize what research can be done now and in the near future to provide useful evidence to policymakers on the results of the coming high minimum wage experiment, whether in the United States or in other countries. [ABSTRACT FROM AUTHOR]

By using the spatial econometrics methodology, this paper investigates the contagion of the risk taking by banks in the US banking sector during 2001 to 2012. In addition, the contagion signals up to the Subprime crisis in 2008 are analyzed and different channels of contagion are studied in order to identify fragile groups of banks. Our analysis reveals that there is no significant contagion transmitted to the whole banking system. However, we observe that the bank contagion is significantly spread locally and for the group of banks that share similar characteristics related to size and bank regulations. [ABSTRACT FROM AUTHOR]

The article analyzes the role of religion on the decision making of judges in the United States. Topics mentioned include the examination of religion based on a category of ideology, the link between religious identification and outcomes, and the use of econometric techniques to determine the role of political and religious ideology in impacting voting behavior.

Objective: To understand the role of county characteristics in the growing divide between rural and urban mortality from 1980 to 2010.Data Source: Age-adjusted mortality rates for all U.S. counties from 1980 to 2010 were obtained from the CDC Compressed Mortality File and combined with county characteristics from the U.S. Census Bureau, the Area Health Resources File, and the Inter-University Consortium for Political and Social research.Study Design: We used Oaxaca-Blinder decomposition to assess the extent to which rural-urban mortality disparities are explained by observed county characteristics at each decade.Principal Findings: Decomposition shows that, at each decade, differences in rural/urban characteristics are sufficient to explain differences in mortality. Furthermore, starting in 1990, rural counties have significantly lower predicted mortality than urban counties when given identical county characteristics. We find changes in the effect of characteristics on mortality, not the characteristics themselves, drive the growing mortality divide.Conclusions: Differences in economic and demographic characteristics between rural and urban counties largely explain the differences in age-adjusted mortality in any given year. Over time, the role these characteristics play in improving mortality has increased differentially for urban counties. As characteristics continue changing in importance as determinants of health, this divide may continue to widen. [ABSTRACT FROM AUTHOR]

No abstract is available for this article. [ABSTRACT FROM AUTHOR]

This article discusses a study of the econometric properties of alternative security return methods. The author explores the econometric properties of the unsystematic security return metric in a simple setting of limited parameter uncertainty. He then compares the differences in return approach with the unsystematic returns approach in the same setting. The results indicate that an unsystematic returns produce more powerful tests and more efficient estimates than differences in returns. Major implications of the results are discussed.

The article examines the evidence on cointegration of money demand and determines whether cointegrating relation of the broad monetary aggregate (M2) demand exists in the U.S. The empirical results in this study suggest that the data structure of M2 demand changed significantly in the 1990s. Accordingly, a M2 error-correction model is no longer appropriate to adopt in applications. According to the author, the theory of cointegration tells that when cointegration exists, a short-run dynamic system should be expressed as a vector error-correction (VEC) model. Since M2 cointegration is no longer supported by the full-sample results, the author expects that a VEC model becomes inappropriate for short-run analyses in the 1990s.

The article presents an evaluation of the U.S. Federal Funds rate as a superior proxy for forecasting monetary policy. The connection of the funds rate to Federal Reserve policy is established but questions are raised concerning its disproportional use, suggesting that its information content is not more unique than other open market indicators. The specific content relayed within the Federal Funds rate is outlined and compared to Treasury bill rates and short term government repurchases, establishing evidence of equivalence to the market interest rate and general financial market efficiency.

The article comments on the paper "The Welfare Gain from the Introduction of Indexed Bonds," by Alan D. Viard. The author praises the contribution of the paper regarding the portfolio management and consumption behavior of consumers in, and out, of indexed-bond markets. Viard concludes that the welfare gains from introducing trading in indexed bonds would be extremely small. Despite this, the author believes indexed-bonds still have some value because there is evidence households are operating far from the constrained optimum.

The article introduces a safe financial asset in zero net supply and analyzes if there is a welfare gain from it. The author connects this welfare gain to risky asset returns and investors' preferences. The article discusses each individual's welfare gain and its relationship to the variance of the minimum variance portfolio. He contends that the results of this study clarify how nominal bonds increase welfare. The model he devises uses only two risky assets in order that the effects of inflation uncertainty can be studied more effectively.

The article discusses the conditional mean and variance of inflation from United States time series data using the statistical technique, autoregressive conditional heteroscedasticity (ARCH). The author states that the ARCH model was developed by researcher Robert F. Engle and allows the variance of a regression to change over time. He explains that the key finding is that the variance of inflation in the seventies was only slightly greater than in the sixties and both were below the variances in the late forties and early fifties. He believes that even though the level of inflation in the seventies was high it was also predictable.

The article focuses on the econometric models constructed to guide the implementation of the Federal Reserve monetary control procedure in the United States. The article examines two econometric models developed to guide the implementation of the Federal Reserve's monetary policy implemented in October 1979. The first model was constructed by the staff economists of the Federal Reserve System's Board of Governors and the second was constructed by John P. Judd and John L. Scadding of the Federal Reserve Bank of San Francisco, California. Several equations are provided to aid in the study.

The authors examine switching and repeal purchase effects of advertising in mature, frequently purchased product categories. They draw on consumer behavior theories of framing and usage dominance to formulate a logit choice model for measuring these effects. They estimate the model using single-source scanner data. Their results suggest that advertising induces brand switching but does not affect the repeat purchase rates of consumers who have just purchased the brand, a result consistent with usage dominance rather than framing. They find the switching influence to be largely confined between the current and previous purchase occasions. They illustrate the magnitude of this effect and explore potential profitability. [ABSTRACT FROM AUTHOR]

The authors propose that a general class of market response models with marketing parameter equations be used for the study of marketing interactions. They inventory alternative model specifications of marketing mix interactions, along with the estimation procedures, and compare two that are relevant in an application to the determinants of U.S. Navy salesforce effectiveness. The authors also illustrate the importance of modeling interactions as a basis for making inferences about marketing mix resource allocation. [ABSTRACT FROM AUTHOR]

This paper describes an active, or symbiotic, decision support system developed for the National Aeronautics and Space Administration called the Generic Operations Simulation Technique (GOST). GOST combines parametric modeling techniques (regression) with discrete-event simulation to cost and plan future space programs. GOST differs from previous process modeling approaches by providing an "artificially intelligent modeling expert" and an "artificially intelligent domain expert" for assisting the user in developing and analyzing process models. An object oriented knowledge representation system provides the foundation for the GOST environment. A taxonomy of Computer Directed Process Managers, implemented within the representation system, controls the appropriate processes responsible for delivering active modeling support. An earlier version of this paper was originally published in the Proceedings of the TwentyFourth Hawaii International Conference on System Sciences (IEEE Computer Society Press, 1991). [ABSTRACT FROM AUTHOR]

The American Customer Satisfaction Index (ACSI) is a new type of market-based performance measure for firms, industries, economic sectors, and national economies. The authors discuss the nature and purpose of ACSI and explain the theory underlying the ACSI model, the nation-wide survey methodology used to collect the data, and the econometric approach employed to estimate the indices. They also illustrate the use of ACSI in conducting benchmarking studies, both cross-sectionally and over time. The authors find customer satisfaction to be greater for goods than for services and, in turn, greater for services than for government agencies, as well as find cause for concern in the observation that customer satisfaction in the United States is declining, primarily because of decreasing satisfaction with services. The authors estimate the model for the seven major economic sectors for which data are collected. Highlights of the findings include that (1) customization is more important than reliability in determining customer satisfaction, (2) customer expectations play a greater role in sectors in which variance in production and consumption is relatively low, and (3) customer satisfaction is more quality-driven than value- or price-driven. The authors conclude with a discussion of the implications of ACSI for public policymakers, managers, consumers, and marketing in general. [ABSTRACT FROM AUTHOR]

Econometric evidence strongly suggests that unemployed job-seekers who use the services of a Public Employment Agency (PEA) have longer unemployment spells than those choosing alternative search methods. Yet, in some well-designed U.S. experiments, increased use of PEA services has been associated with faster transitions in to jobs. The author argues that the nonexperimental studies may be biased toward finding a positive relationship between unemployment spell duration and PEA use because they ignore the possibility that PEAs are chosen by many job- seekers only after other search methods have been tried unsuccessfully and a period of unemployment has elapsed. An analysis of U.K. survey data with information on the timing of PEA use in 1987-88 supports that hypothesis. [ABSTRACT FROM AUTHOR]

We study a new class of unconditional and conditional mean-variance spanning tests that exploits the duality between Hansen-Jagannathan bounds (1991) and mean-standard deviation frontiers. The tests are shown to be equivalent to standard spanning tests in population, but we document substantial differences in the small sample performance of alternative tests. Our empirical application examines the diversification benefits from emerging equity markets using an extensive new data set on U.S. and U.K.-traded closed-end funds. We find significant diversification benefits for the U.K. country funds, but not for the U.S. funds. The difference appears to relate to differences in portfolio holdings rather than to the behavior of premiums in the United States versus the United Kingdom. [ABSTRACT FROM AUTHOR]

This paper provides a detailed examination of the cost imposed by thrift institutions resolved during the period 1980-1988. A simple model is presented to explain the cost of resolution. This model is tested empirically with a comprehensive data set that permits us to avoid some of the econometric problems present in earlier studies. The empirical evidence suggests that the model that explains resolution costs in the late 1980s is significantly different from the model for either the middle or early 1980s. This evidence is consistent with the changing nature of the thrift crisis and changes in the regulator's closure rule. Our econometric evidence, moreover, is consistent with the hypothesis that, for troubled institutions, tangible net worth systematically understates market-value net worth. In addition, the importance of including time effects as well as institution effects as determinants of the cost of resolution is revealed. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine the stability of the demand-for-money function using the United States annual data for the period 1900-1974. During this period the United States economy has undergone a wide variety of institutional and monetary change. The question whether a stable demand-for-money function existed during such a long period is of great importance,l We have examined this issue using the varying parameter technique recently developed by Cooley and Prescott. This technique gives estimates of the parameter vector under the maintained assumption that the parameters may be subject to "sequential changes" over time. And, therefore, unlike the usual procedures for testing for structural shift does not require any prior knowledge of the point in time when the shifts in the parameters of the equation might have occurred. [ABSTRACT FROM AUTHOR]

Previously published research has shed light on the link between money supply and share prices. The current article examines the value of that research in predicting the movement of stock markets. Of particular interest are models derived by Keran, Hamburger-Kochin, and Homa-Jaffee. The author scrutinizes each, and concludes there are good reasons to be skeptical about their presumed stock prognosticating prowess. Overall, the author doubts that fluctuations in the money supply have predictive value concerning stocks.

The article focuses on a sectoral econometric study on interest rates, the business demand for funds and the residential mortgage market. The aim of the article is to construct a model which will help reveal the extent to which the demand for funds from the business sector affects the residential mortgage market through the structure of interest rates and the extent to which it affects this market through influential channels. The second goal of the article is to measure the degree to which the government under written and conventional sectors respond in a dissimilar manner to the above economic forces.

This paper examines the firm's opting out decision and the impact of the 1990 Pennsylvania Antitakeover Law on the stock prices of 123 firms. The results indicate that on average Pennsylvania stock returns decreased by 9 percent from introduction to passage. A comparison indicates that firms that opted out had CARs 18 percentage points higher than firms that chose not to opt out. The event study methodology may not be appropriate because investors may anticipate the passage of legislation and because there may be multiple events. Intervention analysis, an econometric technique not previously used in this area, is applied and the results support the agency cost hypothesis. A logit model is implemented to find the sources of the losses and gains and to study why firms choose to opt out. In this model, firms are controlled for antitakeover amendments, takeover activity, insider holdings, large noninsider holdings, size, and industry. Firms with a proxy for lower agency costs were found to be more likely to opt out of the legislation. [ABSTRACT FROM AUTHOR]

The author comments on economist Carl Christ's paper "Econometric Models of the Financial Sector." He states that Christ's paper examined structural equations and theoretical elements of financial models. He presents a microeconomic non-monetary theory of price level and mentions that many econometric models determine price levels through a function that relates prices to variables including wages, variable markups, import prices, and more. He suggests that the monetary sector is used by many econometric models to explain the term structure of interest rates, the flow of funds in specific credit markets, the availability of mortgage funds, and the composition of financial assets.

The article focuses on econometric models of the U.S. financial sector. It examines the ability of econometric models to track quarterly behavior of the U.S. economy since the end of World War II. It states that three sectors at minimum appear necessary for sector detail: the Federal government, banks, and the nonbank private sector. It presents policy simulations and dynamic simulation results for five selected variables. It states that the financial model specifications fail to take full advantage of available economic theory but manage a reasonable job of tracking the economy.