Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior

This paper presents a modeling methodology capable of accounting for spatial correlation across choice alternatives in discrete choice modeling applications. Many location choice (e.g., residential location, workplace location, destination location) modeling contexts involve choice sets where alternatives are spatially correlated with one another due to unobserved factors. In the presence of such spatial correlation, traditional discrete choice modeling methods that are often based on the assumption of independence among choice alternatives are not appropriate. In this paper, a Generalized Spatially Correlated Logit (GSCL) model that allows one to represent the degree of spatial correlation as a function of a multi-dimensional vector of attributes characterizing each pair of location choice alternatives is formulated and presented. The formulation of the GSCL model allows one to accommodate alternative correlation mechanisms rather than pre-imposing restrictive correlation assumptions on the location choice alternatives. The model is applied to the analysis of residential location choice behavior using a sample of households drawn from the 2000 San Francisco Bay Area Travel Survey (BATS) data set. Model estimation results obtained from the GSCL are compared against those obtained using the standard multinomial logit (MNL) model and the spatially correlated logit (SCL) model where only correlations across neighboring (or adjacent) alternatives are accommodated. Model findings suggest that there is significant spatial correlation across alternatives that do not share a common boundary, and that the GSCL offers the ability to more accurately capture spatial location choice behavior.     

Philosophy of Economics Rules: introduction to the symposium

ABSTRACT

Economists have long been criticized for their use of highly idealized models. In Economics rules: Why economics works, when it fails, and how to tell the difference [Oxford: Oxford University Press, 2015] Dani Rodrik responds to this criticism by offering an account of models that emphasizes the diversity of models in economics. Rodrik’s account presents a rare opportunity for economists and philosophers of economics to engage in a mutually beneficial exchange that could improve our understanding of the power and limits of economics, and the rights and wrongs of the dismal science. The symposium on Rodrik’s Economics Rules is the first attempt to seize this opportunity.     

Measuring the Foreign Exchange Premium and the Premium for Non‐Tradable Outlays for 20 Countries in Africa

In this paper, we develop an analytical general equilibrium framework to measure the foreign exchange premium and the premium for non‐tradable outlays for a country. The framework allows us to capture in a consistent manner the impacts of the sourcing of funds and their expenditure on tradable and non‐tradable goods and services of investment projects. An application of the model is carried out for 20 countries in Africa. The results show that the foreign exchange premiums range from 2.4% to 9.0% and the premium for non‐tradable outlays from ?0.7% to 2.9%. The empirical values depend on a number of factors, including the indirect taxes, production subsidies and international trade distortions of a country. These premiums should be incorporated into the economic evaluation of investment projects.     

The diversity of models as a means to better explanations in economics

ABSTRACT

In Economics Rules, Rodrik [(2015). Economics rules: Why economics works, when it fails, and how to tell the difference. Oxford: Oxford University Press] argues that what makes economics powerful despite the limitations of each and every model is its diversity of models. Rodrik suggests that the diversity of models in economics improves its explanatory capacities, but he does not fully explain how. I offer a clearer picture of how models relate to explanations of particular economic facts or events, and suggest that the diversity of models is a means to better economic explanations.     

AN APPLIED IRR MODEL FOR A LEVERED PROJECT IN THE PRESENCE OF INFLATION

An inflation-adjusted IRR model for the appraisal of levered projects is developed within the supply side studies framework by using the tax approach. An empirical test of the model on industrial projects has indicated that the general pattern of the project rankings was not significantly affected in the presence of inflation. However, the shifts in the ranks of the individual projects and over-and-under biases in the traditional accept decisions are observed. Further, all the projects have shown a magnified impact of financial leverage on the IRRs.     

Persistence and non-linearity in US unemployment: A regime-switching approach

This article examines persistence and nonlinearity in the US unemployment rate in the post-war period by using a regime-switching unit root test. The empirical results indicate that a regime-switching unit root test outperforms conventional unit root tests and describes unemployment behavior better over the business cycle in the sample. While shocks to US unemployment dissipate in expansions, shocks to the unemployment rate seem to be persistent in recessions, supporting the hysteresis hypothesis. This is consistent with the usual explanation of hysteresis that workers may lose valuable job skills in protracted recessions.     

Estimating volatility clustering and variance risk premium effects on bank default indicators

Review of Quantitative Finance and Accounting - Default risk increases substantially during financial stress times due to mainly the two reasons: volatility clustering and investors’ desire...     

Predictability of Emerging Market Local Currency Bond Risk Premia

This article investigates the source of predictability of emerging market (EM) local currency bond risk premia by using a dynamic factor approach based on a large panel of economic and financial time series. We find strong predictable variation in EM local currency excess bond returns that is associated with macroeconomic activity. We provide evidence that the main predictor variables are the factors based on real economic activity that are highly correlated with measures of industrial and manufacturing production; however, factors based on global financial factors also contain information about the future local currency bond returns. The predictive power of the extracted factors is both statistically significant and economically important. Our research has important implications for policymakers and pension fund managers.     

Nonlinearity and nonstationarity in international art market prices: evidence from Markov-switching ADF unit root tests

This study investigates the presence (or lack thereof) of nonlinear dynamics and nonstationarity in international art market prices using quarterly data for the period 1990–2011. We first test whether art market price indices follow stochastic trends or whether they are stationary by means of linear unit root tests. Next, we estimate the Markov regime-switching ADF model and test whether the linear or the nonlinear regime-switching model provides a better characterization of the global art market price series. We find that all art market price indices (except for Drawings) exhibit nonlinearity. To our knowledge, our study is the first one in the literature to suggest that a nonlinear (Markov regime-switching) model provides a better characterization of the behavior of price dynamics in international art markets. In particular, our findings indicate that the market for the overall global art market, paintings, old masters, sculptures, photographs, prints, and contemporary art might indeed be stationary while exhibiting nonlinear regime-switching properties. On the other hand, the market for drawings and the Nineteenth century art are found to be nonstationary. Overall, despite the common ground of a regime-switching framework, we still find that the sub-segments of the art market have their own inner regime switching dynamics and hence they can evolve differently overtime.     

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Only in the U.S. Stock Exchanges, the daily average trading volume is about 7 billion shares. This vast amount of trading shows the necessity of understanding the hidden insights in the data sets. In this study, a data mining technique, clustering based outlier analysis is applied to detect suspicious insider transactions. 1,244,815 transactions of 61,780 insiders are analysed, which are acquired from Thomson Financial, covering a period of January 2010–April 2017. In order to detect outliers, similar transactions are grouped into the same clusters by using a two‐step clustering based outlier detection technique, which is an integration of k‐means and hierarchical clustering. Then, it is shown that outlying transactions earn higher abnormal returns than non‐outlying transactions by using event study methodology.