Inverse Estimation on Trigger Factors of Simultaneous Slope Failures with Purification of Training Data Sets

This paper presents an procedure for purifying training data sets （i.e., past occurrences of slope failures） for inverse estimation on unobserved trigger factors of ＂different types of simultaneous slope failures＂. Due to difficulties in pixel-by-pixel observations of trigger factors, as one of the measures, the authors had proposed an inverse analysis algorithm on trigger factors based on SEM （structural equation modeling）. Through a measurement equation, the trigger factor is inversely estimated, and a TFI （trigger factor influence） map can be also produced. As a subsequence subject, a purification procedure of training data set should be constructed to improve the accuracy of TFI map which depends on the representativeness of given training data sets of different types of slope failures. The proposed procedure resamples the matched pixels between original groups of past slope failures （i.e., surface slope failures, deep-seated slope failures, landslides） and classified three groups by K-means clustering for all pixels corresponding to those slope failures. For all cases of three types of slope failures, the improvement of success rates with respect to resampled training data sets was confirmed. As a final outcome, the differences between TFI maps produced by using original and resampled training data sets, respectively, are delineated on a DIF map （difference map） which is useful for analyzing trigger factor influence in terms of ＂risky- and safe-side assessment＂ sub-areas with respect to ＂different types of simultaneous slope failures＂.

Capital mobility in Latin American and Caribbean countries: new evidence from dynamic common correlated effects panel data modeling

This study investigates the degree of capital mobility in a panel of 16 Latin American and 4 Caribbean countries during 1960 to 2017 against the backdrop of the Feldstein-Horioka hypothesis by applying recent panel data techniques.This is the first study on capital mobility in Latin American and Caribbean countries to employ the recently developed panel data procedure of the dynamic common correlated effects modeling technique of Chudik and Pesaran(J Econ 188:393–420,2015)and the error-correction testing of Gengenbach,Urbain,and Westerlund(Panel error correction testing with global stochastic trends,2008,J Appl Econ 31:982–1004,2016).These approaches address the serious panel data econometric issues of crosssection dependence,slope heterogeneity,nonstationarity,and endogeneity in a multifactor error-structure framework.The empirical findings of this study reveal a low average(mean)savings–retention coefficient for the panel as a whole and for most individual countries,as well as indicating a cointegration relationship between saving and investment ratios.The results indicate that there is a relatively high degree of capital mobility in the Latin American and Caribbean countries in the short run,while the long-run solvency condition is maintained,which is due to reduced frictions in goods and services markets causing increase competition.Increased capital mobility in these countries can promote economic growth and hasten the process of globalization by creating a conducive economic environment for FDI in these countries.

Returns to Lying？ Identifying the Effects of MisreporUng When the Truth Is Unobserved

Consider an observed binary regressor D and an unobserved binary vari- able D＊, both of which affect some other variable Y. This paper considers nonpara- metric identification and estimation of the effect of D on Y, conditioning on D＊ = 0. For example, suppose Y is a person＇s wage, the unobserved D＊ indicates if the person has been to college, and the observed D indicates whether the individual claims to have been to college. This paper then identifies and estimates the difference in av- erage wages between those who falsely claim college experience versus those who tell the truth about not having college. We estimate this average effect of lying to be about 6% to 20%. Nonparametric identification without observing D＊ is obtained ei- ther by observing a variable V that is roughly analogous to an instrument for ordinary measurement error, or by imposing restrictions on model error moments.