Bootstrap Econometrics

This paper investigates the determinants of bilateral immigrant flows to 19 OECD countries between 1998 and 2007 from both advanced and developing origin countries. We pay particular attention to dynamics by including both the lagged migrant flow and the migrant stock to capture partial adjustment and network effects. To correct for the dynamic panel data bias of the fixed effects estimator we use a bootstrap algorithm. Our results indicate that immigrants are primarily attracted by better income opportunities and higher growth rates abroad. Also short-run increases in the host country’s employment rate positively affect migration from both advanced and developing countries. High public services, on the other hand, discourage migration from advanced countries but exert a pull on migration from developing sources, in line with the welfare state hypothesis. Finally, we find evidence for both partial adjustment and the presence of strong network effects. This confirms that both should ...

In many real-world binary classification tasks (e.g. detection of certain objects from images), an available dataset is imbalanced, i.e., it has much less representatives of a one class (a minor class), than of another. Generally, accurate prediction of the minor class is crucial but it's hard to achieve since there is not much information about the minor class. One approach to deal with this problem is to preliminarily resample the dataset, i.e., add new elements to the dataset or remove existing ones. Resampling can be done in various ways which raises the problem of choosing the most appropriate one. In this paper we experimentally investigate impact of resampling on classification accuracy, compare resampling methods and highlight key points and difficulties of resampling.

This study demonstrates the relationship between exchange rate determination and an endogenous monetary policy represented by Taylor rules. We fill a gap in the literature by focusing on a group of fifteen emerging economies that adopted free-floating exchange rates and inflation targeting beginning in the mid-1990s. Because of the limited span of the time series, which is a common obstacle to studying emerging economies, we employ panel data regressions to produce more efficient estimates. Following the recent literature, we use a robust set of out-of-sample statistics, incorporating bootstrapped and asymptotic distributions for the Diebold-Mariano statistic, the Clark and West statistic and Theil's U ratio. By evaluating different specifications for the Taylor rule exchange rate model based on their out-of-sample performances, we find that a present-value forward-looking specification shows strong evidence of exchange rate predictability. 3 Kingdom, Japan and Germany, these authors concluded that, with a one-to twelve-month forecasting horizon, the random walk model performs at least as well as the exchange rate models of that time (i.e., the flexible price and sticky price monetary models and a hybrid model by ).

The bootstrap is typically much less reliable in the context of time-series models with serial correlation of unknown form than it is when regularity conditions for the conventional IID bootstrap, based on resampling, apply. It is therefore useful for practitioners to have available diagnostic techniques capable of evaluating bootstrap performance in specific cases. The techniques suggested in this paper are closely related to the fast double bootstrap, and, although they inevitably rely on simulation, they are not computationally intensive. They can also be used to gauge the performance of the fast double bootstrap itself. Examples of bootstrapping time series are presented which illustrate the diagnostic procedures, and show how the results can cast light on bootstrap performance, not only in the time-series context.

This study demonstrates the relationship between exchange rate determination and an endogenous monetary policy represented by Taylor rules. We fill a gap in the literature by focusing on a group of fifteen emerging economies that adopted free-floating exchange rates and inflation targeting beginning in the mid-1990s. Because of the limited span of the time series, which is a common obstacle to studying emerging economies, we employ panel data regressions to produce more efficient estimates. Following the recent literature, we use a robust set of out-of-sample statistics, incorporating bootstrapped and asymptotic distributions for the Diebold-Mariano statistic, the Clark and West statistic and Theil's U ratio. By evaluating different specifications for the Taylor rule exchange rate model based on their out-of-sample performances, we find that a present-value forward-looking specification shows strong evidence of exchange rate predictability.

In many real-world binary classification tasks (e.g. detection of certain objects from images), an available dataset is imbalanced, i.e., it has much less representatives of a one class (a minor class), than of another. Generally, accurate prediction of the minor class is crucial but it's hard to achieve since there is not much information about the minor class. One approach to deal with this problem is to preliminarily resample the dataset, i.e., add new elements to the dataset or remove existing ones. Resampling can be done in various ways which raises the problem of choosing the most appropriate one. In this paper we experimentally investigate impact of resampling on classification accuracy, compare resampling methods and highlight key points and difficulties of resampling.

Three macroeconomic issues are examined in separate self-contained studies. The novelty is in the combination of time domain time series methods with bootstrap techniques. The combined technique is here used to analyze issues where accuracy in inference, rather than the lack of theory, has increasingly become the central concern. The first study tests the business cycle theory with application of an enhanced ADF test on the U.S. time series of real GNP (Bootstrapping for Unit Roots). The second study tests for IS-LM conditions in the U.S. during the post-Bretton Woods era by combining the Johansen's approach to cointegration with bootstrap algorithms (A Bootstrap Evaluation of Cointegration). The third study seeks to explain the observed persistence in the Bolivian dollarization process where model parameters of the Johansen’s approach to cointegration are also evaluated via a nonparametric bootstrap.

A blood bank is a place where we grater blood as a result of blood donation, stored and preserved for later use in blood transfusion no doubt. There are many online web-based blood bank management systems to satisfy the requirement of donor and receivers. There we are going to modify the existing System as this paper includes the key features, merits and demerits in existing web-based blood bank. This study shows the comparison between various existing systems and provides more idea to improve the existing system In this paper, we present s "Life Treasure Blood Portal" that is a based-on java. To develop this system, we are using different kind of technology, like html, Ajax, JSP, CSS, bootstrap, Servlet , Java-Script, JDBC. This system will monitor the record of blood donation in real time and given the analytical report like which city has more number of blood donor, which any has more number of blood receiver which blood group is more donated etc. So, there I am going to introduce this system and in at the end I will conclude.

We evaluate the predictive performance of a variety of value-at-risk (VaR) models for a portfolio consisting of five assets. Traditional VaR models such as historical simulation with bootstrap and filtered historical simulation methods are considered. We suggest a new method for estimating Value at Risk: the filtered historical simulation GJR-GARCH method based on bootstrapping the standardized GJR-GARCH residuals. The predictive performance is evaluated in terms of three criteria, the test of unconditional coverage, independence and conditional coverage and the quadratic loss function suggested. The results show that classical methods are inefficient under moderate departures from normality and that the new method produces the most accurate forecasts of extreme losses.

Publikationsansicht. 5787419. Time series analysis of macroeconomic conditions in open economies / (1995). Barja, Gover. Abstract. Thesis (Ph. D.)--Utah State University. Dept. of Economics, 1995.. Includes bibliographical references. Details der Publikation. ...

Three macroeconomic issues are examined in separate self-contained studies. The first study tests the business cycle theory with application of an enhanced Augmented Dickey-Fuller test on the U.S. time series of real gross national product. Unlike previous
studies, the null hypothesis of a unit root is rejected. The second study tests for IS-LM conditions in the U.S. during the post-Bretton Woods era by combining the Johansen's approach to ointegration with bootstrap algorithms. The estimated model produces a dynamic version of the IS-LM that permits short-term evaluations of fiscal and monetary policies. The third study seeks to explain the observed persistence in the Bolivan dollarization process. It is found that dollarization is now an irreversible process, with the Bolivian economy in transition toward equalization with U.S. prices and interest rates.

This study focuses on the technical and economic efficiency achieved in two local public service sectors: pharmaceutical services and integrated waste services. In the first study we also took in account the social dimension of the service as the health corner, the CUP service (e.g. Useful to book a medical examination), so in the analysis we calculated the relative levels of efficiency according to different point of views: "business", "social" and "mixed" models. Anyway in this study we do not include environmental variables, the most significant of which are difficult to collect. The second work focuses on the waste services; we estimated the economic and technological efficiency by comparing the city of Perugia to other relatively similar 23 Municipalities of the Center Italy, in this case, through a multi-stage approach, we included in the analyzes the environmental variables that can affect the efficiency of services. The analysis show that in the first case the efficiency scores for the business model showed greater variation if compared to the social and mixed models. The results of the second analysis show that the context variables have a significant influence on the efficiency measures. In particular highly seasonal tourism and high dispersion of the population on the territory negatively affect the efficiency. Suggestions for further research are provided in order to develop a more complete analysis of the local public services efficiency.

This paper investigates the determinants of bilateral immigrant flows to 19 OECD countries between 1998 and 2007 from both advanced and developing origin countries. We pay particular attention to dynamics by including both the lagged migrant flow and the migrant stock to capture partial adjustment and network effects. To correct for the dynamic panel data bias of the fixed effects estimator we use a bootstrap algorithm. Our results indicate that immigrants are primarily attracted by better income opportunities and higher growth rates abroad. Also short-run increases in the host country's employment rate positively affect migration from both advanced and developing countries. High public services, on the other hand, discourage migration from advanced countries but exert a pull on migration from developing sources, in line with the welfare state hypothesis. Finally, we find evidence for both partial adjustment and the presence of strong network effects. This confirms that both should be considered crucial elements of the migration model and that a correction for their joint inclusion is required.

In many real-world binary classification tasks (e.g. detection of certain objects from images), an available dataset is imbalanced, i.e., it has much less representatives of a one class (a minor class), than of another. Generally, accurate prediction of the minor class is crucial but it's hard to achieve since there is not much information about the minor class. One approach to deal with this problem is to preliminarily resample the dataset, i.e., add new elements to the dataset or remove existing ones. Resampling can be done in various ways which raises the problem of choosing the most appropriate one. In this paper we experimentally investigate impact of resampling on classification accuracy, compare resampling methods and highlight key points and difficulties of resampling.

This paper examines the size performance of the Toda-Yamamoto test for Granger causality in the case of trivariate integrated and cointegrated var systems. The standard asymptotic distribution theory and the residual-based bootstrap approach are applied. A variety of types of distribution of error term is considered. The impact of misspecification of initial parameters as well as the influence of an increase in sample size and number of bootstrap replications on size performance of Toda-Yamamoto test statistics is also examined. The results of the conducted simulation study confirm that standard asymptotic distribution theory may often cause significant over-rejection. Application of bootstrap methods usually leads to improvement of size performance of the Toda-Yamamoto test. However, in some cases the considered bootstrap method also leads to serious size distortion and performs worse than the traditional approach based on χ 2 distribution.