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Cointegration, Causality, and Forecasting

tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric;

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Unit Root, Cointegration, Granger-Causality, Threshold Regression and Other Econometric Modeling with Economics and Financial Data

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Essays in Econometrics

forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain;

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Essays in Econometrics

forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain;

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Essays in Econometrics

forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain;

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Essays in Econometrics

of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those;

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Multivariate Modelling of Non Stationary Economic Time Series

cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide;

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Economic Forecasting

in time series modelling is reviewed and includes a discussion of Box-Jenkins models, the vector autogressive approach and cointegration. Ways;

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Gdp of Bangladesh

causality among the variables agriculture, industry, and service. In addition to data description and forecasting, here the VAR model is also used;

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Twin Deficit Dynamics Case Study of Pakistan

, applications of impulsive response functions and variance decompositions along with causality tests are still the matter of investigation. This book;

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Financial, Macro and Micro Econometrics Using R

forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration,financial;

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Conceptual Econometrics Using R

, cointegration, productivity and financial market jumps and co-jumps, among others. Presents chapters authored by distinguished, honored;

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Unit Roots, Cointegration, and Structural Change

Time series analysis has undergone many changes in recent years with the advent of unit roots and cointegration. Maddala and Kim present a;

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Applied Economic Forecasting using Time Series Methods

forecast evaluation and combination. Several chapters cover univariate time series models, vector autoregressive models, cointegration and error;

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Time Series and Panel Data Econometrics

, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive;

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Index tracking strategies using Cointegration

relationship between stock prices and an index price. I compare the theoretical and empirical properties of cointegration optimal equity portfolios;

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Long-Run Growth Forecasting

model. The book uses non-stationary panel techniques to find pair-wise cointegration among GDP per capita and its main correlates.;

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Long-Run Growth Forecasting

model. The book uses non-stationary panel techniques to find pair-wise cointegration among GDP per capita and its main correlates.;

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The Current Account-Budget Deficits Link

, panel VAR Granger Causality analysis and a reduced-form consumption function, this work tries to evaluate the validity of the conventional;

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Time Series and Dynamic Models

mathematical tools. Section three is the heart of the book, and is devoted to a range of important topics including causality, exogeneity shocks;

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Time Series And Dynamic Models

mathematical tools. Section three is the heart of the book, and is devoted to a range of important topics including causality, exogeneity shocks;

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Econometrics of Qualitative Dependent Variables

mathematical tools. Section three is the heart of the book, and is devoted to a range of important topics including causality, exogeneity shocks;

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Time Series Econometrics Analysis

cointegration analysis, and a well explained econometric modeling procedure are described; with application in modeling and forecasting the inflation;

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Recent Developments in Cointegration

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Rethinking Causality in Quantum Mechanics

introductions to quantum causality, and presents a number of advances. It provides an extension and generalization of a framework that enables us to;

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Rethinking Causality in Quantum Mechanics

introductions to quantum causality, and presents a number of advances. It provides an extension and generalization of a framework that enables us to;

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Machine Learning and AI in Finance

multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment;

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