Thematische Bibliographien / Vector autoregressive model / Bücher
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Bücher zum Thema „Vector autoregressive model“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 11. Juni 2025

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1

Brüggemann, Ralf. Model Reduction Methods for Vector Autoregressive Processes. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17029-4.

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2

Mocan, H. Naci. Business cycles and fertility dynamics in the U.S.: A vector-autoregressive model. National Bureau of Economic Research, 1989.

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3

Johansen, Søren. The asymptotic variance of the estimated roots in a cointegrated vector autoregressive model. European University Institute, Department of Economics, 2001.

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4

Hasan, Mohammad S. Monetary policy, fiscal policy, and aggregate economic activity in a vector autoregressive model. University of Northumbria at Newcastle, 1995.

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5

Johansen, Søren. Controlling inflation in a cointegrated vector autoregressive model with an application to US data. European University Institute, Department of Economics, 2001.

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6

Johansen, Søren. A small sample correction of the test for cointegrating rank in the vector autoregressive model. European University Institute, 2000.

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7

Johansen, Søren. A small sample correction of the test for cointegrating rank in the vector autoregressive model. European University Institute, 2000.

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8

Empirical vector autoregressive modeling. Springer-Verlag, 1994.

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9

Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, 1995.

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10

Johansen, Søren. Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, 1995.

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11

Brännström, Tomas. Bias approximation and reduction in vector autoregressive models. typescript, 1995.

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12

Crone, Theodore M. Vector-autoregression forecast models for the third district states. Federal Reserve Bank of Philadelphia, Economic Research Division, 1992.

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13

Ramaswamy, Ramana. Japan's stagnant nineties: A vector autoregression retrospective. International Monetary Fund, Asia and Pacific Department, 1999.

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14

The cointegrated VAR model: Methodology and applications. Oxford University Press, 2006.

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15

Bernanke, Ben S. Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. National Bureau of Economic Research, 2004.

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16

Elitzak, Howard. Quarterly forecasting of meat retail prices: A vector autoregression approach. U.S. Dept of Agriculture, Economic Research Service, Commodity Economics Division, 1989.

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17

Johansen, Søren. A small sample correction for tests of hypotheses on the cointegrating vectors. European University Institute, 1999.

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18

Love, Inessa. Financial development and dynamic investment behavior: Evidence from panel vector autoregression. World Bank, Finance, Development Research Group, 2002.

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19

Bayoumi, Tamim A. Foreign entanglements: Estimating the source and size of spillovers across industrial countries. International Monetary Fund, Western Hemisphere Dept., 2007.

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20

Kobler, Alexander E. Sources and dynamics of macroeconomic fluctuations in Switzerland: Evidence from a structural vector autoregressive approach. Peter Lang, 2000.

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21

Charemza, Wojciech. New directions in econometric practice: General to specific modelling, cointegration, and vector autoregression. E. Elgar, 1992.

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22

Derek, Deadman, ed. New directions in econometric practice: General to specific modelling, cointegration, and vector autoregression. 2nd ed. Edward Elgar Pub., 1997.

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23

Ang, Andrew. A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. National Bureau of Economic Research, 2001.

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24

Reimers, Hans-Eggert. Analyse kointegrierter Variablen mittels vektorautoregressiver Modelle. Physica-Verlag, 1991.

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25

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation.
 
 It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
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26

Fernández-Villaverde, Jesús. A, B, C's (and D)'s for understanding VARS. National Bureau of Economic Research, 2005.

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27

Fernández-Villaverde, Jesús. A, B, C's, (and D's) for understanding VARS. Federal Reserve Bank of Atlanta, 2005.

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28

Model Reduction Methods for Vector Autoregressive Processes. Springer, 2004.

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29

Andree, Bo Pieter Johannes, Phoebe Spencer, Andres Chamorro, Dieter Wang, Sardar Feredun Azari, and Harun Dogo. Pollution and Expenditures in a Penalized Vector Spatial Autoregressive Time Series Model with Data-Driven Networks. World Bank, Washington, DC, 2019. http://dx.doi.org/10.1596/1813-9450-8757.

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30

Gereziher, Hayelom Yrgaw, and Naser Yenus Nuru. Structural estimates of the South African sacrifice ratio. 12th ed. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/2021/946-4.

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This paper estimates the output cost of fighting inflation—the sacrifice ratio—for the South African economy using quarterly data spanning the period 1998Q1–2019Q3. To compute the sacrifice ratio, the structural vector autoregressive model developed by Cecchetti and Rich (2001) based on Cecchetti (1994) is employed. Our findings show us a small sacrifice ratio, which lies within the range 0.00002–0.231 per cent with an average of 0.031 per cent, indicating a low level of output to be sacrificed while fighting inflation. Hence, the reserve bank is recommended to sustain an inflation rate within the target range and reap the benefits of a predictable and stable price path, as restrictive monetary policy has only a transitory effect on real variables like output.
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31

Addison, Tony, and Atanu Ghoshray. Pandemics and their impact on oil and metal prices. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/914-3.

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We examine the effect of pandemics on selected commodity prices—in particular, those of zinc, copper, lead, and oil. We set up a vector autoregressive model and analyse data since the mid-nineteenth century to determine how prices reacted to pandemics such as the 1918 Spanish Flu, 1957 Asian Flu, and 1968 Hong Kong Flu. We control for demand and supply fundamentals to generate forecasts from the point of outbreak, and we consider whether any pattern can be deduced in reactions to adverse global shocks. Results are varied, depending on choice of commodity and magnitude and type of response. No clear conclusions are possible from past pandemics, and we conclude that at the time of writing, forecasts are difficult to make in the ongoing current pandemic too. We conclude by estimating impulse response functions to assess likely impact and the subsequent response of commodity prices to the shock.
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32

Johansen, Soren. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models (Advanced Texts in Econometrics). Oxford University Press, USA, 1996.

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33

Juselius, Katarina. The Cointegrated VAR Model: Methodology and Applications (Advanced Texts in Econometrics). Oxford University Press, USA, 2007.

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34

Juselius, Katarina. The Cointegrated VAR Model: Methodology and Applications (Advanced Texts in Econometrics). Oxford University Press, USA, 2007.

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35

Pevehouse, Jon, and Jason D. Brozek. Time‐Series Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0019.

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This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model. It then describes the stationary models in univariate and multivariate analyses. The nonstationary models of each type are addressed. In addition, various issues regarding the analysis of time series including data aggregation and temporal stability are considered. Before concluding, the article briefly reports the time-series techniques in the context of panel data. In general, time-series analysis can help improve the understanding of the political world.
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36

Charemza, Wojciech W., and Derek F. Deadman. New Directions in Econometric Practice: General to Specific Modelling, Cointegration and Vector Autoregression. Edward Elgar Pub, 1991.

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