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1

Aggarwal, Shalini, Sunita Arora, and Arvinder Kaur. How to Use Vector Autoregressive (VAR) Models. SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036213596.

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2

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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3

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

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6

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

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7

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

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8

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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9

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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10

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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11

Juselius, Katarina. Cointegrated Var Model: Methodology and Applications. Advanced Texts in Econometrics. Oxford University Press, 2006.

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12

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

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13

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

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14

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 articl
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15

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

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16

Brüggemann, Ralf. Model Reduction Methods for Vector Autoregressive Processes. Springer London, Limited, 2012.

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17

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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18

Tissen, Colin, and E. Voisin. Effects of Monetary Policy in the Us. the Vector Error Correction Model (Vecm) Compared to the Structural Autoregressive Model (Svar). GRIN Verlag GmbH, 2017.

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19

Khan, Aman, and Kenneth A. Scorgie. Forecasting Government Budgets. The Rowman & Littlefield Publishing Group, 2022. https://doi.org/10.5040/9781666990355.

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Forecasting is integral to all governmental activities, especially budgetary activities. Without good and accurate forecasts, a government will not only find it difficult to carry out its everyday operations but will also find it difficult to cope with the increasingly complex environment in which it has to operate. This book presents, in a simple and easy to understand manner, some of the commonly used methods in budget forecasting, simple as well as advanced. The book is divided into three parts: It begins with an overview of forecasting background, forecasting process, and forecasting metho
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20

Pengaruh utang luar negeri, kebijakan fiskal terhadap konsumsi masyarakat dalam paradigma Ricardian equivalence, 1990-2004: Penerapan model vector autoregressions (VAR) : laporan penelitian. Fakultas Ekonomi, Universitas Sebelas Maret, 2006.

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21

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
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22

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
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