Relevant bibliographies by topics / Vector autoregression

Academic literature on the topic 'Vector autoregression'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Vector autoregression"

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Dufour, Jean-Marie. "Unbiasedness of Predictions from Etimated Vector Autoregressions." Econometric Theory 1, no. 3 (December 1985): 387–402. http://dx.doi.org/10.1017/s0266466600011270.

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Forecasts from a univariate autoregressive model estimated by OLS are unbiased, irrespective of whether the model fitted has the correct order; this property only requires symmetry of the distribution of the innovations. In this paper, this result is generalized to vector autoregressions and a wide class of multivariate stochastic processes (which include Gaussian stationary multivariate stochastic processes) is described for which unbiasedness of predictions holds: specifically, if a vector autoregression of arbitrary finite order is fitted to a sample from any process in this class, the fitted model will produce unbiased forecasts, in the sense that the prediction errors have distributions symmetric about zero. Different numbers of lags may be used for each variable in each autoregression and variables may even be missing, without unbiasedness being affected. This property is exact in finite samples. Similarly, the residuals from the same autoregressions have distributions symmetric about zero.
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Zhu, Xuening, Rui Pan, Guodong Li, Yuewen Liu, and Hansheng Wang. "Network vector autoregression." Annals of Statistics 45, no. 3 (June 2017): 1096–123. http://dx.doi.org/10.1214/16-aos1476.

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Lanne, Markku, and Pentti Saikkonen. "NONCAUSAL VECTOR AUTOREGRESSION." Econometric Theory 29, no. 3 (November 12, 2012): 447–81. http://dx.doi.org/10.1017/s0266466612000448.

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In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications that currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. Therefore, we propose a procedure for discriminating between causality and noncausality. The methods are illustrated with an application to interest rate data.
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Härdle, W., A. Tsybakov, and L. Yang. "Nonparametric vector autoregression." Journal of Statistical Planning and Inference 68, no. 2 (May 1998): 221–45. http://dx.doi.org/10.1016/s0378-3758(97)00143-2.

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Shapor, Maria Alexandrovna, and Rafael Rubenovich Gevogyan. "Features of the vector autoregression models application in macroeconomic research." Mezhdunarodnaja jekonomika (The World Economics), no. 8 (August 10, 2021): 634–49. http://dx.doi.org/10.33920/vne-04-2108-05.

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In this paper, we analyzed articles by foreign authors that use various vector autoregression models to calculate the impact of qualitative indicators on the economic processes of countries or a group of countries. In particular, the article analyzed the classical model of vector autoregression (VAR), panel model of autoregressive (PVAR), Bayesian model of autoregressive (BVAR), structural model of autoregressive (SVAR), and the global model of autoregressive (GVAR). Among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-trivial variables are rare. This is because financial macroeconomic variables in most cases have a direct impact on economic processes in the country. The analysis of financial indicators and the results obtained can play a significant role in the development of economic strategies in different states, since the results obtained with the help of vector autoregression models are usually quite accurate. The studied articles analyze the data of both developed and developing states or groups of states in different periods. The studied articles were classified according to several criteria, which were selected by the author to structure the work. Note that among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-trivial variables are rare. This is since financial macroeconomic variables in most cases have a direct impact on economic processes in the country. The analysis of financial indicators and the results obtained can play a significant role in the development of economic strategies in different states, since the results obtained with the help of vector autoregression models are usually quite accurate. In the conclusion of this study, the author presented conclusions based on the analysis of autoregressive models.
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Feifei, Wang, Zhu Xuening, and Pan Rui. "Generalized network vector autoregression." SCIENTIA SINICA Mathematica 51, no. 8 (July 9, 2020): 1253. http://dx.doi.org/10.1360/scm-2018-0839.

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Lanne, Markku, and Jani Luoto. "Noncausal Bayesian Vector Autoregression." Journal of Applied Econometrics 31, no. 7 (January 8, 2016): 1392–406. http://dx.doi.org/10.1002/jae.2497.

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Kalliovirta, Leena, Mika Meitz, and Pentti Saikkonen. "Gaussian mixture vector autoregression." Journal of Econometrics 192, no. 2 (June 2016): 485–98. http://dx.doi.org/10.1016/j.jeconom.2016.02.012.

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Tovstik, T. M. "Vector autoregression process. Stationarity and simulation." Journal of Physics: Conference Series 2099, no. 1 (November 1, 2021): 012068. http://dx.doi.org/10.1088/1742-6596/2099/1/012068.

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Abstract For vector discrete-parameter random autoregressive processes and for a mixed autoregression/moving-average model, we obtain conditions which should be satisfied by the correlation functions or the model coefficients in order that the process be weakly stationary. Fairly simple tests are used. Algorithms for modeling such vector stationary processes are given. Examples are presented clarifying testing criteria for stationarity of models defned in terms of the coefficients or the correlation functions of the process.
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Elbourne, Adam, and Jakob de Haan. "Modeling Monetary Policy Transmission in Acceding Countries: Vector Autoregression Versus Structural Vector Autoregression." Emerging Markets Finance and Trade 45, no. 2 (March 2009): 4–20. http://dx.doi.org/10.2753/ree1540-496x450201.

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Dissertations / Theses on the topic "Vector autoregression"

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Brännström, Tomas. "Bias approximation and reduction in vector autoregressive models /." Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1995. http://www.hhs.se/efi/summary/405.

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Dutta, Bordoloi Suwodi. "Interdependence of US Industry Sectors Using Vector Autoregression." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/1073.

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"In this study, we explore the interdependence among different US industries by examining their correlations of the stock portfolios. Furthermore, we focus on the dynamics of their interdependent relations during peaceful and volatile periods; as such relations may change due to different sensitivities of each industry to the macroeconomic conditions. More specifically, we apply Vector Autoregression (VAR) methodology on the US industry portfolios and we use variance decomposition and generalized impulse response functions to identify the strength of the impact of each industry on the others. Based on different portfolio returns of the US industries during 1962 to 2008, we find if the pattern of the dynamic relations of the industries change in different periods. We also deduce the most influential and sensitive sectors in the US domestic market. In addition, we find the direction, strength and durability of the shocks using generalized impulse response function (GIRF)."
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Jeon, Kyung-Seong. "An examination of stock market properties : vector autoregression approach /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841304.

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Unosson, Måns. "A Mixed Frequency Steady-State Bayesian Vector Autoregression: Forecasting the Macroeconomy." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297406.

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This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The proposed model is compared to a steady-state Bayesian VAR model estimated on data aggregated to quarterly frequency and a quarterly least squares VAR with standard parametrization. Forecasts are evaluated using root mean squared errors and the log-determinant of the forecast error covariance matrix. The results indicate that the inclusion of monthly data improves the accuracy of quarterly forecasts of monthly variables for horizons up to a year. For quarterly variables the one and two quarter forecasts are improved when using monthly data.
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Zhang, Wei. "A sensitivity study on identification schemes of the structural vector autoregression /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025669.

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Petrov, Krassimir M. "Forecasting the dairy price complex : an application of Bayesian Vector autoregression modelling /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193272066522.

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Brännström, Tomas. "Bias approximation and reduction in vector autoregressive models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 1995. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-878.

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In the last few decades, vector autoregressive (VAR) models have gained tremendous popularity as an all-purpose tool in econometrics and other disciplines. Some of their most prominent uses are for forecasting, causality tests, tests of economic theories, hypothesis-seeking, data characterisation, innovation accounting, policy analysis, and cointegration analysis. Their popularity appears to be attributable to their flexibility relative to other models rather than to their virtues per se. In addition, analysts often use VAR models as benchmark models. VAR modeling has not gone uncriticised, though. A list of relevant arguments against VAR modelling can be found in Section 2.3 of this thesis. There is one additional problem which is rarely mentioned though, namely the often heavily biased estimates in VAR models. Although methods to reduce this bias have been available for quite some time, it has probably not been done before, at least not in any systematic way. The present thesis attempts to systematically examine the performance of bias-reduced VAR estimates, using two existing and one newly derived approximation to the bias. The thesis is orginanised as follows. After a short introductory chapter, a brief history of VAR modelling can be found in Chapter 2 together with a review of different representations and a compilation of criticisms against VAR models. Chapter 3 reports the results of very extensive Monte Carlo experiments serving dual purposes: Firstly, the simulations will reveal whether or not bias really poses a serious problem, because if it turns out that biases appear only by exception or are mainly insignificant, there would be little need to reduce the bias. Secondly, the same data as in Chapter 3 will be used in Chapter 4 to evaluate the bias approximations, allowing for direct comparison between bias-reduced and original estimates. Though Monte Carlo methods have been (rightfully) criticised for being too specific to allow for any generalisation, there seems to be no good alternative to analyse small-sample properties of complicated estimators such as these. Chapter 4 is in a sense the core of the thesis, containing evaluations of three bias approximations. The performance of the bias approximations is evaluated chiefly using single regression equations and 3D surfaces. The only truly new research result in this thesis can also be found in Chapter 4; a second-order approximation to the bias of the parameter matrix in a VAR(p) model. Its performance is compared with the performance of two existing first-order approximations, and all three are used to construct bias-reduced estimators, which are then evaluated. Chapter 5 holds an application of US money supply and inflation in order to find out whether the results in Chapter 4 can have any real impacts. Unfortunately though, bias reduction appears not to make any difference in this particular case. Chapter 6 concludes.
Diss. Stockholm : Handelshögsk.
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Jordanov, Jordan V. "The size anomaly in the London Stock Exchange : an empirical investigation." Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/7067.

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This study tests the size effect in the London Stock Exchange, using data for all nonfinancial listed firms from January 1985 to December 1995. The initial tests indicate that average stock returns are negatively related to firm size and that small firm portfolios earn returns in excess of the market risk. Further, the study tests whether the size effect is a proxy for variables such as the Book-to- Market Value and the Borrowing Ratio, as well as the impact of the dividend and the Bid- Ask spread on the return of the extreme size portfolios. The originality of this study is in the application of the Markov Chain Model to testing the Random Walk and Bubbles hypotheses, and the Vector Autoregression (VAR) framework for testing the relationship of macroeconomic variables with size portfolio returns.
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White, Alexander B. "Pre- and post-retirement asset allocation: a simulation of retirement investment strategies for agricultural producers." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/38097.

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This research simulates pre-retirement investment scenarios for agricultural producers. Thirty-two investment scenarios are examined, with each scenario differing with respect to retirement vehicle, investment strategy of the producer, and the use of a cash margin for reinvestment in the operation versus prepaying term debt (cash preference). The retirement vehicles included in this study are Individual Retirement Accounts (IRAs), Simplified Employee Pension Plans (SEPs), and 401(k) plans. Investment strategies reflect the producer's preference for investing in conservative, balanced, or aggressive assets, or a combination of these assets. Further, these scenarios are examined for three methods of capitalization: Case I- an operation with a 50 percent debt/asset ratio; Case II - an operation with a 65 percent debt/asset ratio; Case III - an operation with a 65 percent debt/asset ratio with a majority of the farm land being leased. The analytical model simulates the annual cash flows of a commercial agricultural operation for each investment scenario over a 30-year period. Stochastic rates of return, generated using a vector-autoregressive (VAR) model, are incorporated into the simulation model. Each scenario is replicated 100 times using different vectors of stochastic rates of return. Results show investment in retirement vehicles does not significant reduce ending farm assets, regardless of investment strategy or cash preference of the producer. Use of retirement vehicles does have a significant positive impact on ending net worth for the producer. IRAs are not significant investment tools for producers (or spouses) who are participants in another qualified retirement plan. Investment strategy has a major impact on ending net worth. Aggressive and dynamic (aggressive to conservative as retirement approaches) investment strategies dominate conservative and balanced strategies. Use of cash margin to prepay debt has no advantage over reinvesting in the farm. Retirement vehicles greatly improve the probability of meeting estimated family living needs during retirement, and generate greater diversity and liquidity of the retirement portfolio. Further, retirement vehicles are more important for producer with highly-leveraged operations and for producers who lease a majority of their assets.
Ph. D.
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Brüggemann, Ralf. "Model reduction methods for vector autoregressive processes /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0818/2003067373-d.html.

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Books on the topic "Vector autoregression"

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Holden, K. Vector autoregression modelling and forecasting. [Liverpool]: Liverpool Business School, 1994.

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Empirical vector autoregressive modeling. Berlin: Springer-Verlag, 1994.

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Kadiyala, K. R. Forecasting with Bayesian vector autoregressions. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1989.

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

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

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

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

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Ghatak, Anita. Vector autoregression modelling and forecasting growth of South Korea. Milton Keynes: De Montfort University, School of Social Sciences, 1997.

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Wright, Jonathan H. Exact confidence intervals for impulse responses in a gaussian vector autoregression. Washington, D.C: Federal Reserve Board, 2000.

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

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Book chapters on the topic "Vector autoregression"

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Gates, Kathleen M., Sy-Miin Chow, and Peter C. M. Molenaar. "Vector Autoregression (VAR)." In Intensive Longitudinal Analysis of Human Processes, 73–101. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780429172649-4.

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Aljandali, Abdulkader, and Motasam Tatahi. "Vector Autoregression (VAR) Model." In Economic and Financial Modelling with EViews, 211–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92985-9_10.

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McElroy, Tucker, and David Findley. "Fitting Constrained Vector Autoregression Models." In Empirical Economic and Financial Research, 451–70. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03122-4_28.

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Ooms, Marius. "Data Analysis by Vector Autoregression." In Lecture Notes in Economics and Mathematical Systems, 59–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-48792-7_3.

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Das, Panchanan. "Cointegration, Error Correction and Vector Autoregression." In Econometrics in Theory and Practice, 367–416. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9019-8_12.

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McMillan, Susan M. "The Question of Causality: Vector Autoregression Analysis." In Foreign Direct Investment in Three Regions of the South at the End of the Twentieth Century, 116–41. London: Palgrave Macmillan UK, 1999. http://dx.doi.org/10.1007/978-1-349-27218-1_5.

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Mokhtarzadeh, Fatemeh. "A global vector autoregression model for softwood lumber trade." In International trade in forest products: lumber trade disputes, models and examples, 174–93. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789248234.0174.

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Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.
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Mokhtarzadeh, Fatemeh. "A global vector autoregression model for softwood lumber trade." In International trade in forest products: lumber trade disputes, models and examples, 174–93. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789248234.0008.

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Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.
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Hochreiter, Ronald, and Gerald Krottendorfer. "Robust Estimation of Vector Autoregression (VAR) Models Using Genetic Algorithms." In Applications of Evolutionary Computation, 223–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37192-9_23.

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Kharin, Yuriy. "Optimality and Robustness of Vector Autoregression Forecasting Under Missing Values." In Robustness in Statistical Forecasting, 231–72. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00840-0_8.

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Conference papers on the topic "Vector autoregression"

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Haboub, Amine, Hamza Baali, and Abdesselam Bouzerdoum. "Multichannel Signal Classification Using Vector Autoregression." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054144.

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C.J., Harivigneshwar, Dharmavenkatesan K.B., Ajith R., and Jeyanthi R. "Modeling of Multivariate Systems using Vector Autoregression(VAR)." In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2019. http://dx.doi.org/10.1109/i-pact44901.2019.8960145.

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Lu, Xin, and Kiyoshi Nishiyama. "Dependability of Unstructured Estimator in Vector Autoregression Identification." In 2007 IEEE Workshop on Signal Processing Systems. IEEE, 2007. http://dx.doi.org/10.1109/sips.2007.4387615.

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Cavalcante, Laura, and Ricardo J. Bessa. "Solar power forecasting with sparse vector autoregression structures." In 2017 IEEE Manchester PowerTech. IEEE, 2017. http://dx.doi.org/10.1109/ptc.2017.7981201.

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Castano, Alfonso L., Javier Cuenca, Jose Matias Cutillas Lozano, Domingo Gimenez, Jose J. Lopez-Espin, and Alberto Perez-Bernabeu. "Parallelism on Hybrid Metaheuristics for Vector Autoregression Models." In 2018 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2018. http://dx.doi.org/10.1109/hpcs.2018.00134.

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Rimondi, Andrea, Anton Sysoev, Maria Cristina Recchioni, and Pavel Saraev. "Modelling Wealth Inequality: A Structural Vector Autoregression Approach." In 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2020. http://dx.doi.org/10.1109/summa50634.2020.9280600.

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Petrusevich, Denis. "INVESTIGATION OF ORDER SELECTION IN THE VECTOR AUTOREGRESSION MODEL." In 20th International Multidisciplinary Scientific GeoConference Proceedings SGEM 2020. STEF92 Technology, 2020. http://dx.doi.org/10.5593/sgem2020/2.1/s07.025.

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Liashenko, Olena, Tetyana Kravets, and Olha Bobro. "Fractionally Cointegrated Vector Autoregression Model of Spread Estimation for Metals." In 2020 10th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2020. http://dx.doi.org/10.1109/acit49673.2020.9208895.

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Ling, Xing, Yeonwoo Rho, and Chee-Wooi Ten. "Predicting Global Trend of Cybersecurity on Continental Honeynets Using Vector Autoregression." In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, 2019. http://dx.doi.org/10.1109/isgteurope.2019.8905639.

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Raudiya, Fikka, Aniq A. Rohmawati, and Didit Adytia. "Non-Stationary Order of Vector Autoregression in Significant Ocean Wave Forecasting." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527502.

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Reports on the topic "Vector autoregression"

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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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Ang, Andrew, and Monika Piazzesi. A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables. Cambridge, MA: National Bureau of Economic Research, July 2001. http://dx.doi.org/10.3386/w8363.

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Read, Matthew. Estimating the Effects of Monetary Policy in Australia Using Sign-restricted Structural Vector Autoregressions. Reserve Bank of Australia, January 2023. http://dx.doi.org/10.47688/rdp2022-09.

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Existing estimates of the macroeconomic effects of Australian monetary policy tend to be based on strong, potentially contentious, assumptions. I estimate these effects under weaker assumptions. Specifically, I estimate a structural vector autoregression identified using a variety of sign restrictions, including restrictions on impulse responses to a monetary policy shock, the monetary policy reaction function, and the relationship between the monetary policy shock and a proxy for this shock. I use an approach to Bayesian inference that accounts for the problem of posterior sensitivity to the choice of prior that arises in this setting, which turns out to be important. Some sets of identifying restrictions are not particularly informative about the effects of monetary policy. However, combining the restrictions allows us to draw some useful inferences. There is robust evidence that an increase in the cash rate lowers output and consumer prices at horizons beyond a year or so. The results are consistent with the macroeconomic effects of a 100 basis point increase in the cash rate lying towards the upper end of the range of existing estimates.
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Ambaw, Dessie, Madhavi Pundit, Arief Ramayandi, and Nicholas Sim. Real Exchange Rate Misalignment and Business Cycle Fluctuations in Asia and the Pacific. Asian Development Bank, March 2022. http://dx.doi.org/10.22617/wps220066-2.

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This paper investigates the impact of real exchange rate (RER) misalignment on business cycles among 22 economies in Asia and the Pacific from 1990 to 2018. It employs a panel vector autoregression involving consumer price index (CPI) inflation, output gap, short-term interest rate, and RER misalignment. The authors find that RER overvaluation may lead to a reduction in CPI inflation and short-term interest rate. The study also illustrates Asia and the Pacific’s heterogeneity as evidenced by the output gaps of some economies, particularly in Southeast Asia, which are shown to be more susceptible to RER misalignment shocks.
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Dime, Roselle, Juzhong Zhuang, and Edimon Ginting. Estimating Fiscal Multipliers in Selected Asian Economies. Asian Development Bank, August 2021. http://dx.doi.org/10.22617/wps210309-2.

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The surge of the coronavirus disease (COVID-19) pandemic has driven countries worldwide to launch substantial stimulus packages to support economic recovery. This paper estimates effects of fiscal measures on output using data from 2000 to 2019 for a panel of nine developing Asian economies and a vector autoregression model. Results show that (i) the 4-quarter and 8-quarter cumulative fiscal multipliers for general government spending range between 0.73 and 0.88 in baselines, in line with recent estimates for developed countries but larger than those for developing countries; (ii) government spending is more effective than tax cuts in boosting the economy; and (iii) an accommodative monetary policy regime can make fiscal measures more effective.
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Ludvigson, Sydney, Sai Ma, and Serena Ng. Shock Restricted Structural Vector-Autoregressions. Cambridge, MA: National Bureau of Economic Research, March 2017. http://dx.doi.org/10.3386/w23225.

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Giannone, Domenico, Michele Lenza, and Giorgio Primiceri. Prior Selection for Vector Autoregressions. Cambridge, MA: National Bureau of Economic Research, October 2012. http://dx.doi.org/10.3386/w18467.

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Ahumada, Hildegart, Eduardo A. Cavallo, Santos Espina-Mairal, and Fernando Navajas. Sectoral Productivity Growth, COVID-19 Shocks, and Infrastructure. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003411.

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This paper examines sectoral productivity shocks of the COVID-19 pandemic, their aggregate impact, and the possible compensatory effects of improving productivity in infrastructure-related sectors. We employ the KLEMS annual dataset for a group of OECD and Latin America and the Caribbean countries, complemented with high-frequency data for 2020. First, we estimate a panel vector autoregression of growth rates in sector level labor productivity to specify the nature and size of sectoral shocks using the historical data. We then run impulse-response simulations of one standard deviation shocks in the sectors that were most affected by COVID 19. We estimate that the pandemic cut economy-wide labor productivity by 4.9 percent in Latin America, and by 3.5 percent for the entire sample. Finally, by modeling the long-run relationship between productivity shocks in the sectors most affected by COVID 19, we find that large productivity improvements in infrastructure--equivalent to at least three times the historical rates of productivity gains--may be needed to fully compensate for the negative productivity losses traceable to COVID 19.
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Beirne, John, and Eric Sugandi. Risk-Off Shocks and Spillovers in Safe Havens. Asian Development Bank Institute, November 2022. http://dx.doi.org/10.56506/guux7790.

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We examine real and financial spillovers to safe haven financial flow destinations due to risk-off shocks in global financial markets. Using country-specific structural vector autoregression models over the period 1990 to 2021, we show that dynamics for Japan appear to be different to those of Switzerland and the United States in four main ways. First, in response to risk-off episodes over the estimation period, the yen real effective exchange rate appreciates sharply and significantly, with the effect persisting over time. Second, no significant effects on portfolio flows to Japan are found, in spite of the exchange rate effects, suggesting a rapid adjustment of financial markets to shifts in equilibrium exchange rates. Third, negative real spillovers from risk-off shocks appear to only apply to Japan with exchange rate appreciation exacerbating declines in GDP growth. Fourth, risk-off shocks do not have a statistically significant effect on domestic economic policy uncertainty in Japan, which may be related to the strong expectations priced in of overseas portfolio holdings repatriated back to Japan. Our findings have important implications for policy makers in safe haven destinations in managing domestic financial vulnerabilities associated with risk-off episodes.
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Clark, Todd E., and Michael W. McCracken. Evaluating the Accuracy of Forecasts from Vector Autoregressions. Federal Reserve Bank of St. Louis, 2013. http://dx.doi.org/10.20955/wp.2013.010.

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