Log in

Relevant bibliographies by topics / Vector autoregression / Journal articles

Journal articles on the topic 'Vector autoregression'

To see the other types of publications on this topic, follow the link: Vector autoregression.

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Vector autoregression.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Wang, Lei, and Shanshan Ding. "Vector autoregression and envelope model." Stat 7, no. 1 (2018): e203. http://dx.doi.org/10.1002/sta4.203.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Harris, Glen R. "Markov Chain Monte Carlo Estimation of Regime Switching Vector Autoregressions." ASTIN Bulletin 29, no. 1 (May 1999): 47–79. http://dx.doi.org/10.2143/ast.29.1.504606.

Full text

Abstract:

AbstractFinancial time series data are typically found to possess leptokurtic frequency distributions, time varying volatilities, outliers and correlation structures inconsistent with linear generating processes, nonlinear dependence, and dependencies between series that are not stable over time. Regime Switching Vector Autoregressions are of interest because they are capable of explaining the observed features of the data, can capture a variety of interactions between series, appear intuitively reasonable, are vector processes, and are now tractable.This paper considers a vector autoregression subject to periodic structural changes. The parameters of a vector autoregression are modelled as the outcome of an unobserved discrete Markov process with unknown transition probabilities. The unobserved regimes, one for each time point, together with the regime transition probabilities, are determined in addition to the vector autoregression parameters within each regime.A Bayesian Markov Chain Monte Carlo estimation procedure is developed which efficiently generates the posterior joint density of the parameters and the regimes. The complete likelihood surface is generated at the same time, enabling estimation of posterior model probabilities for use in non-nested model selection. The procedure can readily be extended to produce joint prediction densities for the variables, incorporating both parameter and model uncertainty.Results using simulated and real data are provided. A clear separation of the variance between a stable and an unstable regime was observed. Ignoring regime shifts is very likely to produce misleading volatility estimates and is unlikely to be robust to outliers. A comparison with commonly used models suggests that Regime Switching Vector Autoregressions provide a particularly good description of the observed data.

APA, Harvard, Vancouver, ISO, and other styles

13

Tang, Yiming, Yang Bai, and Tao Huang. "Network vector autoregression with individual effects." Metrika 84, no. 6 (January 9, 2021): 875–93. http://dx.doi.org/10.1007/s00184-020-00805-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Brandt, Patrick T., and Todd Sandler. "A Bayesian Poisson Vector Autoregression Model." Political Analysis 20, no. 3 (2012): 292–315. http://dx.doi.org/10.1093/pan/mps001.

Full text

Abstract:

Multivariate count models are rare in political science despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects of exogenous covariate shocks are illustrated for the model. Two full illustrations of the model, its interpretations, and results are presented. The first example is a dynamic model that reanalyzes the patterns and predictors of superpower rivalry events. The second example applies the model to analyze the dynamics of transnational terrorist targeting decisions between 1968 and 2008. The latter example's results have direct implications for contemporary policy about terrorists' targeting that are both novel and innovative in the study of terrorism.

APA, Harvard, Vancouver, ISO, and other styles

15

Teapon, Rizal Rahman H., and Rachman Dano Mustafa. "Shock of Monetary Policy Transmission and Macroeconomic Variable in Indonesia: A Structural VAR Approach." Jurnal Economia 14, no. 2 (October 1, 2018): 177–96. http://dx.doi.org/10.21831/economia.v14i2.21480.

Full text

Abstract:

Abstract: Shock of Monetary Policy Transmission and Macroeconomic Variable in Indonesia: A Structural VAR Approach. The purpose of this paper is to find out how much the shock of monetary policy transmission affects macroeconomic variables in Indonesia and vice versa by using Structural Vector Autoregression (SVAR) model. The results showed that the transmission of monetary policy in Indonesia only gives a weak influence toward inflation, but it greatly stimulates economic growth. However, the shock of macroeconomic variables influences the transmission of monetary policy in Indonesia significantly. Keywords: Structural Vector Autoregression (SVAR), monetary policy, macroeconomic policy.Abstrak: Kejutan Transmisi Kebijakan Moneter dan Variabel Makro Ekonomi di Indonesia: Suatu Pendekatan Structural Vector Autoregression. Tujuan dari tulisan ini adalah untuk mengetahui berapa besar guncangan transmisi kebijakan moneter mempengaruhi variabel makro ekonomi di Indonesia dan sebaliknya, dengan menggunakan model Structural Vector Autoregression (SVAR). Hasil penelitian menunjukan bahwa transmisi kebijakan moneter di Indonesia masih lemah dalam mempengaruhi inflasi tetapi sangat kuat dalam merangsang pertumbuhan ekonomi. Sebaliknya, guncangan variabel makro ekonomi sangat signifikan dalam mempengaruhi transmisi kebijakan moneter di Indonesia. Kata kunci: Structural Vector Autoregression (SVAR), kebijakan moneter, kebijakan makro ekonomi

APA, Harvard, Vancouver, ISO, and other styles

16

Galbraith, John W., Aman Ullah, and Victoria Zinde-Walsh. "ESTIMATION OF THE VECTOR MOVING AVERAGE MODEL BY VECTOR AUTOREGRESSION." Econometric Reviews 21, no. 2 (January 10, 2002): 205–19. http://dx.doi.org/10.1081/etc-120014349.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Rendu, Christel, and Ramana Ramaswamy. "Japan's Stagnant Nineties: A Vector Autoregression Retrospective." IMF Working Papers 99, no. 45 (1999): 1. http://dx.doi.org/10.5089/9781451846454.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Ericsson, Neil R., and Erica L. Reisman. "Evaluating a Global Vector Autoregression for Forecasting." International Finance Discussion Paper 2012, no. 1056 (October 2012): 1–20. http://dx.doi.org/10.17016/ifdp.2012.1056.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Daniel Felix Ahelegbey, Monica Billio, and Roberto Casarin. "Sparse Graphical Vector Autoregression: A Bayesian Approach." Annals of Economics and Statistics, no. 123/124 (2016): 333. http://dx.doi.org/10.15609/annaeconstat2009.123-124.0333.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Freeman, John R., John T. Williams, and Tse-min Lin. "Vector Autoregression and the Study of Politics." American Journal of Political Science 33, no. 4 (November 1989): 842. http://dx.doi.org/10.2307/2111112.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Huang, Xiao. "Panel vector autoregression under cross-sectional dependence." Econometrics Journal 11, no. 2 (July 2008): 219–43. http://dx.doi.org/10.1111/j.1368-423x.2008.00240.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Davidson, James. "THE COINTEGRATION PROPERTIES OF VECTOR AUTOREGRESSION MODELS." Journal of Time Series Analysis 12, no. 1 (June 28, 2008): 41–62. http://dx.doi.org/10.1111/j.1467-9892.1991.tb00067.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Abrigo, Michael R. M., and Inessa Love. "Estimation of Panel Vector Autoregression in Stata." Stata Journal: Promoting communications on statistics and Stata 16, no. 3 (September 2016): 778–804. http://dx.doi.org/10.1177/1536867x1601600314.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Spencer, David E. "Developing a Bayesian vector autoregression forecasting model." International Journal of Forecasting 9, no. 3 (November 1993): 407–21. http://dx.doi.org/10.1016/0169-2070(93)90034-k.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Njenga, Carolyn Ndigwako, and Michael Sherris. "Modeling mortality with a Bayesian vector autoregression." Insurance: Mathematics and Economics 94 (September 2020): 40–57. http://dx.doi.org/10.1016/j.insmatheco.2020.05.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Phillips, Peter C. B. "Fully Modified Least Squares and Vector Autoregression." Econometrica 63, no. 5 (September 1995): 1023. http://dx.doi.org/10.2307/2171721.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Wong, J. K., and P. L. Jolly. "A Bayesian vector autoregression model of inflation." New Zealand Economic Papers 28, no. 2 (December 1994): 117–31. http://dx.doi.org/10.1080/00779959409544220.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Chu, Chi-Hsiang, Mong-Na Lo Huang, Shih-Feng Huang, and Ray-Bing Chen. "Bayesian structure selection for vector autoregression model." Journal of Forecasting 38, no. 5 (February 21, 2019): 422–39. http://dx.doi.org/10.1002/for.2573.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Ericsson, Neil R., and Erica L. Reisman. "Evaluating a Global Vector Autoregression for Forecasting." International Advances in Economic Research 18, no. 3 (May 30, 2012): 247–58. http://dx.doi.org/10.1007/s11294-012-9357-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Burbidge, John B., L. Magee, and Michael R. Veall. "On the seasonality of vector autoregression residuals." Economics Letters 18, no. 2-3 (January 1985): 137–41. http://dx.doi.org/10.1016/0165-1765(85)90168-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Khatraty, Yahjeb Bouha, Nédra Mellouli Nauwynck, Mamadou Tourad Diallo, and Mohamedade Farouk Nanne. "Deep Predictive Models Based on IoT and Remote Sensing Big Time Series for Precision Agriculture." International Journal of Emerging Technology and Advanced Engineering 12, no. 11 (November 1, 2022): 79–88. http://dx.doi.org/10.46338/ijetae1122_09.

Full text

Abstract:

Managing time series data generated by the intelligent objects around us requires cleaning and processing techniques, as well as a prediction model with high accuracy and low complexity. This study attempts to fuse climate time series data (Temperature, Humidity, Wind speed and Rainfall) and remote sensing data to predict rice yield. We performed statistical analysis of the data, additive decomposition, and measured the correlation between the different variables. We checked the stationarity of the data by using the Augmented Dickey-Fuller ADF test to apply statistical prediction models based on AutoRegression and moving averages for univariate series such as ARIMA (Autoregressive Integrated Moving Average) and for multivariate series such as Vector AutoRegression VAR and Vector Autoregressive Moving Average VARMAX. In addition, we applied a non-parametric model such as KNearest Neighbors KNN and Recurrent Neural Network models such as Long short-term memory LSTM and Gated recurrent unit GRU for rice yield prediction. The best yield estimation is achieved using LSTM with a Root Mean Square Error RSME error of 0.100. Keywords— Agriculture, ARIMA ,KNN, LSTM, Time series.

APA, Harvard, Vancouver, ISO, and other styles

32

Balatskiy, E. V., and M. A. Yurevich. "Inflation Forecasting: The Practice of Using Synthetic Procedures." World of new economy 12, no. 4 (June 3, 2019): 20–31. http://dx.doi.org/10.26794/2220-6469-2018-12-4-20-31.

Full text

Abstract:

The article contains a review of inflation forecasting models, including the most popular class of models as one-factor models: random walk, direct autoregression, recursive autoregression, stochastic volatility with an unobserved component and of the integrated model of autoregression with moving average. Also, we discussed the possibilities of various modifications of models based on the Phillips curve (including the “triangle model”), vector autoregressive models (including the factor-extended model of B. Bernanke’s vector autoregression), dynamic general equilibrium models and neural networks. Further, we considered the comparative advantages of these classes of models. In particular, we revealed a new trend in inflation forecasting, which consists of the introduction of synthetic procedures for private forecasts accounting obtained by different models. An important conclusion of the study is the superiority of expert assessments in comparison with all available models. We have shown that in the conditions of a large number of alternative methods of inflation modelling, the choice of the adequate approach in specific conditions (for example, for the Russian economy of the current period) is a non-trivial procedure. Based on this conclusion, the authors substantiate the thesis that large prognostic possibilities are inherent in the mixed strategies of using different methodological approaches, when implementing different modelling tools at different stages of modelling, in particular, the multifactorial econometric model and the artificial neural network.

APA, Harvard, Vancouver, ISO, and other styles

33

Ding, Yishan, Dongwei He, Jun Wu, and Xiang Xu. "Crude Oil Spot Price Forecasting Using Ivanov-Based LASSO Vector Autoregression." Complexity 2022 (November 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/5011174.

Full text

Abstract:

This paper proposes a forecasting methodology that investigates a set of different sparse structures for the vector autoregression (VAR) model using the Ivanov-based least absolute shrinkage and selection operator (LASSO) framework. The variant auxiliary problem principle method is used to solve the various Ivanov-based LASSO-VAR variants, which is supported by parallel computing with simple closed-form iteration and linear convergence rate. A test case with ten crude oil spot prices is used to demonstrate the improvement in forecasting skills gained from exploring sparse structures. The proposed method outperformed the conventional vector autoregressive model.

APA, Harvard, Vancouver, ISO, and other styles

34

Zarepour, M., and S. M. Roknossadati. "MULTIVARIATE AUTOREGRESSION OF ORDER ONE WITH INFINITE VARIANCE INNOVATIONS." Econometric Theory 24, no. 3 (January 22, 2008): 677–95. http://dx.doi.org/10.1017/s0266466608080286.

Full text

Abstract:

We consider the limiting behavior of a vector autoregressive model of order one (VAR(1)) with independent and identically distributed (i.i.d.) innovations vector with dependent components in the domain of attraction of a multivariate stable law with possibly different indices of stability. It is shown that in some cases the ordinary least squares (OLS) estimates are inconsistent. This inconsistency basically originates from the fact that each coordinate of the partial sum processes of dependent i.i.d. vectors of innovations in the domain of attraction of stable laws needs a different normalizer to converge to a limiting process. It is also revealed that certain M-estimates, with some regularity conditions, as an appropriate alternative, not only resolve inconsistency of the OLS estimates but also give higher consistency rates in all cases.

APA, Harvard, Vancouver, ISO, and other styles

35

Li, Xiaozhong, and Feng Huang. "Empirical Study on the Relationship between Agricultural Economic Structure Growth and Environmental Pollution Based on Time-Varying Parameter Vector Autoregressive Model." Journal of Environmental and Public Health 2022 (August 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/5684178.

Full text

Abstract:

In order to better demonstrate the relationship between agricultural economic structure growth and environmental pollution, an autoregressive model based on time-varying parameter vector was proposed. In the process of developing the research, this paper introduces the LDMI method, based on the time-varying parameter vector autoregression model, with the help of sampling formula calculation and other methods. Efforts were made to obtain credible conclusions. The experiment result shows that in this study, a total of 10,000 samples were taken. According to this value, 10000/116.15 = 86, which means that at least 86 unrelated samples can be obtained. Therefore, we can determine that each indicator mentioned in this paper has valid samples when it is introduced into the time-varying parameter vector autoregression (TVP-VAR) model for parameter estimation. After sampling detection image analysis and data calculation, the effect of energy structure, energy intensity industrial structure, and scale effect on the emission scale of environmental pollutants was obtained. It is proved that through the research of this paper, two main conclusions are finally obtained, and the influence of the five factors mentioned above is summarized.

APA, Harvard, Vancouver, ISO, and other styles

36

Buldygin, V. V., and V. A. Koval. "Convergence to Zero and Boundedness of Operator-Normed Sums of Random Vectors with Application to Autoregression Processes." gmj 8, no. 2 (June 2001): 221–30. http://dx.doi.org/10.1515/gmj.2001.221.

Full text

Abstract:

Abstract The problems of almost sure convergence to zero and almost sure boundedness of operator-normed sums of different sequences of random vectors are studied. The sequences of independent random vectors, orthogonal random vectors and the sequences of vector-valued martingale-differences are considered. General results are applied to the problem of asymptotic behaviour of multidimensional autoregression processes.

APA, Harvard, Vancouver, ISO, and other styles

37

KÜÇÜKEFE, Bige, and Dündar Murat DEMİRÖZ. "FAVAR (Factor-Augmented Vector Autoregression) Modeli Literatür Taraması." Fiscaoeconomia 1, no. 2 (May 30, 2017): 38. http://dx.doi.org/10.25295/fsecon.295547.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Emerencia, Ando Celino, Lian van der Krieke, Elisabeth H. Bos, Peter de Jonge, Nicolai Petkov, and Marco Aiello. "Automating Vector Autoregression on Electronic Patient Diary Data." IEEE Journal of Biomedical and Health Informatics 20, no. 2 (March 2016): 631–43. http://dx.doi.org/10.1109/jbhi.2015.2402280.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Lanne, Markku, and Jani Luoto. "GMM Estimation of Non-Gaussian Structural Vector Autoregression." Journal of Business & Economic Statistics 39, no. 1 (July 18, 2019): 69–81. http://dx.doi.org/10.1080/07350015.2019.1629940.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Ni, Shawn, Dongchu Sun, and Xiaoqian Sun. "Intrinsic Bayesian Estimation of Vector Autoregression Impulse Responses." Journal of Business & Economic Statistics 25, no. 2 (April 2007): 163–76. http://dx.doi.org/10.1198/073500106000000378.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Hyatt, Henry R., and Tucker S. McElroy. "Labor Reallocation, Employment, and Earnings: Vector Autoregression Evidence." LABOUR 33, no. 4 (April 21, 2019): 463–87. http://dx.doi.org/10.1111/labr.12153.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Crompton, Paul, and Yanrui Wu. "Bayesian Vector Autoregression Forecasts of Chinese Steel Consumption." Journal of Chinese Economic and Business Studies 1, no. 2 (January 2003): 205–19. http://dx.doi.org/10.1080/1476528032000066703e.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Sanni, T. A. "Vector Autoregression on Nigerian Money and Agricultural Aggregates." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 34, no. 1 (March 1986): 67–85. http://dx.doi.org/10.1111/j.1744-7976.1986.tb02193.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Kim, Jae H. "Asymptotic and bootstrap prediction regions for vector autoregression." International Journal of Forecasting 15, no. 4 (October 1999): 393–403. http://dx.doi.org/10.1016/s0169-2070(99)00006-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Shintani, Mototsugu. "A simple cointegrating rank test without vector autoregression." Journal of Econometrics 105, no. 2 (December 2001): 337–62. http://dx.doi.org/10.1016/s0304-4076(01)00084-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Nielsen, Heino Bohn, and Anders Rahbek. "Unit root vector autoregression with volatility induced stationarity." Journal of Empirical Finance 29 (December 2014): 144–67. http://dx.doi.org/10.1016/j.jempfin.2014.03.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Kolodzeij, Marek, and Robert K. Kaufmann. "Oil demand shocks reconsidered: A cointegrated vector autoregression." Energy Economics 41 (January 2014): 33–40. http://dx.doi.org/10.1016/j.eneco.2013.10.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Zhu, Xuening. "Nonconcave penalized estimation in sparse vector autoregression model." Electronic Journal of Statistics 14, no. 1 (2020): 1413–48. http://dx.doi.org/10.1214/20-ejs1693.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Canarella, Giorgio, and Stephen K. Pollard. "Efficiency of commodity futures: A vector autoregression analysis." Journal of Futures Markets 5, no. 1 (1985): 57–76. http://dx.doi.org/10.1002/fut.3990050107.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Kim, Jae H. "Bias-corrected bootstrap prediction regions for vector autoregression." Journal of Forecasting 23, no. 2 (March 2004): 141–54. http://dx.doi.org/10.1002/for.908.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!