Topic
Vector autoregression
About: Vector autoregression is a research topic. Over the lifetime, 5624 publications have been published within this topic receiving 163096 citations. The topic is also known as: VAR.
Papers published on a yearly basis
Papers
TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
Abstract: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples. If each element of a vector of time series x first achieves stationarity after differencing, but a linear combination a'x is already stationary, the time series x are said to be co-integrated with co-integrating vector a. There may be several such co-integrating vectors so that a becomes a matrix. Interpreting a'x,= 0 as a long run equilibrium, co-integration implies that deviations from equilibrium are stationary, with finite variance, even though the series themselves are nonstationary and have infinite variance. The paper presents a representation theorem based on Granger (1983), which connects the moving average, autoregressive, and error correction representations for co-integrated systems. A vector autoregression in differenced variables is incompatible with these representations. Estimation of these models is discussed and a simple but asymptotically efficient two-step estimator is proposed. Testing for co-integration combines the problems of unit root tests and tests with parameters unidentified under the null. Seven statistics are formulated and analyzed. The critical values of these statistics are calculated based on a Monte Carlo simulation. Using these critical values, the power properties of the tests are examined and one test procedure is recommended for application. In a series of examples it is found that consumption and income are co-integrated, wages and prices are not, short and long interest rates are, and nominal GNP is co-integrated with M2, but not M1, M3, or aggregate liquid assets.
33,350 citations
TL;DR: In this article, the authors consider estimation and hypothesis testing in linear time series models when some or all of the variables have unit roots and show that parameters that can be written as coefficients on mean zero, nonintegrated regressors have jointly normal asymptotic distributions, converging at the rate T'/2.
Abstract: This paper considers estimation and hypothesis testing in linear time series models when some or all of the variables have unit roots. Our motivating example is a vector autoregression with some unit roots in the companion matrix, which might include polynomials in time as regressors. In the general formulation, the variable might be integrated or cointegrated of arbitrary orders, and might have drifts as well. We show that parameters that can be written as coefficients on mean zero, nonintegrated regressors have jointly normal asymptotic distributions, converging at the rate T'/2. In general, the other coefficients (including the coefficients on polynomials in time) will have nonnormal asymptotic distributions. The results provide a formal characterization of which t or F tests-such as Granger causality tests-will be asymptotically valid, and which will have nonstandard limiting distributions.
2,928 citations
TL;DR: In this article, the authors examined the behavior of forecasts made from a co-integrated system as introduced by Granger (1981), Granger and Weiss (1983), and Engle and Granger (1987).
2,226 citations
TL;DR: In this article, the authors apply vector autoregression (VAR) to firm-level panel data from 36 countries to study the dynamic relationship between firms' financial conditions and investment, and find that the impact of financial factors on investment is significantly larger in countries with less developed financial systems.
1,962 citations
Posted Content•
TL;DR: In this article, a factor-augmented structural vector autoregressions (FAVAR) methodology is proposed to identify the monetary transmission mechanism. But the authors do not provide a comprehensive and coherent picture of the effect of monetary policy on the economy.
Abstract: Structural vector autoregressions (VARs) are widely used to trace out the effect of monetary policy innovations on the economy. However, the sparse information sets typically used in these empirical models lead to at least two potential problems with the results. First, to the extent that central banks and the private sector have information not reflected in the VAR, the measurement of policy innovations is likely to be contaminated. A second problem is that impulse responses can be observed only for the included variables, which generally constitute only a small subset of the variables that the researcher and policymaker care about. In this paper we investigate one potential solution to this limited information problem, which combines the standard structural VAR analysis with recent developments in factor analysis for large data sets. We find that the information that our factor-augmented VAR (FAVAR) methodology exploits is indeed important to properly identify the monetary transmission mechanism. Overall, our results provide a comprehensive and coherent picture of the effect of monetary policy on the economy.
1,723 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 120 |
| 2024 | 171 |
| 2023 | 313 |
| 2022 | 454 |
| 2021 | 262 |
| 2020 | 314 |