Markku Lanne
Biographic Data
| ID | 8879119 |
|---|---|
| NAME | Markku Lanne |
| GIVEN NAMES | Markku |
| FAMILY NAME | Lanne |
| SIGNATURE | LANNE M |
| AFFILIATIONS | University of Helsinki |
| ORCID | 0000-0001-9397-2578 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1999 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Statistically identified structural VAR model with potentially skewed and fat‐tailed errors
We introduce a structural vector autoregressive model in which the mutually independent errors follow skewed generalized t ‐distributions, whose flexibility compared with commonly considered Student's t ‐distributions diminishes the risk of misspecification and strengthens identification. Because of statistical identification due to non‐Gaussianity, the plausibility of economic identifying restrictions can be formally assessed. In an empirical ap…
Identifying Structural Vector Autoregression via Leptokurtic Economic Shocks
We revisit the generalized method of moments (GMM) estimation of the non-Gaussian structural vector autoregressive (SVAR) model. It is shown that in the n-dimensional SVAR model, global and local identification of the contemporaneous impact matrix is achieved with as few as n2+n(n−1)/2 suitably selected moment conditions, when at least n – 1 of the structural errors are all leptokurtic (or platykurtic). We also relax the potentially problematic a…
GMM Estimation of Non-Gaussian Structural Vector Autoregression
We consider estimation of the structural vector autoregression (SVAR) by the generalized method of moments (GMM). Given non-Gaussian errors and a suitable set of moment conditions, the GMM estimator is shown to achieve local identification of the structural shocks. The optimal set of moment conditions can be found by well-known moment selection criteria. Compared to recent alternatives, our approach has the advantage that the structural shocks ne…
Structural Vector Autoregressions With Nonnormal Residuals
In structural vector autoregressive (SVAR) modeling, sometimes the identifying restrictions are insufficient for a unique specification of all shocks. In this paper it is pointed out that specific distributional assumptions can help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a possible model that can be used in this context. Our model setup enables us to test restrictions which are just…
A Multivariate Generalized Orthogonal Factor GARCH Model
We propose a factor generalized autoregressive conditional heteroscedasticity (GARCH) model and develop test procedures for checking the correctness of the number of factors. Maximum likelihood estimation of the model is straightforward once computationally simple preliminary estimates are obtained. Motivated by the empirical application of the article, a mixture of Gaussian distributions is considered in addition to the conventional Gaussian lik…
Threshold Autoregressions for Strongly Autocorrelated Time Series
In some cases the unit root or near unit root behavior of linear autoregressive models fitted to economic time series is not in accordance with the underlying economic theory. To accommodate this feature we consider a threshold autoregressive (TAR) process with the threshold effect only in the intercept term. Although these processes are stationary, their realizations switch between different regimes and can therefore closely resemble those of (n…
Testing the Predictability of Stock Returns
Previous literature indicates that stock returns are predictable by several strongly autocorrelated forecasting variables, especially at longer horizons. It is suggested that this finding is spurious and follows from a neglected near unit root problem. Instead of the commonly used t-test, we propose a test that can be considered as a general test of whether the return can be predicted by any highly persistent variable. Using this test, no predict…
Near Unit Roots and the Predictive Power of Yield Spreads for Changes in Long-Term Interest Rates
The ability of yield spreads to predict changes in long-term interest rates implied by the expectations hypothesis is usually rejected. It is suggested that this rejection is often caused by high persistence in the spread when standard inference is employed. Instead, the asymptotically valid method of Cavanagh et al. (1995) is applied to monthly U.S. data from 1952:1-1991:2. The persistence of the spreads seems to have varied over time, and in su…
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Near Unit Roots and the Predictive Power of Yield Spreads for Changes in Long-Term Interest Rates
The ability of yield spreads to predict changes in long-term interest rates implied by the expectations hypothesis is usually rejected. It is suggested that this rejection is often caused by high persistence in the spread when standard inference is employed. Instead, the asymptotically valid method of Cavanagh et al. (1995) is applied to monthly U.S. data from 1952:1-1991:2. The persistence of the spreads seems to have varied over time, and in su…
Threshold Autoregressions for Strongly Autocorrelated Time Series
In some cases the unit root or near unit root behavior of linear autoregressive models fitted to economic time series is not in accordance with the underlying economic theory. To accommodate this feature we consider a threshold autoregressive (TAR) process with the threshold effect only in the intercept term. Although these processes are stationary, their realizations switch between different regimes and can therefore closely resemble those of (n…
Testing the Predictability of Stock Returns
Previous literature indicates that stock returns are predictable by several strongly autocorrelated forecasting variables, especially at longer horizons. It is suggested that this finding is spurious and follows from a neglected near unit root problem. Instead of the commonly used t-test, we propose a test that can be considered as a general test of whether the return can be predicted by any highly persistent variable. Using this test, no predict…
A Multivariate Generalized Orthogonal Factor GARCH Model
We propose a factor generalized autoregressive conditional heteroscedasticity (GARCH) model and develop test procedures for checking the correctness of the number of factors. Maximum likelihood estimation of the model is straightforward once computationally simple preliminary estimates are obtained. Motivated by the empirical application of the article, a mixture of Gaussian distributions is considered in addition to the conventional Gaussian lik…
Structural Vector Autoregressions With Nonnormal Residuals
In structural vector autoregressive (SVAR) modeling, sometimes the identifying restrictions are insufficient for a unique specification of all shocks. In this paper it is pointed out that specific distributional assumptions can help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a possible model that can be used in this context. Our model setup enables us to test restrictions which are just…
GMM Estimation of Non-Gaussian Structural Vector Autoregression
We consider estimation of the structural vector autoregression (SVAR) by the generalized method of moments (GMM). Given non-Gaussian errors and a suitable set of moment conditions, the GMM estimator is shown to achieve local identification of the structural shocks. The optimal set of moment conditions can be found by well-known moment selection criteria. Compared to recent alternatives, our approach has the advantage that the structural shocks ne…
Identifying Structural Vector Autoregression via Leptokurtic Economic Shocks
We revisit the generalized method of moments (GMM) estimation of the non-Gaussian structural vector autoregressive (SVAR) model. It is shown that in the n-dimensional SVAR model, global and local identification of the contemporaneous impact matrix is achieved with as few as n2+n(n−1)/2 suitably selected moment conditions, when at least n – 1 of the structural errors are all leptokurtic (or platykurtic). We also relax the potentially problematic a…
Statistically identified structural VAR model with potentially skewed and fat‐tailed errors
We introduce a structural vector autoregressive model in which the mutually independent errors follow skewed generalized t ‐distributions, whose flexibility compared with commonly considered Student's t ‐distributions diminishes the risk of misspecification and strengthens identification. Because of statistical identification due to non‐Gaussianity, the plausibility of economic identifying restrictions can be formally assessed. In an empirical ap…
Econometrics (8 works) · Mathematics (7 works) · Economics (6 works) · Monetary Policy and Economic Impact (6 works) · Statistics (6 works) · Autoregressive model (5 works) · Computer Science (5 works) · Market Dynamics and Volatility (5 works) · Vector autoregression (4 works) · Financial Risk and Volatility Modeling (3 works)