Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macro-economic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross-correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the "Great Moderation"?

/pdf/shocks-and-frictions-in-us-business-cycles-a-bayesian-dsge-d1f6ynd58q.pdf

Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach

The endogenous two-break unit root test of Lumsdaine and Papell is derived assuming no structural breaks under the null. Thus, rejection of the null does not necessarily imply rejection of a unit root per se, but may imply rejection of a unit root without break. Similarly, the alternative does not necessarily imply trend stationarity with breaks, but may indicate a unit root with breaks. In this paper, we propose an endogenous two-break Lagrange multiplier unit root test that allows for breaks under both the null and alternative hypotheses. As a result, rejection of the null unambiguously implies trend stationarity.

/pdf/minimum-lagrange-multiplier-unit-root-test-with-two-4b1979hgc5.pdf

Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks

A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process.

/pdf/roughing-it-up-including-jump-components-in-the-measurement-35xg2w5blh.pdf

Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility

https://www.lef.wiwi.uni-due.de/fileadmin/fileupload/BWL-LEF/Seminarreihe/RWeron14_Essen.pdf

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.

Unit root tests are considered for time series which have a level shift at a known point in time. The shift can have a very general nonlinear form, and additional deterministic mean and trend terms are allowed for. Prior to the tests, the deterministic parts and other nuisance parameters of the data generation process are estimated in a first step. Then, the series are adjusted for these terms and unit root tests of the Dickey-Fuller type are applied to the adjusted series. The properties of previously suggested tests of this sort are analysed and modifications are proposed which take into account estimation errors in the nuisance parameters. An important result is that estimation under the null hypothesis is preferable to estimation under local alternatives. This contrasts with results obtained by other authors for time series without level shifts.

https://papers.ssrn.com/sol3/Delivery.cfm/JTSA285.pdf?abstractid=370487&mirid=2

Comparison of Unit Root Tests for Time Series with Level Shifts

Unit root tests are considered for time series which have a level shift at a known point in time. The shift can have a very general nonlinear form, and additional deterministic mean and trend terms are allowed for. Prior to the tests, the deterministic parts and other nuisance parameters of the data generation process are estimated in a first step. Then, the series are adjusted for these terms and unit root tests of the Dickey–Fuller type are applied to the adjusted series. The properties of previously suggested tests of this sort are analysed and modifications are proposed which take into account estimation errors in the nuisance parameters. An important result is that estimation under the null hypothesis is preferable to estimation under local alternatives. This contrasts with results obtained by other authors for time series without level shifts.

/pdf/comparison-of-unit-root-tests-for-time-series-with-level-1kzrtdrg3z.pdf

Comparison of unit root tests for time series with level shifts

https://www.bancaditalia.it/pubblicazioni/altri-atti-convegni/2010-ii-conf-int-giannini/III.2_1_Presentation_Luetkepohl.pdf

Structural Vector Autoregressions with Markov Switching

A central issue of monetary policy analysis is the speciflcation of monetary policy shocks. In a structural vector autoregressive setting there has been some controversy about which restrictions to use for identifying the shocks because standard theories do not provide enough information to fully identify monetary policy shocks. In fact, to compare difierent theories it would even be desirable to have over-identifying restrictions which would make statistical tests of difierent theories possible. It is pointed out that some progress towards over-identifying monetary policy shocks can be made by using speciflc data properties. In particular, it is shown that changes in the volatility of the shocks can be used for identiflcation. Based on monthly US data from 1965-1996 difierent theories are tested and it is found that as- sociating monetary policy shocks with shocks to nonborrowed reserves leads to a particularly strong rejection of the model whereas assuming that the Fed accommodates demand shocks to total reserves cannot be rejected.

https://www.econstor.eu/bitstream/10419/25789/1/517004011.PDF

Identifying Monetary Policy Shocks via Changes in Volatility

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-identifying in a standard SVAR framework. The results are illustrated using a U.S. macro data set and a system of U.S. and European interest rates.

https://www.eco.uc3m.es/temp/agenda/luetkepohl15may2006.pdf

Markku Lanne

Papers

Comparison of Unit Root Tests for Time Series with Level Shifts

Comparison of unit root tests for time series with level shifts

Structural Vector Autoregressions with Markov Switching

Identifying Monetary Policy Shocks via Changes in Volatility

Structural Vector Autoregressions With Nonnormal Residuals