Beveridge Curve Shifts and Time-Varying Parameter VARs

Lubik, Thomas A.; Matthes, Christian

doi:10.21144/eq1020302

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…An excessively large value (e.g., κ 2 Q ≥ 1) could lead to erroneous time-variation attribution in the VAR. Lubik and Matthes (2017) and Amir-Ahmadi, Matthes, and Wang (2020) provide a discussion on this issue.…”

Section: Sampling Algorithmmentioning

confidence: 99%

The Time‐Varying Response of Hours Worked to a Productivity Shock

LI¹

2022

J of Money Credit Banking

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This paper revisits the dynamic response of hours worked to a total factor productivity (TFP) shock. I estimate a structural vector autoregression that includes time‐varying parameters and stochastic volatility. The estimation produces structural parameters that are consistent with the long‐run identification. The impulse response functions of hours worked to a TFP shock are negative on impact and at the business cycle horizons. This is evidence that Galí (1999) would interpret as supporting new Keynesian theory. My results also show that TFP shocks are the dominant source of variation in average labor productivity. Structural changes in the U.S. economy play an important role in the TFP–hours worked relationship.

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Section: Sampling Algorithmmentioning

confidence: 99%

The Time‐Varying Response of Hours Worked to a Productivity Shock

LI¹

2022

J of Money Credit Banking

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“…In a simple example above, we argue that the inclusion of stochastic volatility is necessary to avoid a pitfall in the opposite direction. Lubik, Matthes, and Owens (2016) address this aspect in a simulation study based on an underlying nonlinear model and judge that a TVP-VAR does in fact come to the right conclusion as to the sources of time variation, but that a judicious choice of prior is crucial.…”

Section: Figure 1 Estimated Coe Cientsmentioning

confidence: 99%

“…As it turns out, choice of these parameters can a¤ect estimation results along many dimensions. For a recent application that studies the importance of these hyperparameters in producing the 'correct' inference seeLubik, Matthes, and Owens (2016).…”

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confidence: 99%

Time-Varying Parameter Vector Autoregressions: Specification, Estimation, and an Application

Lubik

Matthes

2016

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T ime-varying parameter vector autoregressions (TVP-VARs) have become an increasingly popular tool for analyzing the behavior of macroeconomic time series. TVP-VARs di¤er from more standard …xed-coe¢ cient VARs in that they allow for coe¢ cients in an otherwise linear VAR model to vary over time following a speci…ed law of motion. In addition, TVP-VARs often include stochastic volatility (SV), which allows for time variation in the variances of the error processes that a¤ect the VAR.The attractiveness of TVP-VARs is based on the recognition that many, if not most, macroeconomic time series exhibit some form of nonlinearity. For instance, the unemployment rate tends to rise much faster at the start of a recession than it declines at the onset of a recovery. Stock market indices exhibit occasional episodes where volatility, as measured by the variance of stock price movements, rises considerably. As a third example, many aggregate series show a distinct change in behavior in terms of their persistence and their volatility around the early 1980s when the Great In ‡ation of the 1970s turned into the Great Moderation, behavior that is akin to a structural shift in certainWe are grateful to Pierre-Daniel Sarte, Daniel Tracht, John Weinberg, and Alex Wolman, whose comments greatly improved the exposition of this paper. The views expressed in this paper are those of the authors and not necessarily those of the Federal Reserve Bank of Richmond or the Federal Reserve System.

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Detecting time variation in the price puzzle: a less informative prior choice for time varying parameter VAR models

Reusens

Croux

2017

Studies in Nonlinear Dynamics &Amp; Econometrics

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This paper compares Bayesian estimators with different prior choices for the time variation of the coefficients of Time Varying Parameter Vector Autoregression models using Monte Carlo simulations. Since the commonly used prior choice only allows for a tiny amount of time variation, less informative priors are proposed. Additional empirical evidence on the time varying response of inflation to an interest rate shock is provided for USA. While a ‘price puzzle’ is detected for the period 1972–1979, the estimated response of inflation to an interest rate shock is negative for most other time periods.

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Beveridge Curve Shifts and Time-Varying Parameter VARs

Cited by 5 publications

References 14 publications

The Time‐Varying Response of Hours Worked to a Productivity Shock

The Time‐Varying Response of Hours Worked to a Productivity Shock

Time-Varying Parameter Vector Autoregressions: Specification, Estimation, and an Application

Detecting time variation in the price puzzle: a less informative prior choice for time varying parameter VAR models

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