scholarly journals A mixture autoregressive model based on Gaussian and Student’s t-distributions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Savi Virolainen

Abstract We introduce a new mixture autoregressive model which combines Gaussian and Student’s t mixture components. The model has very attractive properties analogous to the Gaussian and Student’s t mixture autoregressive models, but it is more flexible as it enables to model series which consist of both conditionally homoscedastic Gaussian regimes and conditionally heteroscedastic Student’s t regimes. The usefulness of our model is demonstrated in an empirical application to the monthly U.S. interest rate spread between the 3-month Treasury bill rate and the effective federal funds rate.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Albulena Basha ◽  
Wendong Zhang ◽  
Chad Hart

PurposeThis paper quantifies the effects of recent Federal Reserve interest rate changes, specifically recent hikes and cuts in the federal funds rate since 2015, on Midwest farmland values.Design/methodology/approachThe authors apply three autoregressive distributed lag (ARDL) models to a panel data of state-level farmland values from 1963 to 2018 to estimate the dynamic effects of interest rate changes on the US farmland market. We focus on the I-states, Lakes states and Great Plains states. The models in the study capture both short-term and long-term impacts of policy changes on land values.FindingsThe authors find that changes in the federal funds rate have long-lasting impacts on farmland values, as it takes at least a decade for the full effects of an interest rate change to be capitalized in farmland values. The results show that the three recent federal funds rate cuts in 2019 were not sufficient to offset the downward pressures from the 2015–2018 interest rate hikes, but the 2020 cut is. The combined effect of the Federal Reserve's recent interest rate moves on farmland values will be positive for some time starting in 2022.Originality/valueThis paper provides the first empirical quantification of the immediate and long-run impacts of recent Federal Reserve interest rate moves on farmland values. The authors demonstrate the long-lasting repercussions of Federal Reserve's policy choices in the farmland market.


FEDS Notes ◽  
2017 ◽  
Vol 2017 (2076) ◽  
Author(s):  
Ashish Kumbhat ◽  
◽  
Francisco Palomino ◽  
Ander Perez-Orive ◽  
◽  
...  

2021 ◽  
Vol 2021 (064) ◽  
pp. 1-40
Author(s):  
Callum Jones ◽  
◽  
Mariano Kulish ◽  
James Morley ◽  
◽  
...  

We propose a shadow policy interest rate based on an estimated structural model that accounts for the zero lower bound. The lower bound constraint, if expected to bind, is contractionary and increases the shadow rate compared to an unconstrained systematic policy response. By contrast, forward guidance and other unconventional policies that extend the expected duration of zero-interest-rate policy are expansionary and decrease the shadow rate. By quantifying these distinct effects, our structural shadow federal funds rate better captures the stance of monetary policy given economic conditions than a shadow rate based only on the term structure of interest rates.


2017 ◽  
Vol 21 (2) ◽  
Author(s):  
Tim Oliver Berg

AbstractThis paper discusses how the forecast accuracy of a Bayesian vector autoregression (BVAR) is affected by introducing the zero lower bound on the federal funds rate. As a benchmark I adopt a common BVAR specification, including 18 variables, estimated shrinkage, and no nonlinearity. Then I entertain alternative specifications of the zero lower bound. I account for the possibility that the effect of monetary policy on the economy is different in this regime, replace the federal funds rate by its shadow rate, consider a logarithmic transformation, feed in monetary policy shocks, or utilize conditional forecasts allowing for all shocks implemented through a rejection sampler. The latter two are also coupled with interest rate expectations from future contracts. It is shown that the predictive densities of all these specifications are greatly different, suggesting that this modeling choice is not innocuous. The comparison is based on the accuracy of point and density forecasts of major US macroeconomic series during the period 2009:1 to 2014:4. The introduction of the zero lower bound is not beneficial per se, but it depends on how it is done and which series is forecasted. With caution, I recommend the shadow rate specification and the rejection sampler combined with interest rate expectations to deal with the nonlinearity in the policy rate. Since the policy rate will remain low for some time, these findings could prove useful for practical forecasters.


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