The Information Content of the Federal Funds Rate: Is it Unique?

1995 ◽  
Vol 27 (3) ◽  
pp. 838 ◽  
Author(s):  
Michelle R. Garfinkel ◽  
Daniel L. Thornton
Keyword(s):  
Information Content ◽  
Federal Funds ◽  
Federal Funds Rate

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

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Savi Virolainen
Keyword(s):  
Interest Rate ◽  
Autoregressive Model ◽  
Autoregressive Models ◽  
Federal Funds ◽  
Treasury Bill ◽  
Federal Funds Rate ◽  
Empirical Application ◽  
Model Based ◽  
Interest Rate Spread ◽  
Student’S T

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.

scholarly journals Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

2021 ◽  
pp. 1-21
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Inflation Rate ◽  
Predictive Accuracy ◽  
Moving Average ◽  
Impulse Response Functions ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Long Run ◽  
The Us

Abstract Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

scholarly journals VARIABEL-VARIABEL PENGGERAK INDEKS HARGA SAHAM SEKTOR PERTANIAN

2020 ◽  
Vol 2019 (1) ◽  
pp. 720-725
Author(s):  
Muhammad Febrian Rizky Ramadhan ◽  
Gama Putra Danu Sohibien
Keyword(s):  
Federal Funds ◽  
Federal Funds Rate ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag

Indeks harga saham sektor pertanian di Indonesia cenderung menurun dari tahun 2009 – 2018 dengan rata-rata pertumbuhan sebesar -0,76 persen per tahun. Apabila tidak terjadi pemulihan harga, penurunan yang terjadi berpotensi menimbulkan sentimen buruk terhadap sektor pertanian dan menurunkan aliran modal masuk terhadap perusahaan-perusahaan yang tercakup dalam sektor tersebut. Dalam upaya memecahkan permasalahan tersebut, dilakukan identifikasi variabel-variabel yang mempengaruhi harga saham sektor pertanian. Adapun variabel yang diduga memiliki pengaruh terhadap harga saham sektor tersebut, yakni nilai tukar rupiah terhadap dolar Amerika Serikat, federal funds rate, harga minyak kelapa sawit, dan volume transaksi saham sektor pertanian. Dengan pemodelan Autoregressive Distributed-lag disimpulkan bahwa, keempat variabel tersebut memiliki pengaruh yang signifikan terhadap indeks harga saham sektor pertanian. Federal Funds Rate dan nilai tukar rupiah memiliki pengaruh negatif terhadap indeks harga saham sektor pertanian, sedangkan variabel lainnya memiliki pengaruh positif. Hasil penelitian ini diharapkan mampu memberikan informasi ke penggiat saham dan pemerintah, agar tidak terjadi kerugian yang besar di periode-periode selanjutnya.

scholarly journals The impacts of interest rate changes on US Midwest farmland values

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Albulena Basha ◽  
Wendong Zhang ◽  
Chad Hart
Keyword(s):  
Interest Rate ◽  
Great Plains ◽  
Federal Reserve ◽  
State Level ◽  
Land Values ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Content Type ◽  
Distributed Lag ◽  
Farmland Values

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.

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