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.

Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

scholarly journals A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production

J ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 508-560
Author(s):  
Riccardo Corradini
Keyword(s):  
State Space ◽  
Industrial Production ◽  
Forecast Error ◽  
Moving Average ◽  
Predictive Ability ◽  
State Space Models ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
New Strategy ◽  
Moving Average Model

Normally, econometric models that forecast the Italian Industrial Production Index do not exploit information already available at time t + 1 for their own main industry groupings. The new strategy proposed here uses state–space models and aggregates the estimates to obtain improved results. The performance of disaggregated models is compared at the same time with a popular benchmark model, a univariate model tailored on the whole index, with persistent not formally registered holidays, a vector autoregressive moving average model exploiting all information published on the web for main industry groupings. Tests for superior predictive ability confirm the supremacy of the aggregated forecasts over three steps horizon using absolute forecast error and quadratic forecast error as a loss function. The datasets are available online.

scholarly journals The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields

PLoS ONE ◽  
2011 ◽  
Vol 6 (8) ◽  
pp. e22794 ◽  
Author(s):  
Kun Guo ◽  
Wei-Xing Zhou ◽  
Si-Wei Cheng ◽  
Didier Sornette
Keyword(s):  
Stock Market ◽  
Bond Yields ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
The Us ◽  
Treasury Bond

US Fed will struggle to raise rates much in 2016

2016 ◽  
Keyword(s):  
Interest Rates ◽  
Investor Sentiment ◽  
Commodity Prices ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Content Type ◽  
The Us ◽  
The Face ◽  
Lift Off ◽  
China’S Economy

Subject US monetary policy outlook for 2016 and its global impact. Significance There is a large discrepancy between the US Federal Reserve (Fed)'s estimates for interest rates at end-2016 and the expectations of bond investors. The latter are anticipating less tightening than the 100-basis-point (bp) rise in the Federal Funds rate the Fed has pencilled in for this year. Despite a successful rates 'lift-off' on December 16, the Fed faces many challenges in raising rates in the face of mounting stress in credit markets, disinflationary pressures from the plunge in commodity prices and a contraction manufacturing. Impacts While the Fed will tighten policy, other central banks, including the ECB, will provide further stimulus, accentuating policy divergence. Investors will price in a more hawkish Fed if US inflation accelerates faster than expected, potentially leading to a sell-off. Concerns about China's economy and the commodity prices slump will also shape investor sentiment.

Canadian monetary policy and the US federal funds rate

2008 ◽  
Vol 15 (11) ◽  
pp. 899-904 ◽  
Author(s):  
H. Sonmez Atesoglu ◽  
John Smithin
Keyword(s):  
Monetary Policy ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
The Us

Tracy–Widom law for the largest eigenvalue of sample covariance matrix generated by VARMA

2020 ◽  
pp. 2150022
Author(s):  
Boping Tian ◽  
Yangchun Zhang ◽  
Wang Zhou
Keyword(s):  
Covariance Matrix ◽  
Moving Average ◽  
Sample Covariance Matrix ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Largest Eigenvalue ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Sample Covariance ◽  
Moving Average Model

In this paper, we derive the Tracy–Widom law for the largest eigenvalue of sample covariance matrix generated by the vector autoregressive moving average model when the dimension is comparable to the sample size. This result is applied to make inference on the vector autoregressive moving average model. Simulations are conducted to demonstrate the finite sample performance of our inference.

Expectations about the Federal Funds Rate in the Long Run

FEDS Notes ◽  
2019 ◽  
Vol 2019 (2431) ◽  
Author(s):  
Kasper Joergensen ◽  
◽  
Andrew Meldrum ◽  
Keyword(s):  
Federal Funds ◽  
Federal Funds Rate ◽  
Long Run
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