Wage Net Discount Rates: 1981–2012

2014 ◽  
Vol 25 (2) ◽  
pp. 153-174 ◽  
Author(s):  
David Schap ◽  
Robert Baumann ◽  
Lauren Guest
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Discount Rate ◽  
Constant Term ◽  
Full Time ◽  
Discount Rates ◽  
Short Term ◽  
Treasury Securities ◽  
Short Run ◽  
Short Term Forecasting

Abstract The study explores time series properties of three wage net discount rate series derived using interest rates based on (alternatively) 1-year, 6-month and 3-month Treasury securities coupled with wage growth rates, initially for the period from 1981:01 to 2012:06, then subsequently through 2012:12. Stationarity tests are run on the full series and various sub-series to identify any portion of the series on which reliable forecasting can be based. Initially no support is found for the total offset hypothesis based on the full-time series (but support is subsequently found for total offset when exploring various data sub-series). Positive findings include that the three trended wage net growth rate series for the entire period under study are stationary, implying that reliable short-term forecasting of wage net discount rates is possible based on each of the trended series. Short-run forecasts based on the trended series are presented for 2012:07–2012:12 and compared to actual data in the period. Finally, the three trended wage net discount rate series are re-examined for 1981:01–2012:12, with short-term forecasting equations presented. Various sub-series of WNDRs are then explored in hopes of finding one or more that may be stationary about a constant term. Additional testing identifies three sub-series all ending 2012:12 with varying start dates that have desirable stationarity properties. The sub-series starting 2007:11 is highly stationary, but peculiar, with an associated constant WNDR that is negative and statistically different from zero; and the two sub-series beginning 1990:12 and 1994:05 have stationary attributes, yet possess constant terms that are slightly positive but not statistically different from zero, thus providing modest support for the total offset method.

scholarly journals Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Regime Switching ◽  
Neural Nets ◽  
Ann Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Ramp Events ◽  
Artificial Neural ◽  
Short Term Forecasting

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

Determinants of Treasury-LIBOR Swap Spreads

2005 ◽  
Vol 08 (04) ◽  
pp. 687-705 ◽  
Author(s):  
D. K. Malhotra ◽  
Vivek Bhargava ◽  
Mukesh Chaudhry
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Default Risk ◽  
Yield Curve ◽  
Short Term ◽  
Long Run ◽  
Interest Rate Volatility ◽  
Short Run ◽  
Using Data ◽  
Swap Spreads

Using data from the Treasury versus London Interbank Offer Swap Rates (LIBOR) for October 1987 to June 1998, this paper examines the determinants of swap spreads in the Treasury-LIBOR interest rate swap market. This study hypothesizes Treasury-LIBOR swap spreads as a function of the Treasury rate of comparable maturity, the slope of the yield curve, the volatility of short-term interest rates, a proxy for default risk, and liquidity in the swap market. The study finds that, in the long-run, swap spreads are negatively related to the yield curve slope and liquidity in the swap market. We also find that swap spreads are positively related to the short-term interest rate volatility. In the short-run, swap market's response to higher default risk seems to be higher spread between the bid and offer rates.

scholarly journals Risks and the influence of negative interest rates on economic activity: a case study of Sweden, Denmark, and Switzerland

2020 ◽  
Vol 15 (1) ◽  
pp. 30-41
Author(s):  
Liběna Černohorská ◽  
Darina Kubicová
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Granger Causality ◽  
Economic Activity ◽  
Central Banks ◽  
Time Interval ◽  
Short Term ◽  
Stock Indices ◽  
Negative Interest Rates ◽  
The Impact

The purpose of this paper is to analyze the impact of negative interest rates on economic activity in a selected group of countries, in particular Sweden, Denmark, and Switzerland, for the period 2009–2018. The central banks of these countries were among the first to implement negative interest rates to revive the economic growth. Therefore, this study analyzed long- and short-term relationships between interest rates announced by central banks and gross domestic product and blue chip stock indices. Time series analysis was conducted using Engle-Granger cointegration analysis and Granger causality testing to identify long- and short-term relationship. The first step, using the Akaike criteria, was to determine the optimal delay of the entire time interval for the analyzed periods. Time series that seem to be stationary were excluded based on the results of the Dickey-Fuller test. Further testing continued with the Engle-Granger test if the conditions were met. It was designed to identify co-integration relationships that would show correlation between the selected variables. These tests showed that at a significance level of 0.05, there is no co-integration between any time series in the countries analyzed. On the basis of these analyses, it was determined that there were no long-term relationships between interest rates and GDP or stock indices for these countries during the monitored time period. Using Granger causality, the study only confirmed short-term relationship between interest rates and GDP for all examined countries, though not between interest rates and the stock indices. Acknowledgment The paper has been created with the financial support of The Czech Science Foundation GACR 18-05244S – Innovative Approaches to Credit Risk Management.

scholarly journals Forecasting of boro rice production in Bangladesh: An ARIMA approach

1970 ◽  
Vol 8 (1) ◽  
pp. 103-112 ◽  
Author(s):  
NMF Rahman
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Rice Production ◽  
Arima Models ◽  
Short Term ◽  
Production Uncertainty ◽  
Future Production ◽  
Short Run ◽  
The Government ◽  
Boro Rice

The study was undertaken to examine the best fitted ARIMA model that could be used to make efficient forecast boro rice production in Bangladesh from 2008-09 to 2012-13. It appeared from the study that local, modern and total boro time series are 1st order homogenous stationary. It is found from the study that the ARIMA (0,1,0) ARIMA (0,1,3) and ARIMA (0,1,2) are the best for local, modern and total boro rice production respectively. It is observed from the analysis that short term forecasts are more efficient for ARIMA models. The production uncertainty of boro rice can be minimizing if production can be forecasted well and necessary steps can be taken against losses. The government and producer as well use ARIMA methods to forecast future production more accurately in the short run. Keywords: Production; ARIMA model; Forecasting. DOI: 10.3329/jbau.v8i1.6406J. Bangladesh Agril. Univ. 8(1): 103-112, 2010

scholarly journals ANALYSIS OF METHODS OF INCREASING DATA RELIABILITY FOR PROBLEMS OF SHORT TERM FORECASTING OF NODAL LOAD

2021 ◽  
Vol 2021 (60) ◽  
pp. 51-57
Author(s):  
P.V. Shymaniuk ◽  
◽  
V.O. Miroshnyk ◽  
Keyword(s):  
United States ◽  
Time Series ◽  
Comparative Analysis ◽  
Decomposition Methods ◽  
The United States ◽  
Data Validation ◽  
Clustering Methods ◽  
Short Term ◽  
Northwestern Region ◽  
Short Term Forecasting

A comparative analysis of clustering methods was performed to identify gaps and anomalous values in the data. Data from the northwestern region of the United States were used for evaluation. According to the analysis results, it was found that the use of the DBSCAN method leads to a much smaller number of false positives. An algorithm for two-stage data validation using clustering and time series decomposition methods is proposed. Ref.9, fig. 3, tables 3.

scholarly journals Credit Cycles: Econometric Analysis and Evidence for Russia

2014 ◽  
pp. 113-122
Author(s):  
N. V. Artamonov ◽  
D. V. Artamonov ◽  
V. A. Artamonov
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Interest Rates ◽  
Russian Federation ◽  
Statistical Data ◽  
Econometric Analysis ◽  
Short Term ◽  
Credit Cycles ◽  
Real Gdp

One of the principal problem in contemporary macroeconomics is concerned with factors increasing or decreasing economic dynamics. The mainstream approach is based on neoclassical assumptions, but recently new approaches appear mostly based on new Keynesian concepts. In present time the influence of monetary market and credit instruments become more and more significant. Credit resources of banking and financial structures can affect and distort to reallocation of resources for national and even for global economic. In present paper an empiric and econometric analysis for some macroeconometric and monetary indices for Russian Federation is done. An econometrical models describing the influence of credit variables onto real GDP is estimated. It is shown that in short-term periods changes in credit variables do influence significantly onto GDP. It is shown that on short-term periods changes in money aggregate M2 brings influence (through credit variables) onto national output. As well it is shown that changes in short-term interest rate brings significant negative influence onto real output. Impulse response functions for GDP on shocks of credit variables, monetary base and short-term interest rate are evaluated. For the present study of credit cycles and their impact to real business cycles statistical data (quarterly time series) on the following factors for Russian Federation are collected: nominal and real GDP, monetary base M2, short-term interest rate, long-term interest rate (10-year treasuries bill rate), total debt outstanding. All time series are seasonally adjusted and collected for the period 2004 Q1 - 2013 Q2. All interest rates are adjusted for inflation (i.e. we deal with real interest rates). The investigation of long-term relationship for the factors under consideration are based on integration. It is important to note that in the present paper all econometric models are estimated on "pure" statistical data, while in many research papers on business and credit cycles all evaluations and inferences are based on "filtered" time series (mostly filtered by Hodrick-Prescott's method). In present paper "causality" always means "Granger causality". All estimations are made in gretl, an open-source multiplatform econometric software.

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