scholarly journals The Importance of Time-Series Extrapolation for Macroeconomic Expectations

2012 ◽  
Vol 13 (2) ◽  
pp. 196-210 ◽  
Author(s):  
Michael W. M. Roos ◽  
Ulrich Schmidt
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Historical Data ◽  
Decisive Factor ◽  
Historical Time ◽  
Experimental Setting ◽  
Simple Experiment ◽  
Gross Domestic Product Growth ◽  
Expectation Formation ◽  
Expert Forecasts

Abstract This article presents a simple experiment on how laypeople form macroeconomic expectations. Subjects have to forecast inflation and gross domestic product growth. By varying the information provided in different treatments, we can assess the importance of historical time-series information vs. information acquired outside the experimental setting such as knowledge of expert forecasts. It turns out that the availability of historical data has a dominant impact on expectations and wipes out the influence of outside-lab information completely. Consequently, backward-looking behavior can be identified unambiguously as a decisive factor in expectation formation.

scholarly journals Implementasi Aplikasi Sistem Peramalan Persedian Barang Menggunakan Metode Single Moving Average Berbasis Web

2021 ◽  
Vol 14 (2) ◽  
pp. 224-233
Author(s):  
Eko siswanto ◽  
Eka Satria Wibawa ◽  
Zaenal Mustofa
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Historical Time ◽  
Inventory Planning ◽  
Future Period ◽  
Average Value ◽  
Forecasting Method ◽  
Past Data ◽  
Future Demand

Forecasting is an estimate of future demand based on several forecasting variables based on historical time series or a process of using historical data (past data) that has been owned to use this model and use this model to estimate future conditions.The Ivori mini market SME group is known to be a mini market that sells daily necessities. The goods provided by the ivori mini market are not focused on only one type of goods, but include all types of goods. Ivori mini market often runs out of stock because there is no inventory planning. The main purpose of making this application is to assist employees in determining inventory planning that must be provided next month. While the method used to make this forecast is a single moving average, one of the time series methods in forecasting. Single Moving Average is a forecasting method that is done by collecting a group of observed values, looking for the average value as a forecast for the future period. The result of this forecasting is to predict the number of sales that will occur in the coming month.

scholarly journals SINGLE AND MULTI-SITE OPERATIONAL HYDROLOGY

1972 ◽  
Vol 3 (4) ◽  
pp. 214-238 ◽  
Author(s):  
T. A. McMAHON ◽  
G. P. CODNER ◽  
C. PHILIPS
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Historical Data ◽  
Probability Distributions ◽  
Supply System ◽  
Random Component ◽  
Historical Time ◽  
Site Analysis ◽  
Normal Transformation ◽  
Log Normal

This paper reviews several different single- and multi-site generation models to complement those papers in which the models are developed but not applied. Initially the historical time series is looked at using various time series techniques, including correlograms and spectral analyses, to determine whether a lag one Markov model will satisfactorily represent the historical data. The single site models which are examined include an empirical model using the historical probability distribution of the random component, the Thomas and Fiering model using both logarithms of the data and Matalas' log-normal transformation equations, and finally Moran's Gamma distribution. Matalas' Residual method with several probability distributions and Moran's Multivariate Gamma technique are used in the multi-site analysis. All models are applied to the streams of the Melbourne metropolitan water supply system.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

scholarly journals A Hybrid Energy System Workflow for Energy Portfolio Optimization

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4392
Author(s):  
Jia Zhou ◽  
Hany Abdel-Khalik ◽  
Paul Talbot ◽  
Cristian Rabiti
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Electricity Market ◽  
Process Model ◽  
Net Present Value ◽  
Historical Data ◽  
Energy System ◽  
Initial Guess ◽  
Reduced Order ◽  
The Impact

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

A Bayesian Change Point Model for Historical Time Series Analysis

2004 ◽  
Vol 12 (4) ◽  
pp. 354-374 ◽  
Author(s):  
Bruce Western ◽  
Meredith Kleykamp
Keyword(s):  
Time Series ◽  
Change Point ◽  
Regression Coefficients ◽  
Simulation Methods ◽  
Historical Time ◽  
Point Location ◽  
Point Model ◽  
Political Relationships ◽  
Standard Models ◽  
Change Point Model

Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.

FINANCIAL TECHNOLOGIES FOR THE DEVELOPMENT OF ENTERPRISES AND THE RUSSIAN ECONOMY

2021 ◽  
Vol 1 (10) ◽  
pp. 91-106
Author(s):  
Evgeny V. Sokolov ◽  
◽  
Evgeny V. Kostyrin ◽  
Svetlana V. Lasunova ◽  
◽  
...  
Keyword(s):  
Gross Domestic Product ◽  
Pension Fund ◽  
Social Problems ◽  
The State ◽  
Domestic Product ◽  
Technological Equipment ◽  
Gross Domestic Product Growth ◽  
Development Fund ◽  
Russian Economy ◽  
Competitive Products

The proposed technology of financing enterprises and the Russian economy, harmoniously combining the interests of working citizens, owners and the state, makes it possible, at quite achievable rates of gross domestic product growth (enterprise revenue) by 3.5% per year, to ensure a 46.6% increase in wages of working citizens over 5 years, which will practically end poverty. To increase contributions to the development fund for 5 years by 25%, which the owners of enterprises and the entire workforce are interested in, since this ensures the growth of their incomes and the possibility of constant modernization and updating of technological equipment and the release of new competitive products. Increase in 5 years (despite a gradual decrease to 14.51% of contributions to the Pension Fund RF) the amount of funds received by budgets of all levels by 22%, which will allow the state to solve many social problems.

Market Parameter from Historical Time Series

2019 ◽  
pp. 731-751
Author(s):  
Hans-Peter Deutsch ◽  
Mark W. Beinker
Keyword(s):  
Time Series ◽  
Historical Time

scholarly journals VECTOR AUTOREGRESSIVE APPROACH AFTER FIRST DIFFERENCING: A TIME SERIES ANALYSIS OF INFLATION AND ITS DETERMINANTS IN TANZANIA

2021 ◽  
pp. 43-56
Author(s):  
Rachel R. Cheti ◽  
Bahati Ilembo
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Gross Domestic Product ◽  
Money Supply ◽  
Inflation Rate ◽  
Negative Impact ◽  
Causal Effect ◽  
Domestic Product ◽  
Series Data ◽  
Vector Autoregressive

The objective of the study was to examine the trend of inflation and its key determinants in Tanzania. We used secondary time series data observed annually from January 1970 to 2020 which are inflation rate, GDP, Exchange rate and money supply. The vector autoregressive (VAR) model was employed for modeling. Augmented Dickey-Fuller test (ADF) found that inflation rate, Gross Domestic Product (GDP), exchange rate and Money supply (M3) were initially non-stationary but they became stationary after first differencing so as to proceed with the analysis. Preliminary tests before obtaining vector auto regressive model were carried out before determining the relationship between the variables. Diagnostic test such as serial correlation, heteroscedasticity, stability and normality were also important to evaluate the model assumptions and investigate whether or not there are observations with a large, undue influence on the analysis. We used Granger causality test (GCT) to determine causal- effect relationship between the variables. The results show that, there is a long run relationship between the variables, also the results showed that exchange rate and money supply (M3) both have a positive impact on inflation rate while gross domestic product (GDP) revealed a negative impact on inflation rate. Finally, the forecast of inflation rate for 15 years ahead was performed. The study recommends that the government should pursue both contractionary monetary policy and fiscal policy in order to control inflation in the country.

scholarly journals An analysis of the relationship of inflation and unemployment to the gross domestic product (gdp) in zimbabwe

2014 ◽  
Vol 1 (3) ◽  
pp. 156-162
Author(s):  
Tendai Makoni
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Cointegration Test ◽  
Domestic Product ◽  
Long Run ◽  
Causality Tests ◽  
Granger Causality Tests ◽  
Relationship Of ◽  
The Relationship ◽  
Yearly Data

The time series yearly data for Gross Domestic Product (GDP), inflation and unemployment from 1980 to 2012 was used in the study. First difference of the logged data became stationary as suggested by the time series plots. Johansen Maximum Likelihood Cointegration test indicated a long-run relationship among the variables. Granger Causality tests suggested unidirectional causality between inflation and GDP, implying that GDP is Granger caused by inflation in Zimbabwe. Another unidirectional causality was noted between unemployment and inflation. The causality between unemployment and inflation imply that unemployment do affect GDP indirectly since unemployment influences inflation which in turn positively affect GDP.

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