47 verified PDFs
Macroeconomists build time-series models for testing economic theories, for fore- casting, and for policy analysis. Such models are constructed and applied by.
This paper develops a vector autoregression (VAR) for time series which are observed at mixed frequencies - quarterly and monthly. The model is cast in state- ...
by E Keogh · Cited by 940 — Tasks addressed by the researchers include segmentation, indexing, clustering, classification, anomaly detection, rule discovery, and summarization. Of the ...
by JH Stock · Cited by 1107 — The presence of variable trends in time series data can lead one to draw mistaken inferences using conventional econometric techniques. How can these techniques ...
Abstract. We address the problem of predicting a time series using the ARMA (autoregressive moving average) model, under minimal assumptions on the noise ...
The purpose of this paper is to investigate how real-time data affects out-of-sample exchange rate forecasts of PPP and the Taylor rule models at short and long ...
by BE Hansen · Cited by 13 — The field of econometrics largely started with time series analysis because many early datasets were time- series macroeconomic data.
Findings: Forecast accuracy can be improved by using one of 15 relatively simple evidence-based forecasting methods. One of those methods, knowledge models, ...
In this paper we provide a general framework for forecasting time series under structural breaks that is capable of handling the different scenarios that arise ...
by EP Howrey · 1980 · Cited by 36 — This paper is organized as follows. In the next section the general ap- proaches of classical econometrics and time sries analysis are contrasted. This ...
To fill the gap, in this paper, we systemati-cally review different data-centric methods in time series analysis, covering a wide range of research topics. Based on the time-series data characteristics at sample, feature, and period, we propose a taxon-omy for the reviewed data selection methods.
Time-series analysis is used when observations are made repeatedly over 50 or more time periods. Sometimes the observations are from a single case, ...
Time Series and Computational Methods in Communication Research A time series is a way of indexing or graphing data in temporal order, creating a sequential pattern of the level of a given variable over time.
The aims of time series analysis are to describe and summarise time series data, fit low-dimensional models, and make forecasts. We write our real-valued series ...
We estimate by OLS the parameters of various models. Performances chosen ARIMA model. (1,1,1) are verified on the basis of classical statistical tests and.
Abstract. The International Price Program of the Bureau of Labor Statistics estimates monthly indices on the changes in import and export prices for ...
Random variables that are measured over time are often called “time series.” We define the simplest kind of time series, “white noise,” then we discuss how ...
In this paper, I will discuss a number of Stata's capabilities in the area of time series modeling, including data management and graphics. I will focus on a ...
The theory and applications of these procedures to a variety of econometric modeling and forecast- ing problems as well as Bayesian and non-Bayesian testing, ...
Time series or longitudinal data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time series properties of their data as a ...
