Estimating Autocorrelation In Slowed Time Lags Using Autoregressive Models

Abstract

Autoregressive models, also known as AR models, are statistical models
that make predictions about future values of a time series by analyzing its
past values. These models employ a linear combination of previous
observations of the series as predictors, with the coefficients determined
through data fitting. One key aspect of autoregressive models is their
ability to consider the temporal relationships among observations. This
allows them to detect patterns and trends in the data that might go
unnoticed by simpler models. In the realm of finance, autoregressive
models find practical applications in modeling and forecasting stock
prices, exchange rates, and other financial time series. By fitting an
autoregressive model to a historical time series of prices or returns,
analysts can estimate the probable future behavior of the series and
leverage this insight for making investment decisions. In my study, I used
Autoregressive models to develop a structural model using advanced
statistical analysis techniques (confirmatory factor analysis) and to
highlight the role and importance of the mediating variable (inflation rate)
in determining economic growth rate, exchange rate, and unemployment
rate in Iraq. There are statistically significant differences between the
effect of the exchange rate on growth and its impact on the official growth
of the Iraqi dinar. The effect of the exchange rate on growth and market
growth depends mainly on the level of demand in the local market and the
availability of currency through sale to the Central Bank of Iraq. These
spreads have increased, especially with regard to the selling price. In
addition to that, there is no specific indicator of the exchange rate of the
Iraqi dinar against foreign currencies, due to the lack of a market for the
foreign market 1 . In summary, autoregressive models are a powerful tool
for estimating slowed time lags and predicting the future behavior of time
series data