Estimating Autocorrelation In Slowed Time Lags Using Autoregressive Models - Ahmed Al Nuaimi, Waleed (Universitas Islam Tribakti Lirboyo Kediri, 2024)
Autoregressive models, also known as AR models, are statistical modelsthat make predictions about future values of a time series by analyzing itspast values. These models employ a linear combination of previousobservations of the series as predictors, with the coefficients determinedthrough data fitting. One key aspect of autoregressive models is theirability to consider the temporal relationships among observations. Thisallows them to detect patterns and trends in the data that might gounnoticed by simpler models. In the realm of finance, autoregressivemodels find practical applications in modeling and forecasting stockprices, exchange rates, and other financial time series. By fitting anautoregressive model to a historical time series of prices or returns,analysts can estimate the probable future behavior of the series andleverage this insight for making investment decisions. In my study, I usedAutoregressive models to develop a structural model using advancedstatistical analysis techniques (confirmatory factor analysis) and tohighlight the role and importance of the mediating variable (inflation rate)in determining economic growth rate, exchange rate, and unemploymentrate in Iraq. There are statistically significant differences between theeffect of the exchange rate on growth and its impact on the official growthof the Iraqi dinar. The effect of the exchange rate on growth and marketgrowth depends mainly on the level of demand in the local market and theavailability of currency through sale to the Central Bank of Iraq. Thesespreads have increased, especially with regard to the selling price. Inaddition to that, there is no specific indicator of the exchange rate of theIraqi dinar against foreign currencies, due to the lack of a market for theforeign market 1 . In summary, autoregressive models are a powerful toolfor estimating slowed time lags and predicting the future behavior of timeseries data