There is a need to use appropriate statistical tools and methods for testing theories of economic, estimating economic relationships, and evaluating and implementing policy. There is a significant difference between econometrics and mathematical statistics, where former deals with problematic non-experimental data such as observational data, where researcher act as a passive collector of data from the real world. Therefore, econometricians generally wrestle with non-experimental data due to monetary, scope and morality constraints.
In general, econometrics takes two forms, where one can estimate the relationship and other one is testing a theory. Therefore, empirical analysis takes both the forms. At phdassistance, we analyze variety of economic data such as cross-sectional data (data collected at one point in time, e.g., class height, national unemployment, household spending during new year – often used in microeconomics) , time series data (e.g., tracks the movement of one variable over time – example of such data includes GDP, stock), pooled cross section data (combination of random sample from different years), and panel (pooled) data.
monthly unemployment data, weekly measures of money supply, daily closing prices of stocks indices
We offer the basic time-series models such as autoregressive (AR), and moving average (MA) models, Box-jenkins approach to time-series modeling, stationary and non-stationary time series