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Modeling with Time Series: Issues and Common Errors

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Abstract

Time series data are widely used in empirical research but it is observed that most often the modeling is done with errors. It is common for researchers to use a given estimation method without considering the stochastic properties of the variable. When time series data are used in estimation without addressing the problem of stochastic innovations, the result may be biased with invalid inferential statistics. Hence, hypothesis test will be unreliable and conclusion misleading. This paper provides simple discussion of potential problems that could arise while modeling with time series. Practical step by step approaches of modeling time series variables at different circumstances is also discussed in the paper. It is concluded that necessary pre estimation tests should always be carried out before choosing the appropriate method.

Suggested Citation

  • A. Abdulhakeem, Kilishi, 2022. "Modeling with Time Series: Issues and Common Errors," Working Papers 24, Department of Economics, University of Ilorin.
    • Handle: RePEc:ris:decilo:0024
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    More about this item

    Keywords

    Time Series; Time Trend; Nonstationary; Modeling;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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