Seasonal Adjustment Without Revisions: A Real-Time Approach

Description

Seasonality in economic time series can "obscure" movements of other components in a series that are operationally more important for economic and econometric analyses. In practice, one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course. This book presents a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters, which consists of a simple adaptive procedure to extract the seasonal and the non-seasonal component from an observed series. Once this process is carried out, there will be no need to revise these components at a later stage when new observations become available. The authors describe the main features of CAMPLET, evaluate the outcomes of CAMPLET and X-13ARIMA-SEATS in a controlled simulation framework using a variety of data generating processes, and illustrate CAMPLET and X-13ARIMA-SEATS with three time series: US non-farm payroll employment, operational income of Ahold and real GDP in the Netherlands. Furthermore they show how CAMPLET performs under the COVID-19 crisis, and its attractiveness in dealing with daily data. This book appeals to scholars and students of econometrics and statistics, interested in the application of statistical methods for empirical economic modeling. Preface Contents About the Authors List of Figures List of Tables 1 Introduction 1.1 Seasonality and Seasonal Adjustment 1.2 Revisions 1.3 Aim 1.4 Outline 2 CAMPLET: Seasonal Adjustment Without Revisions 2.1 Introduction 2.2 CAMPLET 2.2.1 Seasonals and Non-seasonals 2.2.2 Seasonal Adjustment in CAMPLET 2.2.3 Initialization 2.2.4 Outliers and Change in Seasonal Pattern 2.2.5 Automatic Parameter Adjustment for Volatile Series 2.2.6 CAMPLET Parameters 2.3 Simulations 2.3.1 Design 2.3.2 X-13ARIMA-SEATS 2.3.3 Quality Measures 2.3.4 Current Vintage Comparison 2.3.5 Quasi-Real-Time Comparison Experiment 2.3.6 Discussion 2.4 Illustrations 2.4.1 U.S. Non-farm Payroll Employment 2.4.2 Ahold 2.4.3 Real GDP in the Netherlands 2.5 Concluding Remarks 3 Seasonal Adjustment of Economic Tendency Survey Data 3.1 Introduction 3.2 Seasonal Adjustment: Census and CAMPLET 3.3 The KOF Barometer 3.4 Empirical Illustration 3.4.1 Comparison of KOF-Census and CAMPLET Seasonally Adjusted Variables 3.4.2 Implications for the KOF Barometer 3.5 Concluding Remarks 4 Residual Seasonality: A Comparison of X13 and CAMPLET 4.1 Introduction 4.2 Measuring Seasonality 4.3 Data 4.4 Seasonality and Residual Seasonality Outcomes 4.5 Concluding Remarks 5 COVID-19 and Seasonal Adjustment 5.1 Introduction 5.2 Seasonal Adjustment Methodology 5.2.1 Seasonal Decomposition 5.2.2 Description of Methods Used in This Chapter 5.2.3 Adjustments Because of the COVID-19 Crisis 5.3 Illustrations 5.3.1 Data and Settings of Seasonal Adjustment Methods 5.3.2 Results 5.3.3 Discussion 5.4 Concluding Remarks 6 Seasonal Adjustment of Daily Data with CAMPLET 6.1 Introduction 6.2 Seasonal Adjustment of Daily Data 6.3 Adjustments in CAMPLET for Daily Data 6.4 Comparisons 6.5 Gas Consumption and Production in the Netherlands 6.5.1 Seasonal Cycles 6.5.2 Gas Consumption and Production, and Mean Temperature 6.6 Concluding Remarks 7 Conclusion Appendix Bibliography