Economic and Business Forecasting E-Book

John E. Silvia

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch

Seitenzahl: 610

Veröffentlichungsjahr: 2014

Beschreibung

Discover the secrets to applying simple econometric techniques to improve forecasting Equipping analysts, practitioners, and graduate students with a statistical framework to make effective decisions based on the application of simple economic and statistical methods, Economic and Business Forecasting offers a comprehensive and practical approach to quantifying and accurate forecasting of key variables. Using simple econometric techniques, author John E. Silvia focuses on a select set of major economic and financial variables, revealing how to optimally use statistical software as a template to apply to your own variables of interest. * Presents the economic and financial variables that offer unique insights into economic performance * Highlights the econometric techniques that can be used to characterize variables * Explores the application of SAS software, complete with simple explanations of SAS-code and output * Identifies key econometric issues with practical solutions to those problems Presenting the "ten commandments" for economic and business forecasting, this book provides you with a practical forecasting framework you can use for important everyday business applications.

Leseprobe

Contents

Preface

Acknowledgments

Chapter 1: Creating Harmony Out of Noisy Data

Effective Decision Making: Characterize the Data

Chapter 2: First, Understand the Data

Growth: How Is the Economy Doing Overall?

Personal Consumption

Gross Private Domestic Investment

Government Purchases

Net Exports of Goods and Services

Real Final Sales and Gross Domestic Purchases

The Labor Market: Always a Core Issue

Establishment Survey

Data Revision: A Special Consideration

The Household Survey

Marrying the Labor Market Indicators Together

Jobless Claims

Inflation

Consumer Price Index: A Society’s Inflation Benchmark

Producer Price Index

Personal Consumption Expenditure Deflator: The Inflation Benchmark for Monetary Policy

Interest Rates: Price of Credit

The Dollar and Exchange Rates: The United States in a Global Economy

Corporate Profits

Summary

Chapter 3: Financial Ratios

Profitability Ratios

Summary

Chapter 4: Characterizing a Time Series

Why Characterize a Time Series?

How to Characterize a Time Series

Application: Judging Economic Volatility

Summary

Chapter 5: Characterizing a Relationship between Time Series

Important Test Statistics in Identifying Statistically Significant Relationships

Simple Econometric Techniques to Determine a Statistical Relationship

Advanced Econometric Techniques to Determine a Statistical Relationship

Summary

Additional Reading

Chapter 6: Characterizing a Time Series Using SAS Software

Tips for SAS Users

The DATA Step

The PROC Step

Summary

Chapter 7: Testing for a Unit Root and Structural Break Using SAS Software

Testing a Unit Root in a Time Series: A Case Study of the U.S. CPI

Identifying a Structural Change in a Time Series

The Application of the HP Filter

Application: Benchmarking the Housing Bust, Bear Stearns, and Lehman Brothers

Summary

Chapter 8: Characterizing a Relationship Using SAS

Useful Tips for an Applied Time Series Analysis

Converting a Dataset from One Frequency to Another

Application: Did the Great Recession Alter Credit Benchmarks?

Summary

Chapter 9: The 10 Commandments of Applied Time Series Forecasting for Business and Economics

Commandment 1: Know What You Are Forecasting

Commandment 2: Understand the Purpose of Forecasting

Commandment 3: Acknowledge the Cost of the Forecast Error

Commandment 4: Rationalize the Forecast Horizon

Commandment 5: Understand the Choice of Variables

Commandment 6: Rationalize the Forecasting Model Used

Commandment 7: Know How to Present the Results

Commandment 8: Know How to Decipher the Forecast Results

Commandment 9: Understand the Importance of Recursive Methods

Commandment 10: Understand Forecasting Models Evolve over Time

Summary

Chapter 10: A Single-Equation Approach to Model-Based Forecasting

The Unconditional (Atheoretical) Approach

The Conditional (Theoretical) Approach

Recession Forecast Using a Probit Model

Summary

Chapter 11: A Multiple-Equations Approach to Model-Based Forecasting

The Importance of the Real-Time Short-Term Forecasting

The Individual Forecast versus Consensus Forecast: Is There an Advantage?

The Econometrics of Real-Time Short-Term Forecasting: The BVAR Approach

Forecasting in Real Time: Issues Related to the Data and the Model Selection

Case Study: WFC versus Bloomberg

Summary

Appendix 11A: List of Variables

Chapter 12: A Multiple-Equations Approach to Long-Term Forecasting

The Unconditional Long-Term Forecasting: The BVAR Model

The BVAR Model with Housing Starts

The Model without Oil Price Shock

The Model with Oil Price Shock

Summary

Chapter 13: The Risks of Model-Based Forecasting: Modeling, Assessing, and Remodeling

Risks to Short-Term Forecasting: There Is No Magic Bullet

Risks of Long-Term Forecasting: Black Swan versus a Group of Black Swans

Model-Based Forecasting and the Great Recession/Financial Crisis: Worst-Case Scenario versus Panic

Summary

Chapter 14: Putting the Analysis to Work in the Twenty-First-Century Economy

Benchmarking Economic Growth

Industrial Production: Another Case of Stationary Behavior

Employment: Jobs in the Twenty-First Century

Inflation

Interest Rates

Imbalances between Bond Yields and Equity Earnings

A Note of Caution on Patterns of Interest Rates

Business Credit: Patterns Reminiscent of Cyclical Recovery

Profits

Financial Market Volatility: Assessing Risk

Dollar

Economic Policy: Impact of Fiscal Policy and the Evolution of the U.S. Economy

The Long-Term Deficit Bias and Its Economic Implications

Summary

Appendix: Useful References for SAS Users

About the Authors

Index

End User License Agreement

List of Illustrations

FIGURE 1.1 Real GDP (Year-over-Year Percentage Change)

FIGURE 1.2 Total Industrial Production Growth (Output Growth by Volume, Not Revenue)

FIGURE 1.3 U.S. Consumer Price Change

FIGURE 1.4 Corporate Profits Growth

FIGURE 1.5 Nonfarm Productivity

FIGURE 1.6 Nonfarm Productivity Change

FIGURE 1.7 Federal Budget Surplus or Deficit

FIGURE 1.8 Yield Curve Spread

FIGURE 1.9 AA Five-Year Spread

FIGURE 1.10 Nonfarm Employment Growth (Year-over-Year Percentage Change)

FIGURE 1.11 Decomposing the 10-Year Treasury (Using the HP Filter)

FIGURE 1.12 Real GDP (Year-over-Year Percentage Change)

FIGURE 1.13 U.S. Consumer Price Index (Year-over-Year Percentage Change)

FIGURE 1.14 GDP versus. Total Domestic Nonfinancial Debt (Year-over-Year Percentage Change)

FIGURE 1.15 M2 Money Supply Growth versus CPI Growth (Year-over-Year Percentage Change)

FIGURE 1.16 Federal Government Outlays and Nominal GDP (Year-over-Year Percentage Change, 12-Month Moving Average)

FIGURE 1.17 Trade Weighted Dollar (March 1973 = 100)

FIGURE 1.18 Ratio: Debt to Equity (Nonfarm Nonfinancial Corporation)

FIGURE 1.19 Ratio of the AA Corporate Yield to the 5-Year Treasury Yield

FIGURE 1.20 Ratio of the 10-Year Treasury Yield to the 2-Year Treasury Yield

FIGURE 2.1 Unemployment Rate (Seasonally Adjusted)

FIGURE 2.2 Labor Force Participation Rate (16 Years and Over, Seasonally Adjusted)

FIGURE 2.3 Real GDP, Volume Growth (Year-over-Year Percentage Change)

FIGURE 2.4 GDP Share by Major Components

FIGURE 2.5 Real Personal Consumption Expenditures (Year-over-Year Percentage Change, Durables 4Q Moving Average)

FIGURE 2.6 Real Private Domestic Investment

FIGURE 2.7 Real Inventory Change versus Business Investment (Year-over-Year Percentage Change)

FIGURE 2.8 Business Inventories: Inventory-to-Sales Ratio

FIGURE 2.9 Real Estate and Local Government Expenditure

FIGURE 2.10 Imports and Exports as Percentage of GDP

FIGURE 2.11 Real Final Sales to Domestic Purchasers (Year-over-Year Percentage Change)

FIGURE 2.12 Nonfarm Employment Change (in Thousands)

FIGURE 2.13 Manufacturing Employment Growth

FIGURE 2.14 Temporary Help Employment (Year-over-Year Percentage Change)

FIGURE 2.15 Construction Employment (Year-over-Year Percentage Change)

FIGURE 2.16 Government Job Growth (Year-over-Year Percentage Change)

FIGURE 2.17 Income Proxy (Three-Month Annualized Rate of Three-Month Moving Average)

FIGURE 2.18 Household and Establishment Employment (Year-over-Year Percentage Change)

FIGURE 2.19 Unemployment Rate (Seasonally Adjusted)

FIGURE 2.20 Unemployment Measures (Seasonally Adjusted)

FIGURE 2.21 Labor Force Participation Rate: Males versus Females, (Seasonally Adjusted)

FIGURE 2.22 Labor Force Growth (Year-over-Year Percentage Change)

FIGURE 2.23 Labor Force Participation Rate: 16 Years or Over (Seasonally Adjusted)

FIGURE 2.24 Labor Force Participation Rate (Seasonally Adjusted)

FIGURE 2.25 Initial Claims for Unemployment (Seasonally Adjusted, in Thousands)

FIGURE 2.26 CPI Disinflation (Year-over-Year Percentage Change)

FIGURE 2.27 Consumer Price Index Weights, 2009–2010

FIGURE 2.28 CPI versus Core CPI (Year-over-Year Percentage Change)

FIGURE 2.29 Food and Energy Spending (Share of Total Consumption)

FIGURE 2.30 Producer Prices by Stage of Processing (Year-over-Year Percentage Change)

FIGURE 2.31 Consumer Price Index versus Producer Price Index (Year-over-Year Percentage Change)

FIGURE 2.32 PCE Market Deflators (Year-over-Year Percentage Change)

FIGURE 2.33 Yield Curve (U.S. Treasuries, Active Issues)

FIGURE 2.34 TED Spread (Three-Month LIBOR Minus Treasury Bill Yield in Basis Points)

FIGURE 2.35 Trade-Weighted Dollar (January 1997 = 100)

FIGURE 2.36 Brazilian Exchange Rate (USD per BRL)

FIGURE 2.37 Corporate Profits Growth (Year-over-Year Percentage Change)

FIGURE 2.38 Profits as a Percentage of GDP (Pretax Profits Divided by Product of Nonfinancial Corporate Businesses)

FIGURE 3.1 Return on Equity Ratio: HP Filter

FIGURE 3.2 Return on Assets Ratio: HP Filter

FIGURE 3.3 Corporate Profits: HP Filter

FIGURE 3.4 Current Assets to Liabilities: HP Filter

FIGURE 3.5 Debt-to-Equity Ratio: HP Filter

FIGURE 3.6 Price-to-Earnings Ratio: HP Filter

FIGURE 4.1 Productivity—Total Nonfarm (Year-over-Year Percentage Change, Three-Year Moving Average)

FIGURE 4.2 10-Year Treasury Yield

FIGURE 4.3 U-Shaped Trend Between 2008 and 2012: Nonfarm Employment (Millions)

FIGURE 4.4 Inverted U-Shaped Trend Between 2003 and 2009: Industrial Production (2007 = 100, Seasonally Adjusted)

FIGURE 4.5 Exponential Trend, the S&P 500 Index

FIGURE 4.6 Log of the S&P 500 Index

FIGURE 4.7 Real GDP (Year-over-Year Percentage Change)

FIGURE 4.8 Nonfarm Employment Growth (Year-over-Year Percentage Change)

FIGURE 4.9 S&P Case-Shiller Home Price Index (not seasonally adjusted)

FIGURE 4.10 Real GDP

FIGURE 5.1 M2 Money Supply Growth versus PCE Deflator Growth (Year-over-Year Percentage Change)

FIGURE 5.2 S&P 500 Index

FIGURE 6.1 Charlotte Unemployment Rate, SA and NSA

FIGURE 7.1 S&P Case-Schiller Composite-10 Home Price Index

FIGURE 7.2 Decomposing Corporate Profits Using the HP Filter

FIGURE 7.3 Cyclical Component of the Profits

FIGURE 7.4 S&P/Case-Shiller Home Price Index

FIGURE 7.5 TED Spread

FIGURE 7.6 LIBOR–OIS Fed Funds Three-Month Spread

FIGURE 7.7 VIX Index

FIGURE 8.1 Employment Cycles: Percentage Change from Cycle Peak

FIGURE 8.2 M2 Money Supply Growth versus CPI Growth (Year-over-Year Percentage Change)

FIGURE 8.3 The S&P 500 Index (YoY)

FIGURE 8.4 Delinquecy Rates

FIGURE 8.5 Delinquency and Charge-offs

FIGURE 8.6 Charge-Off Rates

FIGURE 8.7 Banks’ Willingness to Make Loans

FIGURE 8.8 Federal Reserve Balance Sheet

FIGURE 8.9 Loan-to-Deposit Ratio

FIGURE 10.1 Number of Initial Claims Filed for Unemployment Insurance

FIGURE 10.2 Initial Claims Forecast and 95 Percent Confidence Interval

FIGURE 10.3 Base-Case Scenario: Forecast for Federal Funds Rate Conditioned on CPI and GDP Growth

FIGURE 10.4 Strong GDP Growth Rate Scenario: Forecast for Federal Funds Rate Conditioned on CPI and GDP Growth

FIGURE 10.5 Recession Probability Using Monthly Data

FIGURE 10.6 Recession Probability: Quarterly Average

FIGURE 11.1 Nonfarm Payrolls: Actual, Forecast, and 95 Percent Confidence Interval

FIGURE 12.1 GDP Forecasts Based on the Eight-Variable BVAR Model

FIGURE 12.2 GDP Forecasts Based on the Modified Model with Housing Starts

FIGURE 12.3 GDP Forecasts Based on the Original and Modified BVAR Model

FIGURE 12.4 GDP Forecasts Conditioned on the Average WTI Prices: Without an Oil Price Shock Scenario

FIGURE 12.5 GDP Forecasts Conditioned on Higher WTI Prices: The Oil Price Shock Scenario

FIGURE 13.1 Industrial Production, Total Index (SIC) (Units 2007=100)

FIGURE 13.2 A Group of Black Swans: A Shift in the Distribution

FIGURE 13.3 The Unemployment Rate

FIGURE 13.4 The S&P/Case-Shiller HPI

FIGURE 14.1 Real GDP Growth: Compound Annual Growth Rate

FIGURE 14.2 Industrial Production: Compound Annual Growth Rate

FIGURE 14.3 Real GDP: Compound Annual Growth Rate

FIGURE 14.4 Existing Home Sales

FIGURE 14.5 Light Vehicle Sales

FIGURE 14.6 Unemployment Rate: U-3

FIGURE 14.7 Nonfarm Payrolls

FIGURE 14.8 Nonfarm Employment Change

FIGURE 14.9 Beveridge Curve in Employment Recoveries

FIGURE 14.10 Employment–Population Ratio

FIGURE 14.11 Labor Force Participation Rate

FIGURE 14.12 TIPS Inflation Compensation

FIGURE 14.13 Inflation and the Real Yield

FIGURE 14.14 U.S. Consumer Price Index

FIGURE 14.15 10-Year Implied Inflation Expectations

FIGURE 14.16 Gold Price

FIGURE 14.17 Gold Spot Price and Price Inflation

FIGURE 14.18 M2 Money Supply Velocity

FIGURE 14.19 Inflation and the Real Yield

FIGURE 14.20 Baa Corporate Yield over S&P Index Earnings

FIGURE 14.21 Investment-Grade CDS Index

FIGURE 14.22 Investment-Grade Corporate Issuance

FIGURE 14.23 High-Yield Corporate Issuance

FIGURE 14.24 Aaa and Baa Corporate Bond Spreads

FIGURE 14.25 2-Year Treasury Yield

FIGURE 14.26 10-Year Treasury Yield

FIGURE 14.27 Home Price Growth versus Mortgage Rates

FIGURE 14.28 Commercial and Industrial Loans by Bank Type

FIGURE 14.29 Corporate Profits

FIGURE 14.30 U.S. Dollar Index

FIGURE 14.31 U.S. Budget Gap

FIGURE 14.32 U.S. Federal Government Mandatory Outlays

FIGURE 14.33 U.S. Debt Held by the Public

FIGURE 14.34 Prime Employment–Population Ratio

FIGURE 14.35 Real GDP Growth Estimates

FIGURE 14.36 U.S. Budget Gap

FIGURE 14.37 Top Holders of U.S. Treasuries

List of Tables

TABLE 1.1 Real Gross Domestic Product (Year-over-Year Percentage Change)

TABLE 1.2 Autocorrelation Functions for Nonfarm Payrolls

TABLE 1.3 Partial Autocorrelation Functions for Nonfarm Payrolls

TABLE 1.4 CPI, Unit Root Test Results

TABLE 4.1 Three Eras of U.S. Productivity

TABLE 4.2 Employment Growth Autocorrelations (ACFs)

TABLE 4.3 Employment Growth Partial Autocorrelations (PACFs)

TABLE 4.4 Real GDP (Annualized Rate)

TABLE 4.5 Real Final Sales (Year-over-Year Percentage Change)

TABLE 4.6 Real Personal Consumption (Year-over-Year Percentage Change)

TABLE 4.7 Corporate Profits (Year-over-Year Percentage Change)

TABLE 4.8 Employment Growth (Year-over-Year Percentage Change)

TABLE 4.9 Unemployment Rate

TABLE 4.10 S&P 500 (Year-over-Year Percentage Change)

TABLE 4.11 10-Year Treasury Yield

TABLE 6.1 SAS Output of PROC MEANS Using Single Variable (Real GDP)

TABLE 6.2 SAS Output of PROC MEANS Using Many Variables

TABLE 6.3 Stability Ratio

TABLE 6.4 Mean, Standard Deviation, Stability Ratio, and Business Cycles∗

TABLE 6.5 SAS Output based on the PROC AUTOREG: A Linear Time Trend

TABLE 6.6 SAS Output Based on the PROC AUTOREG: A Nonlinear Time Trend

TABLE 6.7A SAS Output Based on the PROC AUTOREG: A Log-Linear Time Trend

TABLE 6.7B SAS Output Based on the PROC MODEL: A Log-Linear Time Trend

TABLE 6.8 The SIC/AIC of All Three Models

TABLE 6.9A SAS Output Based on the PROC ARIMA

TABLE 6.9B SAS Output Based on PROC ARIMA

TABLE 6.9C SAS Output Based on PROC ARIMA

TABLE 6.10 SAS Output Based on PROC ARIMA: The SCAN Method

TABLE 6.11 Tentative Order Selection Tests

TABLE 7.1 SAS Output Based on the ADF Unit Root Test

TABLE 7.2 SAS Output Based on the PP Unit Root Test

TABLE 7.3 SAS Output Based on the KPSS Unit Root Test

TABLE 7.4 Testing for a Structural Break: The Dummy Variable Approach

TABLE 7.5 Testing for a Structural Break: The Chow Test

TABLE 7.6A Identifying ARIMA (p, d, q) for the HPI

TABLE 7.6B Testing for a Structural Break: The State-Space Approach

TABLE 7.7 Identifying a Structural Break Using the State-Space Approach

TABLE 8.1 The Correlation Analysis Using Level Form of the Variables

TABLE 8.2 The Correlation Analysis Using Difference Form of the Variables

TABLE 8.3 The Regression Analysis Using the Level Form of the M2 and CPI

TABLE 8.4 Regression Analysis Using Difference Form of the Variables

TABLE 8.5 Autocorrelation Test: The Durbin ‘h’ Test

TABLE 8.6 Autocorrelation Test: The LM Test

TABLE 8.7 Finding the Appropriate Lag-Order Using the LM Test

TABLE 8.8 Results Based on the Engle-Granger Cointegration Test

TABLE 8.9 Results Based on Step 2 of ECM

TABLE 8.10 The Johansen Cointegration Approach: The Trace Test

TABLE 8.11 Johansen Cointegration Approach: Maximum Test

TABLE 8.12 Results Based on VECM: Case 2

TABLE 8.13 The Granger Causality Test Results, Using Difference Form of the Variables

TABLE 8.14 Testing an ARCH Effect for the S&P 500 Index

TABLE 8.15 Testing Employment–S&P500 Relationship Using ARCH/GARCH

TABLE 8.16 Testing Delinquency Rates behavior over Two Different Time Periods

TABLE 8.17 Identifying a Time Trend in Charge-off Rates

TABLE 8.18 Identifying Structural Break(s) in the Loan-to-Ratio

TABLE 10.1A ADF Test Results Using Level Form of Initial Claims

TABLE 10.1B ADF Test Results Using Difference Form of Initial Claims

TABLE 10.2 SCAN Results Finding Tentative Order of ARMA (p, q)

TABLE 10.3 Autocorrelation Check of Estimated Residuals

TABLE 10.4 PROC MODEL Results for the Estimated Taylor Rule

TABLE 10.5 Base-Case Scenario: Forecast for Federal Funds Rate Conditioned on CPI and GDP Growth

TABLE 10.6 Strong GDP Growth Rate Scenario: Forecast for Federal Funds Rate Conditioned on CPI and GDP Growth

TABLE 11.1 Net Change in the Nonfarm Payrolls: BVAR VS. Bloomberg Consensus

TABLE 11.2 Summary of the Results: Net Change in Nonfarm Payrolls

TABLE 11.3 Summary of the Results for 20 Variables

TABLE 11.4 Summary of the Results

TABLE 11.5 Summary of the Results

TABLE 11.A Forecast Evaluation 2010

TABLE 12.1 Forecasts Based on the Eight-Variable BVAR Model

TABLE 12.2 Forecasts Based on the Modified Model with Housing Starts

TABLE 12.3 Forecasts Conditioned on the Average WTI Price Model: Without an Oil Price Shock Scenario

TABLE 12.4 Forecasts Based on the Higher WTI Price Model: The Oil Price Shock Scenario

TABLE 14.1 GDP ADF Results Exhibit Stationarity

TABLE 14.2 ADF Results Indicate Stationarity for Industrial Production(2)

TABLE 14.3 Stationarity For the U-3 Measure of Unemployment: The ADF Results

TABLE 14.4 Employment Growth as a Stationary Series: The ADF Results

TABLE 14.5 Business Cycles and Consumer Inflation

TABLE 14.6 Evidence of Nonstationarity of the Two-Year Yield: ADF Results

TABLE 14.7 Ten-Year Treasury: The ADF Results

TABLE 14.8 Stationarity for Corporate Profits: ADF Results

TABLE 14.9 Corporate Profits (Year over Year) S.D.

TABLE 14.10 S&P 500 (Year over Year)

TABLE 14.11 10-Year Treasury

TABLE 14.12 Nonstationary Behavior of the Dollar: ADF Results

TABLE 14.13 Business Cycles and the U.S. Dollar

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Guide

Cover

Table of Contents

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Economic and Business Forecasting

Analyzing and Interpreting Econometric Results

John Silvia

Azhar Iqbal

Kaylyn Swankoski

Sarah Watt

Sam Bullard

Cover image: © Ekspansio/iStockphoto, elly99/iStockphoto

Cover design: Andrew Liefer

Copyright © 2014 by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

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Library of Congress Cataloging-in-Publication Data:

Silvia, John.

Economic and business forecasting : analyzing and interpreting econometric results / John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, Sam Bullard.

pages cm. (Wiley & SAS business series)

ISBN 978-1-118-49709-8 (cloth); ISBN 978-1-118-56980-1 (ebk); ISBN 978-1-118-56954-2 (ebk)

1. Economic forecasting. 2. Business forecasting. 3. Decision-making. 4. Econometrics. I. Title.

HB3730.S484 2014

330.01'5195—dc23

2013039764

To Tiffani Kaliko, Penny and Sherman

Shahkora and Mohammad Iqbal, Nargis, Saeeda, Shahid and Noreen

And to the family and friends who remain our wellsprings of inspiration

If a man will begin with certainties, he shall end in doubts, but if he will content to begin with doubts, he shall end in certainties.

—Francis Bacon, The Advancement of Learning, 1605

Preface

Due to the Great Recession (2007–2009) and the accompanying financial crisis, the premium on effective economic analysis, especially the identification of time series and then accurate forecasting of economic and financial variables, has significantly increased. Our approach provides a comprehensive yet practical process to quantify and accurately forecast key economic and financial variables. Therefore, the timing of this book is appropriate in a post-2008 world, where the behavior of traditional economic relationships must be reexamined since many appear out of character with the past. The value proposition is clear: The framework and techniques advanced here are the techniques we use as practitioners. These techniques will help decision makers identify and characterize the patterns of behavior in key economic series to better forecast these essential economic series and their relationships to other economic series.

This book is for the broad audience of practitioners as well as undergraduate and graduate students with an applied economics focus. This book introduces statistical techniques that can help practitioners characterize the behavior of economic relationships. Chapters 1 to 3 provide a review of basic economic and financial fundamentals that decision makers in both the private and public sectors need to know. Our belief is that before an analyst attempts any statistical analysis, there should be a clear understanding of the data under study. Chapter 4 provides the tools that an analyst will employ to effectively characterize an economic series. One relationship of interest is the ability of leading indicators to predict the pattern of the business cycle, particularly the onset of a recession. Another way to characterize economic relationships is to reflect on the current trend of any economic series of interest relative to the average behavior over prior cycles. In a third approach, we may be interested in identifying the possibility of a structural change in an economic time series to test if the past history of a variable would be different over time.

Different economic and financial variables exhibit differential behavior over the business cycle and over time. In this book we focus on a select set of major economic and financial variables, such as economic growth, final sales, employment, inflation, interest rates, corporate profits, financial ratios, and the exchange value of the dollar.

Our analysis then extends the text into the relationships between different time series. This analysis begins with Chapter 5, and then in Chapters 6 and 7 we take a look at the SAS® software employed in our analysis. We also examine these variables’ patterns over the business cycle, with an emphasis on their recent history, using econometric techniques and the statistical software SAS as a template for the reader to apply to variables of interest. These variables form the core of an effective decision-making process in both the private and public sectors. Chapter 8 provides techniques that an analyst can employ and contains numerous examples of our techniques in action.

Our approach has several advantages. First, effective decision making involves an analysis of the behavior of select economic and financial variables. By choosing a small set of economic factors, we provide a template for decision making that can be easily applicable to a broader set of variables for future study in many economic fields. Our focus is on the importance of a limited, but central, set of select economic and financial variables that provide special insights into economic performance, along with the empirical evidence of their vital role to the economy and financial markets.

Second, using a small set of simple data descriptors and econometric techniques to characterize and describe the behavior of economic variables provides value in a number of contexts. We can examine the behavior of any particular economic series in numerous ways so that the analysis is less subject to personal beliefs and biases. This helps overcome the confirmation bias of many decision makers who search for the results they want to see from any analysis. Many analysts may search for the comfortable, familiar historical statistical relationships in a post-2008 era when, in fact, many of those relationships have vanished.

Third, our detailed discussion about SAS and its applications creates a valuable starting point for researchers. We provide a practical forecasting framework for important everyday applications. Finally, our work discusses SAS results and identifies econometric issues and solutions that are of interest to addressing a number of economic and business issues. One outgrowth of our experience with many of these issues is reviewed in Chapter 9, where we focus on our 10 commandments of applied time series forecasting. Chapters 10 and 11 build on these commandments with a focus on single equations in Chapter 10 and multiple equations in Chapter 11.

The net result is the application of econometrics in a way that contributes to effective decision making in both the private and public sectors. In Chapter 12 we focus on model-based forecasting applied to make long-term forecasts for the next five to 10 years, which reflects the reality of determining the real sustainability of projects and their profitability overtime. Chapter 13 then highlights the risks and challenges of such forecasting. Finally in Chapter 14 we illustrate some of the lessons we have learned in recent years as we identify and understand the changes that are ongoing in the twenty-first-century economy. As an additional resource, there is a test bank to accompany this text.

This book is dedicated first to young professional economists and aspiring students who wish to provide a thoughtful statistical basis for better decision making in their careers, whether it is in the public or the private sector. This book is also aimed to serve professional analysts who wish to provide statistical support for effective decision making. This work reflects the years of experience of the authors whose work contains a focus on simple yet practical techniques needed for efficient decision making without extensive theoretical and mathematical refinements that are ancillary to effective decision making. That we leave for authors with the luxury of time and tenure. The techniques in the text are being used in our work every day. They have brought us numerous forecasting awards and published papers that reflect the practical undertakings required of young professionals who wish to add value to the decision-making process in their organizations.

Acknowledgments

We would like to thank all the people who have supported us through the writing and publication of this book. Special thanks to Larry Rothstein and Zachary Griffiths, for without their help this book would not have been possible. We also wish to express our gratitude for the many people at Wells Fargo who have supported this project, including Diane Schumaker-Krieg and John Shrewsberry, as well as the technical support staff at Wells Fargo. Thank you Robert Crow, editor of Business Economics, and the referees of that journal as well as the referees of articles that have appeared in other journals; you have improved the quality of our research over the years. We are grateful for the instructors and students who have come into our lives and taught and inspired us (Nuzhat Ahmad, M. S. Butt, Kajal Lahiri, Asad Zaman, Adil Siddique, Ambreen Fatima, Hasan N. Saleem, Jon Schuller, and Anika Khan).

Chapter 1Creating Harmony Out of Noisy Data

By the spring of 2012, the economic performance of the United States was operating at a much different pace from what many analysts had expected. Decision makers in both private and public sectors faced a set of mixed and unclear economic and financial indicators that offered a confused picture of the state of the economic recovery, the pace of that recovery, and the character of the structural challenges facing the economy.

Three major trends characterized the confusion. First, top-line economic growth had been unusually low and uneven relative to past economic recoveries since World War II. During the recovery, the economy accelerated after an initial stimulus but then lost momentum as the stimulus generated no follow-on growth. Decision makers had the difficult challenge of identifying what the true trend in the economy was and what the cycle around that trend was. Had trend economic growth downshifted in the United States?

Second, job growth had become the number one political issue. But the lack of job growth appeared out of line with traditional economic models on a cyclical basis. Further, weak job growth intimated a sharp structural break in both private and public sector decision makers’ preconceived understanding of the relationship between employment and population growth. Had there been a structural break between employment and population growth, and/or between employment and output growth? Why have exceptionally low mortgage interest rates not spurred a pickup in housing, as in prior recoveries? Had this relationship experienced a structural break as well?

Third, corporate profits, business equipment spending, and industrial production had improved in this cycle in a way reminiscent of prior recoveries despite the overall perception that the economic recovery had been subpar. How can we identify economic series that appear to be behaving in typical cyclical fashion compared to those that are not?

In this book, we test whether certain series, such as output, employment, profits, and interest rates, exhibit a steady pace of growth over time, or if that pace has drifted. In statistical terms, is the series stationary or not? If not, then oft-used statistical tools cannot be employed to evaluate the behavior of an economic series without introducing statistical bias.

To address these issues effectively, we examine many economic and business series and pursue alternative statistical approaches to make effective decisions based on the application of simple economic and statistical methods. Our work here is in contrast to two common approaches: econometric-only approaches or economic theory-only approaches. Our work returns to an earlier tradition of applied research rather than mathematical elegance, which is an alternative to econometrics that uses all technique with little to no real-world application or all-theory approaches with no technique and only hypotheses about the real world.

EFFECTIVE DECISION MAKING: CHARACTERIZE THE DATA

The first task for many analysts is to characterize the behavior of a particular time series. For example, is there a cyclical component to the data? Many economic data series show some cyclicality, but, alternatively, some are driven more by secular changes in our economy—for example, the labor force participation rate trended steadily higher between the early 1960s and late 1990s as women joined the workforce. Yet often a time series, such as employment, is influenced by both cyclical and secular factors, where the cyclical element may change the pace but not derail longer-term secular shifts in the economy.

If a time series does display a cyclical component, how does it behave as we move through the business cycle? Does the data in the time series decline when the economy is in a recession, or is it countercyclical and increase during a recession, such as the saving rate for households? How distinguishable are turning points in the series? If the series is volatile on a period-to-period basis, a large move in one direction or another may not be enough to signify a turning point, but instead care must be taken with a few recent data points in order to smooth out any volatility and distinguish the true trend. Moreover, do turning points in the time series lead or lag those of other series? Is the time series linear or nonlinear over the period of study?

Part IA: Identifying Trend in a Time Series: GDP and Public Deficits

Throughout the recovery from the Great Recession of 2007 to 2009, the pace of economic growth has been below par, and public sector deficits have persisted. This has led to a greater problem of public debt than many policy makers anticipated when the recovery began. Today, perceptions of the effectiveness of fiscal policy actions and the competitiveness of the U.S. economy have been brought into question. Both are critically dependent on the estimates of the underlying trend in essential economic variables like growth, inflation, interest rates, corporate profits, and the dollar exchange rate as well as other financial variables. For example, one key issue since the recession of 2007 to 2009 has been to identify the trend pace of economic growth, which, in turn, reflects the influence of underlying economic forces, such as productivity growth and labor force participation. Identifying the trend of these series helps to characterize the pattern of sustainable federal, state, and local revenues that will make for better budgeting in government and help guide policy makers over time.

The question is: What is the trend pace of economic growth, and has that pace downshifted in the United States over recent years? This issue is critical at both federal and state levels of government as well as for the strategic vision of private sector firms when they estimate their top-line revenue growth. Trend growth in the United States is a primary driver of tax revenues and thereby influences the outlook for budget deficits—a key focus of policy today. The ability of federal and state policy makers to balance their budgets depends critically on the pace of economic growth. Trend growth reflects the underlying influence of productivity and labor force participation rates at the national level.

But unfortunately, many decision makers suffer from an anchoring bias.1 They base decisions on estimates anchored on historical growth rates without consideration that the model of economic growth they are using may have been altered. Nor do they consider that the potential growth of the economy, and therefore federal revenues, has downshifted compared to past estimates.

It is also important to distinguish whether the pace of economic growth, for example, can be described as a linear trend or as a nonlinear trend. If it is a linear trend, then the average pace of growth would provide a useful benchmark for anticipating revenues over time and thereby improve budget forecasts. If the trend is nonlinear, however, then estimating the growth of public revenues becomes more difficult, as will forecasting top-line revenue for private sector businesses. It is also important to know whether the average rate of economic growth has changed over time and whether its volatility has altered as well. Interpreting econometric issues of trend and volatility in a useful context is vital to practical decision making. For example, if the average rate of economic growth has downshifted, private firms are likely to become more cautious in hiring and equipment spending while also increasing oversight on inventories. Similarly, rising volatility for any series suggests a heightened sense of risk in using that series, which will also alter the behavior of decision makers toward an emphasis on avoiding risk.

Therefore, the first step in an econometric analysis is to identify the character of a trend in a time series—that is, whether a time series follows a linear or a nonlinear trend. A linear trend indicates a constant growth rate in a series and a nonlinear trend represents a variable growth rate. For trend selection, we will employ different types of methods, including t-value, R-squared, Akaike Information Criteria (AIC), and Schwarz Information Criteria (SIC).2 A complete estimation process to identify the time in a time series is discussed in Chapter 6, and the U.S. unemployment rate is used as a case study.

Here we focus on the real gross domestic product (GDP) growth rate and determine the type of trend. The results indicate that the real GDP growth rate follows a nonlinear—more likely inverted U-shaped—time trend since 1980. The nonlinear trend implies that the average growth rate of real GDP is not constant over time, and it increases at a faster rate for some periods than others (see Figure 1.1). Since the average growth rate is not constant over time, it is therefore not an easy task to forecast the future real GDP trend.

FIGURE 1.1 Real GDP (Year-over-Year Percentage Change)

Source: U.S. Bureau of Economic Analysis

Another way to characterize the rate of GDP growth is to calculate the mean, standard deviation, and stability ratio for different business cycles. Using a trough-to-trough definition of a business cycle, there were three business cycles between 1982 and 2009. As shown in Table 1.1, the average growth rate for the entire sample is 2.98 percent and the standard deviation is 2.1 percent, which is smaller than the mean. The stability ratio—the standard deviation relative to the mean—is 70.47 percent. However, when we break the series down into periods of individual business cycles, the stability ratio changes. For instance, the highest average growth rate during 1982 to 2009 is attached to the 1982 to 1991 business cycle; after that, the average growth rate declined in each subsequent business cycle. The most volatile business cycle is the 2001 to 2009 cycle, as this period experienced the smallest average growth rate along with the highest standard deviation.

TABLE 1.1 Real Gross Domestic Product (Year-over-Year Percentage Change)

Both trend and business cycle analysis reveal that the average real GDP growth varies over time, with some periods having a higher average growth rate than others, as shown in Table 1.1. Moreover, the average growth rate has a decreasing trend over time, while swings in GDP growth—evidenced by the stability ratio—have gotten larger. Note the growth rate for the 2001 to 2009 period is far below the pace of 1982 to 1991 and 1991 to 2001 periods. Meanwhile, the stability ratio for the 2001 to 2009 period exceeds that of the two earlier periods.

Part IB: Identifying the Cycle for a Time Series

In recent years, decision makers have been challenged to identify the changes in the stage of the business cycle—recession, recovery, expansion, slowdown—in the U.S. economy along the lines of the stylized economic cycle pictured in Figure 1.2 using industrial production. This identification is essential for business management in terms of planning production schedules, adjusting inventories and ordering inputs for the production process. In government, identifying the stage of the economic cycle will allow for better preparation for the cyclical rhythms of revenues and spending flows. Here again we see the importance of simple data description to improve decision making.

FIGURE 1.2 Total Industrial Production Growth (Output Growth by Volume, Not Revenue)

Source: Federal Reserve Board

To identify a cycle in an economic or financial time series, we recognize first that many, but not all, macroeconomic time series follow a predictable pattern over the business cycle and, as such, can be characterized by certain statistical properties. In this sense, econometrics can provide a solution to identifying changes in a series over the economic cycle and can allow decision makers to anticipate those changes and alter their business plans accordingly. We employ a number of techniques to identify and characterize a cycle, such as the mean, variance, autocorrelation, and partial autocorrelation. A complete econometric analysis to identify the cyclical elements in a time series is presented in Chapter 6. Other important macroeconomic variables with cyclical properties are GDP growth, the consumer price index (see Figure 1.3), corporate profits (see Figure 1.4), productivity (see Figure 1.5), employment (see Figure 1.6), federal budget deficit/surplus (see Figure 1.7), the yield curve (10 year/2 year, see Figure 1.8), and the credit spread (AA/5 year, see Figure 1.9).

FIGURE 1.3 U.S. Consumer Price Change

Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis

FIGURE 1.4 Corporate Profits Growth

Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis

FIGURE 1.5 Nonfarm Productivity

Source: U.S. Bureau of Labor Statistics

FIGURE 1.6 Nonfarm Productivity Change

Source: U.S. Bureau of Labor Statistics

FIGURE 1.7 Federal Budget Surplus or Deficit

Source: U.S. Department of the Treasury and Federal Reserve Board

FIGURE 1.8 Yield Curve Spread

Source: U.S. Department of the Treasury and Federal Reserve Board

FIGURE 1.9 AA Five-Year Spread

Source: Federal Reserve Board and IHS Global Insight

In the following section we characterize nonfarm payrolls growth using autocorrelations and partial autocorrelations functions.3 A simple plot of the payrolls growth (see Figure 1.10) suggests that it may not contain an explicit (linear) time trend, but it does contain a strong cyclical element. During an economic expansion, the rate of employment growth is greater than zero, and during a recession, the rate of employment growth turns negative. To confirm the cyclical behavior of payrolls growth, we plot autocorrelations and partial autocorrelations along with two-standard deviation error bands (standard errors). A good rule of thumb to determine whether a series contains a cyclical element is to check whether: (1) autocorrelations are large relative to their standard errors, (2) autocorrelations have a slow decay, and (3) partial autocorrelations spike at first few lags and are large compared to their standard errors.

FIGURE 1.10 Nonfarm Employment Growth (Year-over-Year Percentage Change)

Source: U.S. Bureau of Labor Statistics

As shown in Table 1.2, the autocorrelations (column 3) for nonfarm payroll growth are large compared to their standard errors. The autocorrelations display slow, one-sided decay, which is represented by asterisks in column 4. The partial autocorrelations (Table 1.3) show a spike at lag-one, and this spike is large for first four lags relative to their standard errors. Taken together, both autocorrelations and partial autocorrelations suggest that nonfarm payroll growth has a strong cyclical behavior.

TABLE 1.2 Autocorrelation Functions for Nonfarm Payrolls

TABLE 1.3 Partial Autocorrelation Functions for Nonfarm Payrolls

Lag

Correlation

1

0.99064

********************

2

−0.50231

**********

3

−0.38539

********

4

−0.19967

****

5

0.02576

*

6

−0.01864

.

7

−0.05064

*

8

−0.04183

*

9

−0.0928

**

10

0.01544

.

The asterisks “*” signal a visual representation of the autocorrelation.

However, while the cyclical character of the economy is evident, we also recognize that often decision makers fall for recency bias in their thinking. That is, many decision makers in the midst of an economic expansion see that expansion as the most recent experience of the business cycle and thereby project that experience into the future. In contrast, when facing a recession, decision makers project that the recession will continue for the foreseeable future. The recency bias then leads decision makers to project the most recent experience into the future and thereby fail to recognize that the cyclical pattern within the economy actually changes over time, as we have seen with the employment series in Figure 1.10.

Part IC: Identifying the Subcycles of Economic Behavior: Use of the HP Filter

During the 2010–2011 period, the pace of job and economic growth appeared to move up and down without entering into the extremes of recession or economic boom as growth remained below the pace of prior economic expansions. Yet this subcycle pattern occurred within the expansion phase itself and introduced considerable uncertainty for decision makers. Decision makers need to identify how the current cyclical behavior in any economic series stands relative to its underlying trend behavior. For example, is the series above or below trend during the current economic expansion? One simple technique to analyze any time series is through filtering and decomposing the series by applying the Hodrick-Prescott (HP) filter,4 as one among several filters. A key advantage of the HP filter is that we can observe at any point in time whether a series is moving below trend or above trend relative to the historical values of that series.

This feature of the HP filter contains a useful policy implication that will help decision makers identify the stage of the cycle—slowdown or acceleration around a trend—in any economic time series. For example, in the spring of 2012 and often in the prior two years of the economic recovery, decision makers had been challenged to read the tea leaves and to ferret out the trend of the economy and labor market. Was the economy slowing down? Speeding up? What was the trend pace of growth over time? Had the trend pace changed over time? These questions were asked many times in relationship to the pace of GDP growth, job growth, and inflation between 2009 and 2012. These subcycles in the economy are not characterized by all-or-nothing boom-or-bust metrics. Instead, there is a constant acceleration and deceleration of economic activity. An effective decision maker needs to be able to identify these subcycles, which is another case of the use of econometric techniques in a practical setting. In addition, many decision makers succumb to the confirmation bias, expecting a stronger recovery, and so will jump at the opportunity to point out that when growth peaks above trend, this is a signal of permanent prosperity—the perma-bull in the financial markets. In contrast, any slowdown in the cycle below trend leads the perma-bear to declare the emergence of the next great depression. The careful implementation of econometrics can make for better decision making even in the financial markets when faced with claims by the perma-bull or perma-bear.

We begin the HP analysis by recognizing that an economic series, such as real GDP, termed yt (log form), with gt its long-run growth path, can continuously grow, but that growth may be less than its long-run growth path-term rate, gt, for a period of time—this has in fact been the U.S. experience for several years now. So while there is no recession, usually approximated by a negative growth rate of GDP (more specifically, roughly gauged as two consecutive quarters of negative growth rate, although that was not precisely true for the 2001 U.S. recession), there are periods of time during any economic expansion that the acceleration of the economy would lead some to project a speculative boom, while a decelerating economy will lead some to project the onset of recession. Yet decision makers who recognize that periods of below- or above-trend growth are typical of every cycle will first analyze the pattern of the data and then make the correct assessments necessary for effective employment and production decisions. The economy has at times suffered a major slowdown in the rate of growth while the actual pace of growth remains positive, such as during the mid-1990s. These midcycle slowdowns are ripe for the confirmation bias. It is certainly possible to conceive a severe and long slowdown causing more hardship than a mild and short recession, the 2009 to 2011 period being a precise example. In fact, long slowdowns in employment and demand growth have occurred repeatedly in recent times, even while output and supply growth held up well, supported by the process of technology and productivity. Note that the patterns of cycle and trend can differ between economic series, evident in the current cyclical behavior of output gains in manufacturing despite manufacturing employment declining in the early phase of the recovery. With the help of the HP filter, we can see where any series stands relative to trend and therefore make better decisions for investment spending, inventories, and hiring.

Rather than waiting for a public announcement of a recession, any economic slowdown merits serious consideration by decision makers. For example, a slowdown in employment and demand growth can lead to an overall slowdown in economic output or, perhaps, to recession ahead. A decision maker may thus want to alter production and inventory levels today.

Over longer periods of time than just a single business cycle, both private and public decision makers must distinguish between the long-term trends of any business series from that of the short-term cycle for that series. For instance, 10-year Treasury rates are constantly moving during the business cycle. But are the ups and downs in Treasury rates simply the representations of a cycle around a longer-term trend? In a similar way, are the movements of labor force growth and labor force participation partly due to the current phase of the business cycle, but also are they moving within a band that indicates a longer-term trend?

Therefore, an effective analysis must separate cyclical movements from long-term trend growth in a time series. As an example, we apply the HP filter on the 10-year Treasury yield, shown in Figure 1.11, to separate cyclical movements from a long-term trend component. The log of the 10-year Treasury along with a long-run trend, based on the HP, is plotted. Since 1980, the 10-year Treasury yield has trended downward. Yet, since 2008, the plot shows a volatile pattern, which may represent uncertainty in the financial market as well as in the economic outlook. The HP filter also helps to identify periods of expansion, as evidenced by the log of the 10-year Treasury yield typically running above the long-run trend (1995), and periods of weakness in the series when rates are below their long-run trend (1986, 1994, and 2012).

FIGURE 1.11 Decomposing the 10-Year Treasury (Using the HP Filter)

Source: Federal Reserve Board

Part ID: Spotting Structural Breaks in a Time Series

Over the past 40 years, a number of instances have appeared where the basic character of an economic series, or the relationship between two series, has changed. Yet decision makers appear to have anchored their expectations of the behavior of a series in the distant past, generating an anchoring bias. For example, the growth rate of productivity appeared to change during the 1970s in response to the rapid rise in the price of oil. Employment gains in each economic recovery since 1990 appear to be much slower than employment gains prior to that time. In recent years, considerable discussion has centered on whether the entry of China into the global trading environment has altered the behavior of inflation. In contrast, the recency bias leads a researcher to emphasize that this time is different. Perhaps it is, but the assumption must be tested to determine if this time really is different.

Essentially, the questions in 2012 became: Are interest rates permanently lower today than in the past? Is there a structural break in the behavior of interest rates? If a time series experiences a sudden shift (upward or downward) in its mean and/or variance, then we characterize that shift as a structural break. Yet if decision makers are hindered by an anchoring bias, then the implementation of statistical tests will help provide evidence to overcome that bias. Similarly, statistical tests will help to overcome the recency bias, showing whether there is a structural break in the series from long-term trends. The three primary tests of a structural break in a time series—the dummy variable approach, the Chow approach, and the state-space approach—are discussed in more detail in Chapter 4. These tests have a null hypothesis that the underlying series contains a break and the alternative hypothesis is that the series does not contain a structural break. Chapter 6 provides applications and SAS codes for these tests.

We apply the Chow test to determine whether there has been a structural break in GDP growth (see Figure 1.12). The results indicate that, indeed, GDP has experienced a structural break, which occurred in the fourth quarter of 2007, as suggested by the sharp decline shown in Figure 1.12