Economic and Business Forecasting

JOHN E. SILVIA is a Managing Director and the Chief Economist for Wells Fargo Securities. In 2010, he was recognized for the Best Inflation Forecast, the Best Overall Forecast, and the Best Personal Consumption Expenditures Forecast by The Federal Reserve Bank of Chicago. AZHAR IQBAL is an Econometrician and Vice President at Wells Fargo Securities where he provides quantitative analysis to the Economics group as well as modeling and forecasting of macro and financial variables. He has spoken at the American Economic Association, Econometric Society, and other international conferences. SAM BULLARD is a Managing Director and Senior Economist at Wells Fargo Securities providing analysis and commentary on financial markets and macroeconomic developments. SARAH WATT is an Economist with Wells Fargo Securities. She covers the U.S. macro economy, including labor market trends. She also works closely with senior members of her team to produce special reports and regional economic commentary on several U.S. states. KAYLYN SWANKOSKI is an Economic Analyst at Wells Fargo Securities.

Preface xiii

Acknowledgments xvii

Chapter 1 Creating Harmony Out of Noisy Data 1

Effective Decision Making: Characterize the Data 2

Chapter 2 First, Understand the Data 27

Growth: How Is the Economy Doing Overall? 30

Personal Consumption 31

Gross Private Domestic Investment 33

Government Purchases 35

Net Exports of Goods and Services 36

Real Final Sales and Gross Domestic Purchases 37

The Labor Market: Always a Core Issue 37

Establishment Survey 39

Data Revision: A Special Consideration 42

The Household Survey 43

Marrying the Labor Market Indicators Together 48

Jobless Claims 48

Inflation 49

Consumer Price Index: A Society’s Inflation Benchmark 50

Producer Price Index 53

Personal Consumption Expenditure Deflator: The Inflation Benchmark for Monetary Policy 55

Interest Rates: Price of Credit 56

The Dollar and Exchange Rates: The United States in a Global Economy 58

Corporate Profits 60

Summary 62

Chapter 3 Financial Ratios 63

Profitability Ratios 64

Summary 73

Chapter 4 Characterizing a Time Series 75

Why Characterize a Time Series? 76

How to Characterize a Time Series 77

Application: Judging Economic Volatility 101

Summary 109

Chapter 5 Characterizing a Relationship between Time Series 111

Important Test Statistics in Identifying Statistically Significant Relationships 115

Simple Econometric Techniques to Determine a Statistical Relationship 119

Advanced Econometric Techniques to Determine a Statistical Relationship 120

Summary 126

Additional Reading 127

Chapter 6 Characterizing a Time Series Using SAS Software 129

Tips for SAS Users 130

The DATA Step 131

The PROC Step 135

Summary 156

Chapter 7 Testing for a Unit Root and Structural Break Using SAS Software 157

Testing a Unit Root in a Time Series: A Case Study of the U.S. CPI 158

Identifying a Structural Change in a Time Series 162

The Application of the HP Filter 169

Application: Benchmarking the Housing Bust, Bear Stearns, and Lehman Brothers 172

Summary 177

Chapter 8 Characterizing a Relationship Using SAS 179

Useful Tips for an Applied Time Series Analysis 179

Converting a Dataset from One Frequency to Another 182

Application: Did the Great Recession Alter Credit Benchmarks? 215

Summary 221

Chapter 9 The 10 Commandments of Applied Time Series Forecasting for Business and Economics 223

Commandment 1: Know What You Are Forecasting 224

Commandment 2: Understand the Purpose of Forecasting 226

Commandment 3: Acknowledge the Cost of the Forecast Error 226

Commandment 4: Rationalize the Forecast Horizon 229

Commandment 5: Understand the Choice of Variables 231

Commandment 6: Rationalize the Forecasting Model Used 232

Commandment 7: Know How to Present the Results 234

Commandment 8: Know How to Decipher the Forecast Results 235

Commandment 9: Understand the Importance of Recursive Methods 238

Commandment 10: Understand Forecasting Models Evolve over Time 239

Summary 240

Chapter 10 A Single-Equation Approach to Model-Based Forecasting 241

The Unconditional (Atheoretical) Approach 242

The Conditional (Theoretical) Approach 251

Recession Forecast Using a Probit Model 257

Summary 261

Chapter 11 A Multiple-Equations Approach to Model-Based Forecasting 263

The Importance of the Real-Time Short-Term Forecasting 265

The Individual Forecast versus Consensus Forecast: Is There an Advantage? 266

The Econometrics of Real-Time Short-Term Forecasting: The BVAR Approach 268

Forecasting in Real Time: Issues Related to the Data and the Model Selection 275

Case Study: WFC versus Bloomberg 280

Summary 288

Appendix 11A: List of Variables 289

Chapter 12 A Multiple-Equations Approach to Long-Term Forecasting 291

The Unconditional Long-Term Forecasting: The BVAR Model 293

The BVAR Model with Housing Starts 296

The Model without Oil Price Shock 298

The Model with Oil Price Shock 304

Summary 306

Chapter 13 The Risks of Model-Based Forecasting: Modeling, Assessing, and Remodeling 307

Risks to Short-Term Forecasting: There Is No Magic Bullet 308

Risks of Long-Term Forecasting: Black Swan versus a Group of Black Swans 310

Model-Based Forecasting and the Great Recession/Financial Crisis: Worst-Case Scenario versus Panic 314

Summary 315

Chapter 14 Putting the Analysis to Work in the Twenty-First-Century Economy 317

Benchmarking Economic Growth 318

Industrial Production: Another Case of Stationary Behavior 322

Employment: Jobs in the Twenty-First Century 324

Inflation 331

Interest Rates 337

Imbalances between Bond Yields and Equity Earnings 338

A Note of Caution on Patterns of Interest Rates 345

Business Credit: Patterns Reminiscent of Cyclical Recovery 347

Profits 348

Financial Market Volatility: Assessing Risk 349

Dollar 351

Economic Policy: Impact of Fiscal Policy and the Evolution of the U.S. Economy 353

The Long-Term Deficit Bias and Its Economic Implications 358

Summary 362

Appendix: Useful References for SAS Users 365

About the Authors 367

Index 369