設備生產價格已顯示出經濟整體健康狀況的反映。此外，除了與建築相關的設備價格（例如，挖掘機，起重機和吊索鏟價格（ECDP）和通用機械價格（GMP））以外，對於建築設備價格（CEP）也是如此。最重要的是，由於包括資本在內的許多資產類別的效率提高，過去幾十年來的技術進步降低了價格。但是，在學術文獻中，總體上設備價格的相關性已被很大程度上忽略。對於CEP，ECDP和GMP更是如此。那麼，CEP，ECDP和GMP有什麼關聯？利用聖路易斯聯邦儲備銀行1993年至2019年的月度數據，本文使用普通最小二乘（OLS）回歸和向量自回歸（VAR），後者採用正交化的脈衝響應函數（OIRF）來確定什麼CEP，ECDP和GMP的長期關聯，有時還包括短期關聯。 OLS方法和VAR的OIRF的結果表明，許多技術，宏觀經濟和商品變量始終與這些指數相關。但是，最一致的相關變量是那些

Equipment production prices have been shown to be a reflection of the overall health of economies. Additionally, this is also true for Construction Equipment Prices (CEPs) in addition to construction-related equipment prices such as excavator, crane, and dragline prices (ECDPs), and general machinery prices (GMPs). On top of this, technological progress over the past few decades has lowered prices due to increased efficiency for many asset classes including capital. However, correlates of equipment prices in general has been largely ignored in the academic literature. This is even more so true for CEPs, ECDPs, and GMPs. What then are the correlates of CEPs, ECDPs, and GMPs? Using monthly data from 1993 until 2019 from the Bank of the Federal Reserve in St. Louis, this thesis uses ordinary least square (OLS) regressions and vector autoregressions (VARs) the latter of which implements orthogonalized impulse response functions (OIRFs) to determine what the long-term correlates and in some instances short-term correlates of CEPs, ECDPs, and GMPs. The results of the OLS method and the VAR’s OIRFs show that a number of technological, macroeconomic, and commodity variables consistently correlate with these indices. However, the most consistently correlated variables are those associated with financial markets.

Table of Contents
1. Introduction 13
2. Literature Review 17
2.1. Interest Rates and Investment 21
2.2. Growth, Supply, Demand, Labor, and Commodities 24
2.3. Oligopolistic Competition in the US Construction Industry 26
2.4. Equipment Prices and Other Financial Market Indices 29
2.5. Machinery or Equipment Prices or Inflation and Technological Innovation 33
2.6. Tariff Impacts on CEPs 36
2.7. Price Versus Cost and Post-Keynesian Price Theory 36
2.8. Political Economy of the US Construction Industry 37
3. Methodology 42
3.1. Descriptive Statistics and Properties of Variables 45
3.2. Ordinary Least Square Linear Regression Method 52
3.3. Vector Auto-Regression Models 53
3.4. Augmented Dickey Fuller Unit Root Tests 56
3.5. Lag Order Selection Criterion 62
3.6. Impulse Response Functions 63
3.7. Orthogonalized Impulse Response Functions 64
3.8. Granger Causality 66
3.9. Dynamic Forecasts 66
3.10. Added Variable Plots 67
4. Empirical Results 69
4.1. Ordinary Least Square Regressions 69
4.1.1. CEPs Model 1—NASDAQ, Data Processing, and Technology CPI 74
4.1.2. CEPs Model 2— Diesel Prices, DXY, and Inventory Over Sales 84
4.1.3. CEPs Model 3—GDP, Interest Rates, and Industrial Production 94
4.1.4. CEPs Model 4—One-Month Differenced NASDAQ, Interest Rates, and Steel Prices 104
4.2. ECDP VAR Models 112
4.2.1. ECDP Model 1—Data Processing, the NASDAQ, and Technology Hardware CPI 114
4.2.2. ECDP Model 2—DXY, Interest Rates, and GDP 123
4.2.3. ECDP Model 3—Industrial Production, Iron and Steel Prices, and Diesel Prices 134
4.3. Added Variable Plots 142
5. Analysis of the Results 156
5.1. Bivariate Relationships of CEPs 156
5.1.1. The NASDAQ Composite and CEPs 156
5.1.2. Macroeconomic Variables 172
5.1.3. NRCS 179
5.1.4. Gross Domestic Product 181
5.1.5. Iron Ore, Steel, Diesel, and the DXY 181
5.1.6. Industrial Production of Business Equipment 182
5.2. General-Purpose Equipment Correlates 183
5.3. Excavators, Cranes, and Dragline OLS Correlations 191
5.4. Vector Auto-Regression Orthogonalized Impulse Responses 195
5.5. Summary and Ramification of Technical Analysis 200
5.6. Granger Causality Results 206
5.7. Political Party Impacts 207
6. Conclusion 220
7. Bibliography 228

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