經濟測量學

經濟測量學（粵拼：gin1 zai3 caak1 loeng4 hok6；英文：econometrics），粵文又叫計量經濟學，係經濟學同統計學嘅結合領域，旨在諗點樣用統計學方法分析啲描述經濟現象嘅數據，靠噉嚟搵出唔同經濟現象之間嘅關係^[1]。技術性啲噉講，經濟測量學做嘅嘢可以噉樣描述^[2]：

「

原版英文："... The quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference."
粵文翻譯：（經濟測量學係）對實際經濟現象嘅量化分析，基於同期嘅理論發展同埋觀察，而呢啲嘢係用適合嘅推論方法連繫埋一齊嘅。

」

經濟測量學會用好多推論統計技術：喺最基本上，經濟測量學用嘅技術學係迴歸模型（regression model）－迴歸模型做嘅係攞一柞數據，數據入面有若干個個案，每個個案都喺若干（ $n$ ）個變數 $\{x_{1},x_{2},...x_{n}\}$ 上有個值，迴歸分析會畫出一條線（迴歸模型），例： $x_{1}=\beta _{2}x_{2}+\beta _{3}x_{3}+...$ ，表達呢柞變數之間嘅關係，而人攞住呢個噉嘅模型，將來就有得用個模型嚟做預測，例：用 $x_{2}$ 同 $x_{3}$ 嘅值估 $x_{1}$ 嘅值^[3]。除咗迴歸模型，經濟測量學研究仲可以用到結構方程模型（SEM）等更加深嘅統計方法^[4]^[5]。

經濟測量學喺商學上相當有用：好似係管理學噉，管理學成日都會有興趣分析一間公司嘅表現－營業額、申請到嘅專利嘅數量... 等等－會點樣受到（例如）間公司嘅社會網絡影響，而呢種研究就會涉及分析政府數據庫當中有關唔同公司嘅營業額數據，用到經濟測量學方法^[6]^[7]。第啲商學領域上都會用到類似嘅方法做分析。

基礎

量度經濟

經濟學（economics）係社會科學嘅一個領域，專門研究貨品同服務嘅生產、消費同埋分配：人有好多慾望，但係就算有咗現代科技，佢哋手頭上嘅資源－錢、時間、精神－都梗係有限嘅，佢哋成日都冇足夠嘅資源達到嗮佢哋想要達到嘅目的（稀缺性），例如一間公司唔夠資源生產嗮佢想要生產嘅嘢；因為資源有限，人喺做決策嗰陣往往需要做犧牲，需要放棄某啲嘢－呢啲取捨同資源分配嘅過程就係經濟學研究重心^[8]。

經濟學同第啲科學一樣，會要求研究者有方法客觀噉量度自己研究嘅現象同埋研究呢啲現象之間嘅關係。經濟指標（economic indicator）泛指能夠反映經濟現象嘅指標數值。舉個例說明，本地生產總值（gross domestic product，GDP）係經濟學成日研究嘅一個經濟指標：將某段時間之內一個經濟體裏面嘅最終貨品服務嘅市場價值冚唪唥加埋一齊，得出個數就係嗰個經濟體喺嗰段時間之內嘅 GDP；喺計 GDP 嘅過程之中，個分析者將個經濟體入面一大柞個體物件（貨品服務）嘅屬性（市場價值）以某啲方法結合埋做一個數值（就噉加埋一齊），用嚟反映經濟活動－呢啲價值會反映個經濟體總共生產咗幾多貨品服務^[9]。經濟測量學跟住就會想用統計方法分析呢啲指標值之間嘅關係，從而理解經濟現象^[1]。

迴歸分析

迴歸分析（regression analysis）可以話係經濟測量學嘅基礎。迴歸分析係一類嘅統計模型技術，指一啲用嚟建立描述兩個或者以上唔同變數之間嘅關係嘅數學模型^[10]：喺統計學上，研究者好多時會想用一個變數嘅數值嚟預測第啲變數嘅數值；喺最簡單嗰種情況下，個統計模型會涉及兩個連續性（continuous）嘅變數，當中一個係自變數（independent variable；IV），而另一個就係應變數（dependent variable；DV），而個研究者會用個 IV 嘅數值嚟預測個 DV 嘅數值；對個研究者嚟講，一個可能嘅做法係搜集啲數據返嚟，用啲數據做迴歸分析，整個模型（即係畫條線）出嚟，個模型就能夠幫佢預測「當 IV 係呢個數值嗰陣，假設第啲因素不變，個 DV 嘅數值會傾向係幾多」^[11]^[12]。迴歸模型有以下呢啲^[13]：

DV=a+b(IV)

（線性迴歸；linear regression）；

DV=a+b(IV)+c(IV)^{2}

（多項式迴歸；polynomial regression）；

... 等等。原則上，如果有個方法可以由過往數據嗰度搵出 $a$ 同埋 $b$ 等參數嘅數值，第時就可以靠條式大致上用 IV 嘅值估計 DV 嘅值；而家想像 $DV$ 係失業率，而 $IV$ 係本地生產總值（GDP）增長率，用過去數據得出參數 $a$ 同 $b$ 嘅數值，噉啲人第時就有可能睇到 GDP 增長嚟估計失業率嘅數值^[1]^[2]。順帶一提，好多第啲經濟測量上用嘅方法－好似係結構方程模型（SEM）噉－查實都係迴歸分析嘅變種^[4]。

理論

數據來源

睇埋

參考

Giovannini, Enrico. Understanding Economic Statistics (PDF), OECD Publishing, 2008, ISBN 978-92-64-03312-2.

攷

↑ ^1.0 ^1.1 ^1.2 Stock, J. H., and M. W. Watson. (2015). Introduction to Econometrics, Third Update, Global Edition. Pearson Education Limited.
↑ ^2.0 ^2.1 P. A. Samuelson, T. C. Koopmans, and J. R. N. Stone (1954). "Report of the Evaluative Committee for Econometrica." Econometrica, 22(2), p. 142.
↑ Greene, William (2012). "Chapter 1: Econometrics". Econometric Analysis (7th ed.). Pearson Education. pp. 47–48. ISBN 9780273753568. Ultimately, all of these will require a common set of tools, including, for example, the multiple regression model, the use of moment conditions for estimation, instrumental variables (IV) and maximum likelihood estimation."
↑ ^4.0 ^4.1 Greene, William (2012). Econometric Analysis (7th ed.). Pearson Education. pp. 34, 41–42.
↑ Wooldridge, Jeffrey (2012). "Chapter 1: The Nature of Econometrics and Economic Data". Introductory Econometrics: A Modern Approach (5th ed.). South-Western Cengage Learning. p. 2.
↑ Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study (PDF). Administrative science quarterly, 45(3), 425-455.
↑ Zaheer, A., & Soda, G. (2009). Network evolution: The origins of structural holes (PDF). Administrative Science Quarterly, 54(1), 1-31.
↑ Krugman, Paul; Wells, Robin (2012). Economics (3rd ed.). Worth Publishers.
↑ Howitt, Peter M. (1987). "Macroeconomics: Relations with microeconomics". In Eatwell, John; Milgate, Murray; Newman, Peter. The New Palgrave Dictionary of Economics. The New Palgrave: A Dictionary of Economics (first ed.). pp. 273–276.
↑ Linear Regression with example. Towards Data Science.
↑ Seber, G. A., & Lee, A. J. (2012). Linear regression analysis (Vol. 329). John Wiley & Sons.
↑ YangJing Long (2009). "Human age estimation by metric learning for regression problems". Proc. International Conference on Computer Analysis of Images and Patterns: 74–82.
↑ Regression in Machine Learning. Towards Data Science.

出面網頁

Journal of Financial Econometrics.
The Econometrics Journal.
Econometric Society.
Econometric Links.
Teaching Econometrics (Index by the Economics Network (UK)).
Applied Econometric Association.
The Society for Financial Econometrics.
The interview with Clive Granger – Nobel winner in 2003, about econometrics.

[stock2015-1] 1.0 ^1.1 ^1.2 Stock, J. H., and M. W. Watson. (2015). Introduction to Econometrics, Third Update, Global Edition. Pearson Education Limited.

[samuelson54-2] 2.0 ^2.1 P. A. Samuelson, T. C. Koopmans, and J. R. N. Stone (1954). "Report of the Evaluative Committee for Econometrica." Econometrica, 22(2), p. 142.

[3] Greene, William (2012). "Chapter 1: Econometrics". Econometric Analysis (7th ed.). Pearson Education. pp. 47–48. ISBN 9780273753568. Ultimately, all of these will require a common set of tools, including, for example, the multiple regression model, the use of moment conditions for estimation, instrumental variables (IV) and maximum likelihood estimation."

[greene2012-4] 4.0 ^4.1 Greene, William (2012). Econometric Analysis (7th ed.). Pearson Education. pp. 34, 41–42.

[5] Wooldridge, Jeffrey (2012). "Chapter 1: The Nature of Econometrics and Economic Data". Introductory Econometrics: A Modern Approach (5th ed.). South-Western Cengage Learning. p. 2.

[ahuja2000-6] Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study (PDF). Administrative science quarterly, 45(3), 425-455.

[7] Zaheer, A., & Soda, G. (2009). Network evolution: The origins of structural holes (PDF). Administrative Science Quarterly, 54(1), 1-31.

[8] Krugman, Paul; Wells, Robin (2012). Economics (3rd ed.). Worth Publishers.

[howittmacro-9] Howitt, Peter M. (1987). "Macroeconomics: Relations with microeconomics". In Eatwell, John; Milgate, Murray; Newman, Peter. The New Palgrave Dictionary of Economics. The New Palgrave: A Dictionary of Economics (first ed.). pp. 273–276.

[datascienceregression-10] Linear Regression with example. Towards Data Science.

[11] Seber, G. A., & Lee, A. J. (2012). Linear regression analysis (Vol. 329). John Wiley & Sons.

[12] YangJing Long (2009). "Human age estimation by metric learning for regression problems". Proc. International Conference on Computer Analysis of Images and Patterns: 74–82.

[13] Regression in Machine Learning. Towards Data Science.

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