Appendix. Table A1. Estimation results in the hedonic price regression
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1 Appendix Table A1. Estimation results in the hedonic price regression Variable Coefficient Standard Error Soil quality δ *** Plot size ρ *** dummy variables u $ Ammerland *** Aurich *** Bentheim *** Celle *** Cloppenburg *** Cuxhaven *** Diepholz *** Emsland *** Friesland *** Gifhorn *** Goslar *** Göttingen *** Hamelin-Pyrmont *** Hanover Region *** Harburg *** Heidekreis *** Helmstedt *** Hildesheim *** Holzminden *** Leer *** Lüchow--Dannenberg *** Lüneburg *** Nienburg *** Northeim *** Oldenburg *** Osnabrück *** Osterholz *** Osterode *** Peine *** Rotenburg *** Schaumburg *** Stade *** Uelzen *** Vechta *** Verden *** Wittmund *** Wolfenbüttel *** Linear trend σ $ Ammerland *** Aurich *** Bentheim *** Celle *** Cloppenburg *** Cuxhaven *** Diepholz *** Emsland *** Friesland *** Gifhorn *** Goslar Göttingen *** Hamelin-Pyrmont ***
2 Variable Coefficient Standard Error Hanover Region *** Harburg *** Heidekreis *** Helmstedt *** Hildesheim *** Holzminden *** Leer *** Lüchow-Dannenberg *** Lüneburg *** Nienburg *** Northeim ** Oldenburg *** Osnabrück *** Osterholz *** Osterode *** Peine Rotenburg *** Schaumburg *** Stade *** Uelzen *** Vechta *** Verden *** Wittmund *** Wolfenbüttel *** Number of observations 78,253 Squared correlation *, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent levels, respectively.
3 Table A2: Pairwise error correction models for suitable dominant counties Cloppenburg Vechta Emsland Oldenburg φ $' φ '$ φ $' φ '$ φ $' φ '$ φ $' φ '$ Ammerland *** *** *** *** Aurich *** / / *** / / Bentheim *** *** *** *** Celle *** *** *** *** Cloppenburg *** *** ** *** Cuxhaven *** *** *** / / Diepholz *** *** *** *** Emsland *** ** *** *** * Friesland *** *** *** *** Gifhorn *** *** *** *** Goslar *** *** *** *** Göttingen *** *** *** *** Hamelin- Pyrmont *** *** *** *** Hanover Region *** *** *** *** Harburg *** *** *** *** Heidekreis *** *** *** *** Helmstedt *** *** *** *** Hildesheim *** *** *** *** Holzminden *** *** *** *** Leer *** *** *** *** Lüchow- Dannenberg *** / / *** *** Lüneburg *** *** *** / / Nienburg *** *** *** *** Northeim *** / / *** *** Oldenburg *** 0.461*** *** 0.398*** *** 0.645*** Osnabrück *** *** *** ** *** Osterholz *** *** *** *** Osterode *** *** *** *** Peine *** / / *** *** Rotenburg *** ** *** *** Schaumburg *** *** *** *** Stade *** *** *** *** Uelzen *** *** *** *** Vechta *** *** *** Verden *** *** *** *** Wittmund *** / / *** *** Wolfenbüttel *** *** *** *** / denotes that there is no cointegration relationship between the candidate and other counties. *, **, and *** denote significance at the 90%, 95%, and 99% level, respectively.
4 Table A3. Cointegration tests with neighbors and a dominant county Cloppenburg Neighbors β) $ Cointegrating vector Cloppenburg β) $' Trace statistic Ammerland 0.493*** 0.396*** *** Aurich 0.418*** 0.347*** *** Bentheim 0.552*** *** Celle 0.225* 0.373*** *** Cuxhaven 0.345*** 0.371*** *** Diepholz 0.901*** 0.201*** *** Emsland 0.395*** 0.573*** *** Friesland 0.272** 0.556*** *** Gifhorn 0.768*** 0.328*** *** Goslar 0.536*** *** Göttingen 0.240** 0.158*** *** Hamelin-Pyrmont 0.277** 0.152*** *** Hanover Region 0.897*** *** Harburg 0.441*** 0.242** *** Heidekreis 0.835*** 0.181** *** Helmstedt 0.450*** 0.140** *** Hildesheim 0.589*** 0.094** 96.65*** Holzminden 0.673*** *** Leer 0.879*** *** Lüchow-Dannenberg 0.380*** 0.422*** *** Lüneburg 0.498*** 0.223** *** Nienburg 0.946*** 0.146** *** Northeim 0.497*** 0.177*** *** Oldenburg 0.629*** 0.381*** *** Osnabrück 0.626*** 0.205*** *** Osterholz *** Osterode 0.571*** 0.218*** *** Peine 0.616*** *** Rotenburg 0.721*** 0.383*** *** Schaumburg 0.201** 0.179*** *** Stade 0.590*** 0.378*** *** Uelzen 0.860*** *** Vechta 0.709*** *** Verden 0.469*** 0.319*** *** Wittmund 0.789*** 0.287** *** Wolfenbüttel 0.362** 0.173*** *** The trace statistic for testing H0: r=0 vs. H1: r 1 was estimated with unrestricted intercepts and restricted trend coefficients, where r denotes the number of cointegrating vectors. *, **, and *** denote significance at the 90%, 95%, and 99% level, respectively.
5 Table A4. Estimation results of regional price diffusion equations with neighbors and a dominant county Cloppenburg Adjustment speed Own lag effects Neighbor lag effects Cloppenburg lag effects Neighbors contemp. effect Cloppenburg contemp. effect Wu- Hausman test Ammerland *** * *** Aurich *** *** *** * Bentheim *** *** ** Celle *** * *** 0.282** 0.27 Cuxhaven *** *** ** ** Diepholz *** *** ** Emsland *** ** *** 0.101** 0.145** 3.05* Friesland *** ** Gifhorn *** *** 0.422*** Goslar *** * Göttingen *** *** *** Hamelin- Pyrmont *** Hanover Region *** * *** Harburg *** * ** Heidekreis *** *** * 1.09 Helmstedt *** ** Hildesheim *** Holzminden *** ** *** Leer *** *** Lüchow- Dannenberg *** * Lüneburg *** *** Nienburg *** *** ** Northeim *** *** ** Oldenburg *** *** ** 0.279** Osnabrück *** *** *** Osterholz *** *** ** ** Osterode *** *** ** 0.201* 0.27 Peine *** Rotenburg *** *** Schaumburg *** Stade *** ** *** Uelzen *** *** ** Vechta *** *** Verden *** ** Wittmund *** ** *** Wolfenbüttel *** *** ** The lag orders for each region are selected separately using the Bayesian information criterion using a maximum lag order of four. The reported coefficient for the lagged effects is the value with the lowest p-value. denotes a lag order of zero. All regressions include an intercept term. *, **, and *** denote significance at the 90%, 95%, and 99% level, respectively.
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