Hello Everyone,

I am trying to understand how one can determine the probability of variable being positive in the next time period. Will it simply be the Beta coefficient?

I have this house house price index data with quarterly time periods however, I am unable to determine a method of checking this. I tried looking online but could not really find a clear direction or answer. WIll be great if you all can help out.

Similarly I am interested in knowing the estimated probability of the variable of interest having a positive value in the next period, assuming some values for this period and (if necessary) earlier periods? I was thinking of taking a lag of 12 on the independent variable? However will be great to know if there is some logical reasoning behind determining this.

Thank you.

The data is posted below:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float qtr double ire byte ire_up
 60  3.612956374109727 .
 61 3.6073629526062674 0
 62  3.683168175768565 1
 63  3.702017767254164 1
 64 3.6792002614994734 0
 65  3.629673356965487 0
 66    3.6683909916866 1
 67  3.724207987167231 1
 68  3.676384212721243 0
 69  3.673920626655173 0
 70 3.7650253482932237 1
 71  3.778535040432059 1
 72  3.863949784193698 1
 73 3.9036097127264004 1
 74 3.9415953998103266 1
 75  3.924492931710415 0
 76 3.9547701855887216 1
 77 4.0093094130255205 1
 78  4.000722290432553 0
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 80  3.990009446587858 1
 81  3.943699951168572 0
 82 4.0003946235368835 1
 83  4.017056667675216 1
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 85  3.939542837177556 0
 86 3.9534777597137096 1
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 89 3.8552304070225127 0
 90 3.8647867333428336 1
 91  3.863217141135468 0
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 99 3.7170076218352723 0
100  3.692007058057744 0
101 3.6713822672465364 0
102  3.696135564753703 1
103  3.716917683394428 1
104 3.6800808597575867 0
105  3.669508033834646 0
106 3.7157769512320895 1
107 3.6996438099944564 0
108 3.6630692172141193 0
109 3.6228892130740906 0
110 3.6823053922447677 1
111 3.7328102114198773 1
112 3.7051193699347245 0
113 3.6766525087394157 0
114 3.7343784941851546 1
115  3.761541917737704 1
116   3.78048049081674 1
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118 3.7970138689536563 1
119  3.853423082524823 1
120 3.8787908848059027 1
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122  3.899830983862784 1
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124 3.8793593079843487 1
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126 3.8638049775575234 1
127  3.892697464308256 1
128 3.8826415674566297 0
129  3.838294653517233 0
130 3.8740448799429448 1
131  3.874088504748847 1
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141 3.8820502775311962 0
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143  3.958839795203055 1
144 3.9589886490288735 1
145 3.9676715021389626 1
146  4.068551427612793 1
147  4.074595833473368 1
148 4.0795405211520865 1
149  4.125101675328523 1
150  4.192195451083905 1
151 4.2566788080002205 1
152 4.2943000019498445 1
153     4.302323036163 1
154   4.34191922852453 1
155  4.427729896401545 1
156  4.468891021694349 1
157   4.43590028457885 0
158  4.523991583430157 1
159  4.556902835127961 1
160  4.547791041906662 0
161   4.54359154674171 0
162  4.575734166208472 1
163  4.622839166128182 1
164  4.625797962950707 1
165  4.605191185767595 0
166 4.5846949925700144 0
167  4.589255211965498 1
168  4.616067592975519 1
169  4.607940345550701 0
170  4.650407379157976 1
171  4.675129923242991 1
172  4.686915136331837 1
173  4.693351378476683 1
174   4.74270530300092 1
175   4.78383422889201 1
176  4.777993443080734 0
177 4.7770204429147505 0
178  4.818012865016663 1
179 4.8634646415452805 1
180  4.863010386137779 0
181  4.871530302548306 1
182  4.901472745045978 1
183  4.938217994513995 1
184  4.951859337444899 1
185  4.971777341803963 1
186  5.018327632056576 1
187  5.031761228441853 1
188  5.038868946870288 1
189  5.024072607659581 0
190  5.013223275714393 0
191   4.99844192284112 0
192  4.968086114405627 0
193  4.922857077705888 0
194  4.888583660926652 0
195  4.844771954759856 0
196   4.80277682839408 0
197  4.745126022608024 0
198   4.71274664462973 0
199  4.694106459175766 0
200 4.6652082828912365 0
201  4.622958039892735 0
202  4.587940605565803 0
203  4.541432410834532 0
204 4.4869743151269805 0
205  4.420819371643034 0
206  4.357804380197787 0
207  4.295690938451489 0
208  4.247753538483833 0
209  4.215130492082665 0
210  4.225391830295849 1
211  4.235588015313755 1
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226  4.584292761304076 1
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230  4.694218071219737 1
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233  4.758522833186285 1
234  4.773402115181506 1
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end
format %tq qtr