我正在尝试使用来自R的glarma软件包来估计一个没有移动avarage组件的二进制自回归移动平均（BARMA）模型.jstat文件（https://www.jstatsoft.org/article/view/v067i07）认为BARMA模型可以适应glarma软件包但它没有给出任何拟合二项分布的例子，伯努利是我感兴趣的特例。在实践中，我想得到经济衰退的概率（衰退时y = 1，扩张时y = 0）使用二进制变量的过去观察和连续预测器，比如x。
z<-(
"Month_Year X Y
86 1996-02 99.45975 0
87 1996-03 99.65509 0
88 1996-04 99.84560 0
89 1996-05 100.01900 0
90 1996-06 100.16610 0
91 1996-07 100.28170 0
92 1996-08 100.36410 0
93 1996-09 100.43720 0
94 1996-10 100.52220 0
95 1996-11 100.63570 0
96 1996-12 100.76500 0
97 1997-01 100.89550 0
98 1997-02 101.00230 0
99 1997-03 101.07710 0
100 1997-04 101.10980 0
101 1997-05 101.08710 0
102 1997-06 101.00720 0
103 1997-07 100.86730 0
104 1997-08 100.66720 0
105 1997-09 100.40610 1
106 1997-10 100.08910 1
107 1997-11 99.73795 1
108 1997-12 99.41429 1
109 1998-01 99.17356 1
110 1998-02 99.04559 1
111 1998-03 98.99677 1
112 1998-04 98.97177 1
113 1998-05 98.91449 1
114 1998-06 98.79021 1
115 1998-07 98.58416 1
116 1998-08 98.31280 1
117 1998-09 98.03580 1
118 1998-10 97.83183 1
119 1998-11 97.75050 1
120 1998-12 97.79238 1
121 1999-01 97.94292 1
122 1999-02 98.18636 1
123 1999-03 98.51665 1
124 1999-04 98.91557 1
125 1999-05 99.34491 1
126 1999-06 99.74365 0
127 1999-07 100.07490 0
128 1999-08 100.32100 0
129 1999-09 100.49780 0
130 1999-10 100.62220 0
131 1999-11 100.71340 0
132 1999-12 100.79530 0
133 2000-01 100.88310 0
134 2000-02 100.98710 0
135 2000-03 101.10020 0
136 2000-04 101.21350 0
137 2000-05 101.32690 0
138 2000-06 101.42570 0
139 2000-07 101.48150 0
140 2000-08 101.47170 0
141 2000-09 101.40530 0
142 2000-10 101.30170 0
143 2000-11 101.18180 0
144 2000-12 101.05700 1
145 2001-01 100.91470 1
146 2001-02 100.72290 1
147 2001-03 100.45690 1
148 2001-04 100.11370 1
149 2001-05 99.70970 1
150 2001-06 99.29787 1
151 2001-07 98.94471 1
152 2001-08 98.70450 1
153 2001-09 98.59423 1
154 2001-10 98.61945 1
155 2001-11 98.76863 1
156 2001-12 98.99450 1
157 2002-01 99.24209 1
158 2002-02 99.45296 1
159 2002-03 99.59255 1
160 2002-04 99.64648 1
161 2002-05 99.61874 1
162 2002-06 99.53635 1
163 2002-07 99.42953 1
164 2002-08 99.31564 1
165 2002-09 99.19376 1
166 2002-10 99.05710 1
167 2002-11 98.91230 1
168 2002-12 98.76412 1
169 2003-01 98.63148 1
170 2003-02 98.52281 1
171 2003-03 98.44492 1
172 2003-04 98.40364 1
173 2003-05 98.41142 1
174 2003-06 98.50822 1
175 2003-07 98.73607 1
176 2003-08 99.10629 0
177 2003-09 99.56125 0
178 2003-10 100.01620 0
179 2003-11 100.39520 0
180 2003-12 100.65770 0
181 2004-01 100.79950 0
182 2004-02 100.84060 0
183 2004-03 100.82970 0
184 2004-04 100.81450 0
185 2004-05 100.82410 0
186 2004-06 100.85740 0
187 2004-07 100.88310 0
188 2004-08 100.86770 0
189 2004-09 100.79620 1
190 2004-10 100.66380 1
191 2004-11 100.47460 1
192 2004-12 100.24670 1
193 2005-01 100.00600 1
194 2005-02 99.77551 1
195 2005-03 99.55034 1
196 2005-04 99.32622 1
197 2005-05 99.11332 1
198 2005-06 98.93478 1
199 2005-07 98.81171 1
200 2005-08 98.75800 1
201 2005-09 98.77325 1
202 2005-10 98.85077 1
203 2005-11 98.99262 1
204 2005-12 99.19006 0
205 2006-01 99.40803 0
206 2006-02 99.61194 0
207 2006-03 99.77552 0
208 2006-04 99.88607 0
209 2006-05 99.94712 0
210 2006-06 99.98738 0
211 2006-07 100.04780 0
212 2006-08 100.14650 0
213 2006-09 100.30130 0
214 2006-10 100.51280 0
215 2006-11 100.75700 0
216 2006-12 101.00010 0
217 2007-01 101.23690 0
218 2007-02 101.48260 0
219 2007-03 101.73630 0
220 2007-04 102.00100 0
221 2007-05 102.25590 0
222 2007-06 102.49180 0
223 2007-07 102.70640 0
224 2007-08 102.89490 0
225 2007-09 103.06730 0
226 2007-10 103.20180 0
227 2007-11 103.27270 0
228 2007-12 103.27810 0
229 2008-01 103.24160 0
230 2008-02 103.19320 0
231 2008-03 103.11820 0
232 2008-04 102.98450 0
233 2008-05 102.72400 1
234 2008-06 102.25180 1
235 2008-07 101.47420 1
236 2008-08 100.50930 1
237 2008-09 99.40029 1
238 2008-10 98.26229 1
239 2008-11 97.28954 1
240 2008-12 96.63335 1
241 2009-01 96.35611 1
242 2009-02 96.43845 1
243 2009-03 96.81737 1
244 2009-04 97.40713 0
245 2009-05 98.09644 0
246 2009-06 98.79945 0
247 2009-07 99.46492 0
248 2009-08 100.06810 0
249 2009-09 100.59230 0
250 2009-10 101.02140 0
251 2009-11 101.34270 0
252 2009-12 101.55690 0
253 2010-01 101.67500 0
254 2010-02 101.71390 0
255 2010-03 101.69120 0
256 2010-04 101.62320 0
257 2010-05 101.54930 0
258 2010-06 101.51170 0
259 2010-07 101.53350 0
260 2010-08 101.62020 0
261 2010-09 101.75220 0
262 2010-10 101.89080 0
263 2010-11 101.99610 0
264 2010-12 102.05080 0
265 2011-01 102.05290 0
266 2011-02 102.00730 0
267 2011-03 101.91880 0
268 2011-04 101.77360 0
269 2011-05 101.56500 1
270 2011-06 101.30440 1
271 2011-07 101.02060 1
272 2011-08 100.76050 1
273 2011-09 100.56920 1
274 2011-10 100.46990 1
275 2011-11 100.45810 1
276 2011-12 100.51640 1
277 2012-01 100.60950 1
278 2012-02 100.70350 1
279 2012-03 100.76430 0
280 2012-04 100.78170 0
281 2012-05 100.77250 0
282 2012-06 100.77170 0
283 2012-07 100.79880 0
284 2012-08 100.84520 0
285 2012-09 100.88340 0
286 2012-10 100.88510 0
287 2012-11 100.84560 0
288 2012-12 100.77100 0
289 2013-01 100.66200 0
290 2013-02 100.52760 0
291 2013-03 100.37900 0
292 2013-04 100.22020 0
293 2013-05 100.05030 0
294 2013-06 99.87241 0
295 2013-07 99.71181 0
296 2013-08 99.58968 0
297 2013-09 99.48809 0
298 2013-10 99.37336 0
299 2013-11 99.24930 1
300 2013-12 99.12724 1
301 2014-01 99.01431 1
302 2014-02 98.92592 1
303 2014-03 98.86857 1
304 2014-04 98.83715 1
305 2014-05 98.81997 1
306 2014-06 98.81721 1
307 2014-07 98.81522 1
308 2014-08 98.78034 1
309 2014-09 98.69115 1
310 2014-10 98.53143 1
311 2014-11 98.31426 1
312 2014-12 98.06211 1
313 2015-01 97.80693 1
314 2015-02 97.56847 1
315 2015-03 97.35264 1
316 2015-04 97.17543 1
317 2015-05 97.02750 1
318 2015-06 96.90185 1
319 2015-07 96.80600 1
320 2015-08 96.74126 1
321 2015-09 96.71504 1
322 2015-10 96.71930 1
323 2015-11 96.73604 1
324 2015-12 96.77872 1
325 2016-01 96.87116 1
326 2016-02 97.04812 1
327 2016-03 97.31297 1
328 2016-04 97.64771 1
329 2016-05 98.02731 1
330 2016-06 98.43169 1
331 2016-07 98.82601 1
332 2016-08 99.16944 1
333 2016-09 99.44402 0
334 2016-10 99.63253 0
335 2016-11 99.76746 0
336 2016-12 99.89494 0
337 2017-01 100.04950 0
338 2017-02 100.22390 0
339 2017-03 100.40570 0
340 2017-04 100.59000 0
341 2017-05 100.78670 0
342 2017-06 101.00930 0
343 2017-07 101.28520 0
344 2017-08 101.61680 0
345 2017-09 101.98400 0
346 2017-10 102.35380 0
347 2017-11 102.70540 0
348 2017-12 103.04680 0
349 2018-01 103.37380 0
350 2018-02 103.65930 0
351 2018-03 103.88830 0"
)
df2<-read.table(text=z,header=T)
根据帮助页面，对于二项式情况，y变量应为矩阵nx2，其中一列具有成功次数（衰退），另一列具有失败次数（扩展）。此外，jstat论文认为＆＃34;身份残差的使用允许在Wang和Li（2011）中考虑的BARMA模型适合使用glarma包。＆＃34;那么，为了我的拟合目的（拟合二元AR（1），x作为外生预测因子），应该这样做：
y=df2[,3] #selecting binary variable
y1<-ifelse(y==1,1,0) #1 for recessions (success)
y2<-ifelse(y==0,1,0) #1 for expansions (failure)
yy<-cbind(y1,y2) #binding both
const<-rep(1,nrow(yy)) #creating a constant
x<- df2[,2] #continuous variable
myglarma <- glarma(yy, cbind(const,x), phiLags = 1, phiInit=0, type = "Bin",
method = "NR",
residuals = "Identity",
maxit = 1000, grad = 1e-6)
但是，我收到以下错误：glarma中的错误（yy，cbind（const，x），phiLags = 2，phiInit = 1，type =＆＃34; Bin＆＃34;，：Fisher Scoring无法收敛最初的估计。
我说得对吗？如何解决收敛问题？感谢所有提前。